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Assistive devices of human knee joint: A review
Journal Pre-proof Assistive devices of human knee joint: A review Li Zhang, Geng Liu, Bing Han, Zhe Wang, Han Li, Yan Jiao
PII: DOI: Reference:
S0921-8890(19)30320-3 https://doi.org/10.1016/j.robot.2019.103394 ROBOT 103394
To appear in:
Robotics and Autonomous Systems
Received date : 21 April 2019 Revised date : 3 October 2019 Accepted date : 2 December 2019 Please cite this article as: L. Zhang, G. Liu, B. Han et al., Assistive devices of human knee joint: A review, Robotics and Autonomous Systems (2019), doi: https://doi.org/10.1016/j.robot.2019.103394. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
© 2019 Published by Elsevier B.V.
Journal Pre-proof
Assistive Devices of Human Knee Joint: A Review Li Zhang, Geng Liu*, Bing Han, Zhe Wang, Han Li, Yan Jiao Shaanxi Engineering Laboratory for Transmissions and Controls ，Northwestern Polytechnical University，Xi'an 710072，P.R.China Corresponding author: Geng Liu, Email: [email protected]
pro of
Abstract: Knee dysfunction, such as knee osteoarthritis, meniscus injury, ligament injury, spinal cord injury and stroke, considerably impacts the normal living ability and mental health of these patients. Developing more effective knee assistive devices is in urgent need for effectively recovering their motion capabilities and improving their self-living activities. In this paper, we review and discuss the mechanical system design, sensing and control systems design, and performance evaluation of the main research advances in knee assistive devices. Firstly, in order to clearly illustrate and compare the mechanical system design, the mechanical system design is classified into four components to discuss: human attachment design, joint alignment design, actuation design and power transmission design. Then, the sensing and control systems design, which includes human biological signals based control systems,
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human-device interaction signals based control systems and device signals only based control systems, is compared and discussed. Furthermore, the performance evaluation methods and effectiveness of most of the knee assistive devices are reviewed. Finally, a discussion of the existing problems in the current studies and some recommendations for future research are presented.
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1. Introduction
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Keywords: Knee dysfunction; Knee assistive device; Wearable robot; Gait rehabilitation; Human-robot
The knee joint, as the main lower limb motor joint of human beings, is the most vulnerable and susceptible joint [1]. In the process of human motion, knee joint plays a major role in supporting body weight, assisting lower limb swing and absorbing strike shock [2]. The impairments of knee joint are the most common surgical injury which impact the normal living ability and mental health of patients [3]. The knee assistive device, as an effective physical conservative therapy, has continuously attracted scientific studies over the past years. Typical ones include brace which can limit knee movement after surgery, orthosis which can assist orthopedic of abnormal knee, exoskeleton which can assist knee motion
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based on human motion intention, etc.
In the last decade, many researchers reviewed the assistive devices from different aspects and levels. In the aspect of assistive strategies, Yan et al. [4] analyzed the lower-limb assistive strategies of poweredlocomotion augmentation exoskeletons and orthoses. In the aspect of mechanical and control systems, Huo et al. [5] reviewed the actuation systems and control strategies of lower limb wearable robots for movement assistance and rehabilitation. Mohammed et al. [6] discussed the actuated lower limb wearable robotics and, particularly, the knee joint active orthosis for movement assistance. Anam et al. [7] described the control systems of active exoskeleton based on different classification methods. In the aspect of performance, Federici et al. [8] examined the effectiveness of lower-limb powered exoskeleton
Journal Pre-proof in neuro-rehabilitation of paraplegic patients. Arazpour et al. [9] discussed the influence of orthosis on walking parameters in spinal cord injuries patients. Louie et al. [10] reviewed the influence of lowerlimb robotics exoskeletons on post-stroke rehabilitation of gait. Chang et al. [11] analyzed the powered lower-limb exoskeletons to restore gait for individuals with paraplegia. However, it is rare to systematically discuss the assistive devices from the aspect of single knee joint. To fill the gap of knowledge in the knee assistive devices and develop a more effective knee assistive device in the future study, this paper presents the review of the knee assistive devices for last decade. For developing an effective, portable and comfort knee assistive device, two main aspects usually need
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to be taken into account: physical human-robot interaction (pHRI) and cognitive human-robot interaction (cHRI). The target of pHRI design is to decrease the force and pressure which act upon the human body, whereas the cHRI design aims at increasing the human cognition and control for machine [12]. Based on the above, this paper reviews and discusses the knee assistive devices from three main components: mechanical system, the sensing and control systems, and devices performance evaluation of existing. For each aspect, we classify the knee assistive devices and provide some typical examples. The characteristics, the pros and cons of each category are provided and discussed.
The rest of this paper is organized as follows. In Section 2, the mechanical systems design of knee assistive devices is reviewed according to the human attachment design, joint alignment design, actuation
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and power transmission design. In Section 3, the sensing and control systems of knee assistive devices up to now are analyzed. In Section 4, the performance evaluation methods and effectiveness of knee assistive devices are discussed briefly. In Section 5, the limitations of the current studies and some
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recommendations for future research are presented.
2. The mechanical systems of knee assistive devices The knee assistive device is an electromechanical or a pure-mechanical structure worn by the user and matching the shape and functions of user’s knee [7]. In Fig. 1, some typical structures of knee assistive
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devices are shown. According to the working principle, the knee assistive devices can be divided into three groups: powered devices, quasi-passive devices and passive devices. For the powered devices, additional power needs to be provided from the actuators to the user’s knee at the appropriate time. For the quasi-passive devices and passive devices, no additional power is provided to the human-device system. These assistive devices mainly work by absorbing and releasing power when the knee does negative and positive work, respectively. The difference between the quasi-passive devices and passive devices is that the precise time control of energy absorbing and releasing is implemented by puremechanical structure for passive assistive devices and by electronic systems for quasi-passive assistive
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devices. According to the intended application, the knee assistive devices can be classified into three main categories: human performance augmentation, locomotion assistance and rehabilitation. The human performance augmenting devices mainly apply to healthy people. Its goal is to increase the user’s loading capability or decrease the user’s metabolism in different motion tasks [13]. The locomotion assistive devices and the rehabilitation devices mainly apply to patients with musculoskeletal or neurological disorders. Tables 1-3 give an overview of the powered, quasi-passive and passive devices of the existing exoskeletons for last decade, respectively. In each table, the mechanical systems are compared and tabulated with respect to the type of layout, actuator and power transmission, degree of freedom (DOF)
Journal Pre-proof of simulated biological knee joint, mass, range of motion (ROM) and application domain. In order to illustrate and compare the mechanical systems design of knee assistive devices better, we divided the mechanical systems into three components: human attachment design, joint alignment design, and actuation design and power transmission design. For each component, we classified the associated knee assistive devices and provide some typical examples. The characteristics, advantages and disadvantages
a
b
pro of
of each category are provided and discussed.
c
d
e
f
Fig. 1 Typical structures of knee assistive devices, a tethered exoskeleton for knee rehabilitation [14]; b passive knee exoskeleton for cycling assistance [15]; c Elevate Robotic Ski Xo (Roam Ski Inc., USA); d robotic knee exoskeleton for treatment of crouch gait [16]; e backdrivable
2.1 Human attachment design·
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knee exoskeleton with comfort consideration [17] and f Levitation (Sping-Loaded Technology Levitation Inc., Canada)
The human attachment component transmits the assistive torque from actuator to human knee and has a significant impact on the user’s wearing comfort. Its design is based on two main considerations: the
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configuration of attachment component and the layout of the mechanical frame [17]. The former aims at distributing the assistive torque to the human body and reducing the normal pressure, and the latter wants to reduce or eliminate the undesired tangential force.
According to the number of attachment points, the attachment configuration mainly includes 2attachment points (1 on the thigh and 1 on the shank) and 4-attachment points (2 on the thigh and 2 on
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the shank). For 4-attachment points, Witte et al. [14] set the attachment straps at the top of thigh, just above the knee, the shin below, and just above the ankle. Wang et al. [17] placed the distal thigh strap and proximal calf strap at the closest distance to knee while securing knee flexion clearance. The proximal thigh strap and distal calf strap were placed near the groin and above the ankle, respectively. For 2-attachment points, Kardan et al. [18] placed the thigh strap at 0.3 m above the knee and the shank strap at 0.25 m below the knee. However, Maeda et al. [19] set the attachment straps at 0.10 m above the knee and 0.18 m below the knee, respectively. Among the compared attachment configuration in Tables 1-3, more than 50% of them used 4-attachment points as configuration. About 35% of them were
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configured 2-accachment points and only 15% of them used other configurations. The study of Wang et al. [17] indicated that the 4-attachment points were preferred than 2-attachment points for minimizing the undesirable interaction force at the attachment location. For the 4-attachment points, the study of Witte et al. [14] pointed out that the 2 points on the same body section should be located as far from each other as possible to maximize their leverage about the knee and minimize the magnitude of force applied to the user.
Electric
Electric
Electric
Celebi et al. [22]
Khamar et al. [23]
Wang et al. [17]
Layout Actuator and Power transmission Sensing System
A'-3
Zhou et al. [28]
Shepherd et al. [29] Electric
A'-2
C-4
Electric
C-4
A'-4
A-2
A'-2
A-2
A'-2
Electric
Liao et al. [27]
Luo et al. [26]
25]
Electric
Electric
Ergin et al. [21]
Saccares et al. [24-
Electric
Method FSM; Force (sit to stand),
Control Method
(motor position and velocity)
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effector force)
re-
(knee angle and angle velocity)
reducer and elastic element)
insoles/3 IMUs (gait events detection)
belt, ball screw and springs)
along spring)
SEA (brushless DC motor, timing Rotary encoder (knee angle) and linear encoder (position
mechanisms
bar linkage and gear meshing potentiometer (knee angle and angular velocity) and pressure
Brushless DC motors, gear-rack, 5- Load cell (human-exoskeleton interaction force) and
Encoder (motor position and velocity) and goniometers
exoskeleton and shank)
Torque control
(each state)
FSM; Impedance-torque control
control
Integral sliding-mode (ISM)
Torque control
Torque control
control
Back-stepping sliding mode
—
Impedance control
control (sit/swing state)
(stand state) and impedance
1
2
1
6
4
2
2
3
2
DOF
4.1
2.3
—
3.75
3.2
—
1.4
—
3.5
Swing phase of walking and sit-
Application Domain
0-120
—
-5-90
0-120
—
—
sit-to-stand assistance
Patients (post-stroke hemiparesis)
patients
Walking/sit-stand-sit assistance of
muscle weakness patients
Walking assistance for elder or
persons or patients
sit-to-stand assistance for healthy
for patients (hemiplegic stroke)
Stance phase of walking assistance
and extension) assistance
Human movement (knee flexion
0-90 Knee flexion/extension assistance
0-90 Knee rehabilitation training
(hemiplegic)
0-120 stand-sit assistance for patients
(kg) (deg)
Mass ROM
pro of
Force-torque sensor (interaction forces and torques between
IMU sensor (gait cycle detection)
Encoder (knee angle) and current sensor (motor current)
SEA (DC servo motor, gear
and universal joint
reducer and elastic element), S2AP
SEA (brushless DC motor, gear
belt
Current motor and 2-stage timing
and 4-bar linkage
Linear actuator, curved segment
and Schmidt coupling
cable-series compression spring 3 optical encoders (deflection of two disks)
Brushed DC motor, gear reducer,
rings
Brushed DC motor, belt and 3-RRP Optical encoders (motor position) and force sensors (end-
4-bar linkage
Brushless motor, gear reducer and 3 FSRs (COP), torque sensor (output torque) and encoder damper (stand to sit), position
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Actuation
Kim et al. [20]
Study
Table 1 Comparison of active knee assistive devices
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Inc.)
Keeogo (B-Temia
Martin Inc.)
ONYX (Lockheed
(RIVEXO Inc.)
T-Xanadu One
[43]
Figueiredo et al.
Felix et al. [42]
41]
Electric
Electric
Electric
Electric
Electric
Electric
Han et al. [35-38]
Karavas et al. [39-
Electric
Electric
Electric
Santos et al. [34]
[33]
Mazumder et al.
32]
Kardan et al. [18,
Electric
Kamali et al. [31]
A'-4
A'-4
A'-4
A-4
A'-4
A
A-3
A-2
C-2
C-2
A-4
blocks) and pressure force sensor (GRF) 2 optical encoders (motor position and link deflection
Gear-rack, Ball screw and spring)
CompAct ARS (brushless motor,
Motor, gear reducer, etc.
Motor, gear reducer, etc.
Motor, gear reducer, etc.
reducer
—
Sensors detect speed, direction and angle of movement
—
torque), IMU/FSR/EMG sensors (gait event detection)
—
—
—
(motor torque), strain gauges (user-orthosis interaction Adaptive impedance control
Tele-impedance control
state)
1
1
1
1
1
1
1
1
1
1
1
DOF
5.4
64
6.0
2.02
2.1
—
2.53
2.0
—
—
—
Application Domain
—
—
—
3-98
0-120
—
—
0-95
—
—
or
patients
walking
Patients with unsteady or older
Soldiers, workers, etc.
Hiker
assistance
Patients (gait pathologies) walking
assistance for healthy or patients
Walking, sit-to-stand and squatting
assistance
Healthy
rehabilitation
Patients (Neurological disorders)
walking/sit-stand-sit assistance
Patients (stroke or paraplegia)
assistance
Healthy person or patients motion
extremity weakness patients
Sit-to-stand assistance for lower
0-120 Support body weight for worker
(kg) (deg)
Mass ROM
pro of
Potentiometer (angle and angular speed), hall effect sensors
potentiometer (pivot point position) and 6 EMG sensors
springs)
Brushless DC motor and gear
angle), encoder (motor position after reduction),
gear reducer, lever arm and 2
re-
3 potentiometer (knee angle and distances between brake FSM; Position control (each
Multimodal SEA (DC servo motor,
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opto-electronic incremental encoder (actuator output)
worm gear and torsion spring)
Torque and impedance control
Magneto-resistant incremental encoder (motor rotation) and
Rotary SEA (brushed DC motor,
force control (stand up/ sit down)
Position control (walking) and
(OFAC)
deflection) and 6 EMG sensors
Optical sensor (knee angle) and infra-red sensor (spring
Output feedback assistive control
Impedance control
Torque control
Control Method
pulley, ball-screw and 2 springs)
SEA (brushed DC motor, belt-
pulley, ball screw and 2 springs)
2 encoders (spring deflection and motor position)
rotation) and 4 force sensors (GRF)
pulley, ball screw and spring set)
Linear SEA (serve motor, belt-
Linear encoder (estimate the output force), encoder (ankle
Linear SEA (serve motor, belt-
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Motor, gas spring and 4-bar linkage Encoder (knee angle) and load cell (gas spring load)
Layout Actuator and Power transmission Sensing System
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Electric
Method
Actuation
Noh et al. [30]
Study
Table 1 continued
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Electric
Electric
Electric
Electric
Electric
Wehbi et al. [3]
Huang et al. [49]
Witte et al. [14]
Liu et al. [50]
Rifai et al. [51-52]
Inc.)
(Lockheed Martin
A'-5
A-2
C-4
A-4
A-4
A-4
A
Pneumatic A'-4
Electric
Shan et al. [48]
Soft exoskeleton
Electric
Jiang et al. [47]
[46]
Angular potentiometer (knee position) and 2 FSRs (estimate
the motion intention)
Encoder (knee angle), goniometer (hip angle and angular velocity) and 2 FSRs (gait phase detection)
Servo AC motor, double-tendon-
sheath transmission and pulley
Brushless DC motor, ball screw,
belt transmission and cable drive
Torque sensor (motor output torque), encoder (knee position) and 3 EMG sensors
Brushless DC motor, beading bars
and stiffness variation system
Tubular pneumatic actuators
screw and cable-pulley
Some micro-sensors
Encoder (joint angle and angular velocity)
strain gauges (cable tension)
pulley
Brushless DC motor, belt, ball
Encoder (knee angle), heel switches (foot contact) and
spring and 1 for knee angle) and EMG sensor
Servo motor, 2 bowden cables and
gear, torsion spring)
2 potentiometers (1 for input displacement of the torsion
—
Nested saturation based control
EMG based impedance control
Torque control
impedance control
feedback control and zero
Hybrid of direct EMG bio-
Active impedance control
Fuzzy control
EMG based position control
1
1
1
6
1
1
1
1
1
1
DOF
2.3
—
—
0.76
0.84
—
2.1
—
3.18
3.2
—
0-120
—
0-120
—
—
—
—
—
—
(kg) (deg)
Mass ROM
pro of
for predicting knee angle)
screw
re-
Encoder (knee angle) and 5 EMG sensors (neural network
Motor, belt transmission and ball
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compression sensors (exoskeleton output torque)
actuator (BTSA) (DC motor, bevel
state)
acceleration), torque sensor (patients torque) and 2 Torque control
Optical encoder (knee angle, angular velocity and angular
sensor (exoskeleton output torque)
brake
Back-drivable torsion spring
Control Method
FSR (foot ground contact), encoder (knee angle) and torque FSM; Torque control (each
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electro-rheological fluid based
A-4
chain sprocket
Motor, planetary gear system and
Electric
Brushless motor, gear reducer and
and Weinberg et al.
A'-4
Layout Actuator and Power Transmission Sensing System
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Electric
Method
Actuation
Nikitczuk et al. [45]
44]
Lerner et al. [16,
Study
Table 1 continued
Soldiers
rehabilitation
Lower limb assistance and
Motor function rehabilitation
assistance
Healthy or patients walking
Patients walking rehabilitation
patients with lower limb weakness
Walking swing assistance for
walking assistance
Patients (impaired motion ability)
—
swing phase of walking assistance
Stroke patients stance flexion and
children with crouch gait from CP
Knee extension assistance for
Application Domain
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pneumatic
[57]
solenoid valve
2 PAMs and high-speed on/off
Pneumatic B-4
Chandrapal et al.
2 PPAMs and 4-bar linkage
regulator and 4 (2 pairs) PAMs
compressor,
lP
Encoder (knee angle) and 6 EMG sensors
re-
2 pressure sensors (GRF) and IMUs (knee angle)
Position control
pressure control (low level)
Stiffness (high level) and binary
control
Proxy-based sliding mode
Stiffness control
Torque control
Control Method
1
3
1
1
1
DOF
—
0.16
4.5
0.8
9.0
—
—
0-90
0-74
—
(kg) (deg)
Mass ROM
pro of
knee angle) and 2 force sensors (exoskeleton output torque)
2 pressure sensors (PPAMs pressure), encoder (exoskeleton
Pressure sensor (ground contact events detection)
Many sensors detect the human motion intention
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Air
Soft-inflatable actuators
Pneumatic A'-4
Pneumatic B-2
Sridar et al. [55-56] Pneumatic C-2
Knaepen et al. [54]
Beyl et al. [53]
Maeda et al. [19]
Inc.)
Fan-shaped airbag
Layout Actuator and Power Transmission Sensing System
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Method
Actuation
Xo (Roam Robotics Pneumatic A'-2
Elevate Robotic Ski
Study
Table 1 continued
Disabled persons assistance
assistance
Patients (stroke) walking
assistance
Patients (SCI/MS) walking
assistance
Healthy person or patients walking
Skier
Application Domain
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Dollar et al. [64]
A'-4
Elliott et al. [61-63] A'-2
60]
A-2
A-2
Rogers et al. [59]
Shamaei et al. [2,
A'-2
Tung et al. [58]
Electric actuator and wrap
Transmission
Actuator and Power
Knee extension of stance phase (assistance)
2.
Knee flexion of stance phase (storing energy)
Knee extension of stance phase (assistance)
1.
2. carriage
ball screw and spring
Brushed DC motor, belt,
acceleration),
1
Finite state machine 1
—
re-
1
pro of
(knee angle) and foot switch (ground contact Position control
Encoder (motor position), potentiometer
events)
DOF
Finite state machine 1
Control Method
angular velocity) and Finite state machine 1
(vertical
encoder (exoskeletal knee angle)
gyroscope (hip
Accelerometer
and insole sensors (gait cycle detection)
Rotary potentiometer (knee velocity sign)
Foot switch (heel-ground contact detection)
IMU (the absolute angle of the thigh link)
Sensing System
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Clutch, planetary gear and
tendon and pulley
Stiffness switching module,
Air spring
2. Knee extension of terminal stance (assistance) elastic strips
1. Knee flexion of early stance (storing energy)
Weight acceptance phase assistance
Knee flexion of stance phase (storing energy)
1.
spring
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Knee flexion assistance
Layout Walking Principle
Study
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Table 2 Comparison of quasi-passive knee assistive devices
2.5
0.71
2.45
—
1.2
(kg)
0-97
0-130
—
—
—
(deg)
Mass ROM
walking,
stairs
(musculoskeletal
assistance
Healthy
assistance
Healthy
persons
person
running
running
disorders) walking assistance
Patients
Descent assistance
descent-ascent assistance
Patients
Application Domain
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C
A
A'-4
B-2
Jun et al. [71]
Wu et al. [72]
Pagani et al. [73-74]
Moyer et al. [75]
to the knee
Three-points bending; Apply abduction or adduction moment
to the knee
Three-points bending; Apply abduction or adduction moment
3. Free swing knee extension
deg) and swing knee flexion (30-55 deg)
2. Releasing energy in stance knee extension (exceeds 5
—
—
4-bar linkage and gas spring
1
1
1
1
1
1
3
1
2
DOF
—
—
0.73
0.14
0.9
0.99
—
—
0.7
(kg)
FSM: Finite state machine FSR: force sensitive resistor IMU: inertial measurement unit
DOF: degree of freedom of simulated biological knee joint
ROM: range of motion of knee assistive devices in sagittal plane
SEA: series elastic actuator
—
—
—
—
0-120
—
—
0-160
0-120
(deg)
Mass ROM
pro of
band (PEB)
forces and torques at knee joint
re-
Parallel coupled compliant plate (PCCP) and pennate elastic
Hybrid mechanism (rigid and flexible) provides assistive
1. Storing energy in stance knee flexion (5-30 deg)
springs
4. Knee extension exceeds 60 deg (releasing energy)
lP 4 Springs, 4 wire cables, 4 pulley disks and 4 smaller return
Liquid spring, cord and gear
Srew-jack, compression spring and universal joint
Teflon spring, eccentric pulley and compression spring
4-bar configuration and planar spiral spring
Actuator and Power Transmission
3. Knee flexion exceeds 60 deg (storing energy)
2. Knee extension (releasing energy)
1. Knee flexion (storing energy)
—
2. Knee extension (releasing energy)
1. Knee flexion (storing energy)
2. Knee extension exceeds 60 deg (releasing energy)
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1. Knee flexion exceeds 60 deg (storing energy)
Walking Principle
Types of layouts: A lateral-support layout; A' improved-lateral-support layout; B two-sides-support layout; C anterior/posterior-support layout; 2/3/4/5 numbers of attachment points
B-4
Ranaweera et al. [70]
Technology Inc.)
A'-4
A-4
A-4
B-4
Layout
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Levitation (Sping-Loaded
Saleem et al. [69]
Li et al. [68]
Yuan et al. [67]
66]
Chaichaowarat et al. [65-
Study
Table 3 Comparison of passive knee assistive devices
Knee osteoarthritis (KOA) patients
Knee osteoarthritis (KOA) patients
Paraplegics walking assistance
mobility of user
Protect knee from injury and increases
Healthy person squat lifting assistance
bearing workers, athletes, etc.
KOA or knee injured patients, load-
Overweight person walking assistance
assistance
Weight bearing walking and climbing
Healthy person cycling assistance
Application Domain
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Journal Pre-proof According to the location of support, the layout of mechanical frame can be divided into four categories: lateral-support layout, improved-lateral-support layout, two-side-support layout and anterior/ posterior-support layout, as shown in Fig. 2. Lateral-support layout means the mechanical frame on thelateral side of lower limb, such as the researches from Wehbi et al. [3], Felix et al. [76-77], Nikitczuk et al. [45] and Shan et al. [48]. The deficiency of this layout is that the undesired twisting force can be generated when the assistive torque transmits through soft attachment components, such as straps. Improved-lateral-support layout resolves the problem by extending the mechanical frame from lateral to anterior and/or posterior, such as the devices from Rifai et al. [51-52], Luo et al. [26], Celebi et al. [22],
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ONYX (Lockheed Martin Inc.), Keeogo (B-Temia Inc.), Levitation (Sping-Loaded Technology Levitation Inc.) and Elevate Robotic Ski Xo (Roam Ski Inc.). Two-side-support layout also resolves the problem by adding a mechanical frame on the middle side of lower limb. But compared with improvedlateral-support layout, this layout may produce interference between two legs according to the researches from Chandrapal et al. [57] and Ranaweera et al. [70]. Anterior/posterior-support layout means the mechanical frame on the anterior or posterior side of lower limb, such as the researchers from Kardan et al. [18, 32], Liao et al. [27], Sridar et al. [55-56]. Compared with others, the structure of this layout is relatively complicated. Among the compared mechanical frame layout in Tables 1-3, more than 38% layout used lateral-support layout. Improved-lateral-support layout was utilized by 40% and only about
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10% layout used two-side-support layout. For anterior and posterior support layout, the percentage was
a
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about 12%.
b
c
d
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Fig. 2 The mechanical frame layout of knee assistive devices, a lateral-support layout; b improved-lateral-support layout; c two-side-support layout and d anterior/posterior-support layout [17]
In order to enhance security and comfort, other issues related to attachment design should be considered. Firstly, the frame should be held firmly on the lower limb and can be adjusted to fit the user’s height and limb’s length. Secondly, the cushioning pads should be placed at the interface to avoid direct contact and guarantee wear comfort. Finally, the areas in which muscle bellies may protrude should be avoid, most notably around the hamstrings and gastrocnemius.
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2.2 The knee joint alignment design According to the human physiological property, the main motion of human knee is the flexion-extension in sagittal plane. Thus, one purely rotary DOF is always used as the design of device knee [43-44, 57, 72]. Reducing the weight and simplifying the complexity of the mechanical structure and control systems are the advantages of this design. But according to the researchers from Celebi et al [22] and Kim et al [20], the real knee joint moves with a polycentric motion in sagittal plane, i.e. the center of rotation changes during the rotation, as shown in Fig. 3a. The trajectory of the center of knee joint seems to be a J-shaped curve in sagittal plane [78]. Thus, one purely rotary DOF would cause misalignment between
Journal Pre-proof human and device knee joint. As the misalignment appears, the human-device system becomes mechanically over-constrained, causing an undesired tangential force at the attachment locations and excessive internal force at the human knee [17]. These forces would scratch the user’s skin and become
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a source of loose binding force.
Fig. 3 Schematic representation of knee polycentric motion and bionic knee mechanism, a knee polycentric motion in sagittal plane [22]; b 4-bar linkage [20]; c 5-bar linkage [27]; d 3-RRP mechanism [21]; e Schmidt coupling [22] and f soft exosuit [15-56]
There are many methods that have been applied to decrease the misalignment. Firstly, one-DOFmechanism always be utilized to improve the alignment. Tucker et al. [79-80], Kim et al. [20], Khamar
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et al. [23], and Chaichaowarat et al. [65-66] implemented 4-bar linkage to similar the polycentric motion of human knee, as shown in Fig. 3b. The rolling joint mechanism was utilized in the study of Wang et al. [17]. 5-bar linkage and gear meshing mechanisms were selected in the studies of Liao et al. [27], Zhou et al. [28] and Li et al. [68], as shown in Fig. 3c. Secondly, an under-actuated mechanism was employed in many studies. It provides free movement of knee rotation center without affecting the transmission of assistive torque. Celebi et al. [22] designed an AssistOn-knee which implemented an under-actuated Schmidt coupling as the underlying mechanism, as shown in Fig. 3e. This mechanism aims at self– aligning by accommodating the passive translation of the instantaneous center of rotation of the knee.
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Ergin et al. [21] selected 3-RRP mechanism as the underlying mechanism for implementation of selfaligning knee, as shown in Fig. 3d. This structure allowed translational movements of the knee on the sagittal plane along with the rotation. Lastly, the designs with soft structure also effectively minimize or eliminate misalignment. Sridar et al. [55-56] developed a soft-inflatable knee exosuit that can enhance the compliance between human and device, as shown in Fig. 3f. In order to improve the alignment in all three planes, the design of 6 DOFs was always be utilized. Saccares et al. [24-25] designed an iT-knee that possesses the features of 6 DOFs and self-aligning. The iT-knee used two articulated parallelograms mechanism and a universal joint to overcome the misalignment between the human knee and device knee. Witte et al. [14] developed a simple rotary joints
Journal Pre-proof for alignment in three planes. The flexion and extension of device were actively controlled. But the small displacements in other 5 DOFs were allowed through high compliance in uncontrolled directions. Among the compared DOF design of knee assistive devices in Table 1-3, more than 77% of them has not considered the alignment problems. One-DOF-mechanism and under-actuated mechanism were utilized by about 11% and 4% to solve the knee alignment problems, respectively. And only 8% of them used other methods to achieve knee alignment.
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2.3 The actuation and power transmission design The actuation and power transmission mainly determine the performances of knee assistive devices, such as the efficiency, output torque, and portability [5]. Thus, the design of these two parts is crucial significance for the assistive devices. Based on the method of actuation, knee assistive devices can be classified into three categories: active actuation, quasi-passive actuation, and passive actuation. Among the compared actuation methods in Tables 1-3, the active, quasi-passive and passive actuation were utilized by about 73%, 10% and 17%, respectively. For quasi-passive or passive actuation, a spring controlled by clutch are usually employed for actuation. And the gear drives, ball screws, cable drives or belt drives are usually employed for power transmission. Based on the type of actuators, active actuation
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can be divided into electric actuation, pneumatic actuation, and hydraulic actuation. In electric actuation, the power transmission can be implemented by gear drives, ball screws, cable drives, belt drives or hybrid drives. In pneumatic actuation, pneumatic cylinder and pneumatic artificial muscle (PAM) are usually employed for power transmission. Along with oil pump, the power transmission of hydraulic actuation can be implemented by hydraulic piston and cylinder [12]. Among the compared active actuation
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methods in Table 1, more than 83% of them used electric actuation and 17% of them used pneumatic actuation.
In addition, there are various types of power sources used in knee assistive devices, such as direct driving motor, tendon mechanism and electric material power source, as shown in Fig. 4. As the most widely used power source, the direct driving motor can directly translate the rotational moment of motor
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into the assistive moment of knee joint, or translate the rotational movement of motor into linear movement of electric putter and then into the rotational movement of knee joint through some mechanical transmission devices, as shown in Figs. 4a and 4b. Larger volume, higher weight, limited energy supply and poor wearability are the drawbacks of this power source. As a soft, light weight and comfortable method, the power source of tendon mechanism can utilize the external devices including spring, artificial muscle, cable and so on to simulate the function of human tendon. For the quasi-passive or passive device, spring or elastic band is always utilized, which can absorb power from knee joint when the knee dose negative work and release it to knee joint when the knee dose positive work, as shown in Fig. 4c. For the
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pneumatic and electric devices, artificial muscle and cable is always employed, respectively, which simulate the contraction of human flexor or extensor muscle-tendon unit and provide power for the knee movement, as shown in Figs. 4d and 4e. Besides, for better providing assistive moment, the direction of artificial muscle and cable imitate the direction of internal muscle-tendon unit. The electric material is a new developing power source in recent years, which can convert other forms of power into electric power by using new materials and then provide assistive power for the knee assistive devices. The common used electric material power sources include piezoelectric materials, triboelectric materials, solar energy materials, etc., as shown in Fig. 4f. Unlimited energy supply is the biggest advantage of this power source.
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c
d
e
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Fig. 4 The power sources of assistive devices, a direct driving motor-rotary [39]; b direct driving motor-linear [18]; c tendon mechanismspring [62]; d tendon mechanism-artificial muscle (Lockheed Martin Inc.); e tendon mechanism-cable [81] and f electric material power source [82]
The electric actuation is commonly used for the knee assistive devices because of the higher controllability and precision. Based on the type of drive, the electric actuation can be divided into rigid drives, soft drives and hybrid drives. As a traditional electric actuation method, rigid drive is common utilized to transmit the motor power into knee joint. For example, Lerner et al. [16, 44] implemented a
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chain-sprocket mechanism and Nikitcauk et al. [45] implemented planetary gear system and electrorheological fluid based brake to transmit the power from motor to knee joint. Based on the linkage mechanisms, Tucker et al. [79-80] and Shirota et al. [83] used worm gear and 4-bar linkage, Kim et al. [20] used gear reducer and 4-bar linkage, Khamar et al. [23] used curved segment and 4-bear linkage, and Liao et al. [27] used gear-rack, 5-bar linkage and gear meshing mechanisms to implement the power
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transmission. Although the rigid drive is widely used, there are still some inherent limitations, such as bulky, heavy weight, bad human-device physical interaction and wearing uncomforted. As a newly developing electric method, the soft drive possesses high back-drivability, lightweight and wearing comfort, and can solve the limitations of rigid drive. For example, Shan et al. [48] employed driving pulley, double-tendon-sheath and driven pulley and Witte et al. [14] applied 2 Bowden cables (located
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on the anterior and posterior side of the leg) to transmit the power. Bearing the tension but not bearing the stress is the limitation of these drive method. The hybrid drives can combine the advantages of the above methods. For example, Rifai et al. [51-52] and Wehbi et al. [3] used belt, ball screw and cablepulley and Ergin et al. [21] employed belt and self-adjusted 3-RRP rings to transmit the power. Because of the inherently compliance, light weight and high power-weight ratio, pneumatic actuation can provide a better human-robot interaction. This actuation commonly be used as a tethered device for rehabilitation training. And the control unit, air compressor and pump are always out of the human body. The method of providing air power source is an important issue in pneumatic actuation. Based on the
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actuation mode, pneumatic actuators can be classified into rotary actuators and linear actuators. For the former, Sridar et al. [55-56] developed a soft-inflatable exosuit with I cross-section to provide knee extension assistance in the swing phase of gait. Elevate Robotic Ski Xo designed by Roam Robotics Inc. utilized fan-shaped airbag to provide knee power for skiers. For the latter, Beyl et al. [53, 84] and Knaepen et al. [54] employed pleated PAMs and 4-bar linkages, Maeda et al. [19] employed an agonistantagonist PAMs system and a soft knee exoskeleton designed by Lockheed Martin Inc. employed tubular pneumatic actuators to transmit the motor power into knee joint. The hydraulic actuation is usually designed for load carrying devices because of the high power-weight ratio. But due to the complexity and huge volume of oil supply system, hydraulic actuation is very seldom used in knee assistive devices.
Journal Pre-proof In order to enhance the back-drivability of actuation and decrease the output mechanical impedance, the series elastic actuator (SEA) has been employed in many knee assistive devices. SEA is a bionics device in which an elastic element is intentionally introduced in series between actuator’s driver and output. The advantages of this configuration include lower reflected inertia, shock tolerance, and more accurate stable force control in unconstrained environments [5]. Based on the type of elastic element, SEA can be divided into linear SEA and rotary SEA. For the former, Kamali et al. [27, 85], Kardan et al. [18, 32] and Mazumder et al. [33] developed a SEA which consisted of serve motor, belt-pulley, ball screw and two springs, Saccares et al. [24-25] developed a SEA which equipped with brushless DC motor,
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Harmonic drive gearbox and flexible elements, and Han et al. [35-38] developed a multimodal SEA that combined the motor, spring, brake and clutch elements to achieve the power transmission. Compared with the linear SEA, the rotary SEA can transmit the motor moment into the knee rotary moment directly. For example, Santos et al. [34] designed a rotary SEA which included a DC motor, a worm gear and a torsion spring, Luo et al. [26] designed a SEA which equipped with servo motor, gear reducer and elastic element. Shepherd et al. [29] developed a SEA which included brushless DC motor, timing belt, ball screw and fiberglass beam spring. Huang et al. [49] designed a backdrivable torsion spring actuator which consisted of DC motor, bevel gear and torsion spring to transmit the motor moment into knee rotary moment directly.
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Because of the simple structure, little volume and light weight, the quasi-passive and passive actuation are very popular in recent years. For the quasi-passive actuation, Elliott et al. [61-63] employed a clutchspring knee assistive device that could store energy in early stance phase of running and release energy in terminal stance phase of running. Dollar et al. [64] used spring carriage and Rogers et al. [59] used air spring to store and release energy in stance phase. Shamaei et al. [2, 60] utilized a spring based stiffness
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switching module, tendon and pulley to provide weight acceptance phase assistance. For the passive actuation, Chaichaowarat et al. [65-66] adopted planar spiral spring and 4-bar linkage to store and release energy when the knee angle exceeded 60 deg. Yuan et al. [67] and Li et al. [68] used Teflon spring, eccentric pulley and compression spring to achieve weight bearing assistance. Ranaweera et al. [70] implemented spring, wire cable, pulley disk and smaller return spring, to store the biomechanical energy
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dissipated at the knee during decent phase and release the harnessed energy in the ascent phase in a squatting cycle. Wu et al. [72] implemented a 4-bar linkage and gas spring to store energy in knee flexion of stance phase and release energy in knee extension of stance phase and knee flexion of swing. Levitation designed by Sping-Loaded Technology Inc. utilized a liquid spring to store energy when the knee flexed and release it when the knee extended.
2.4 The interrelationship of the three components
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The designs of three components, especially the designs of joint alignment and actuation-power transmission, influence each other. Therefore, understanding the interrelationship of these three designs is an important issue in the mechanical design of knee assistive devices. For the joint design of one purely rotary DOF, electric actuation, pneumatic actuation, quasi-passive actuation and passive actuation are all common actuation methods. Among them, SEA as a new developing electric actuation is commonly implemented in recent years. For the joint design of 4-bar linkage, the driving part is connected to the driving bar of 4-bar linkage and the coupler bar is connected to the user’s shank, and then generates the knee joint motion. The common actuation methods for a 4bar linkage joint include electric actuation, such as the devices of Kim et al. [20], Khamar et al. [23] and
Journal Pre-proof Noh et al. [30], pneumatic actuation, such as the devices of Beyl et al. [53] and Knaepen et al. [54], and passive actuation, such as the devices of Chaichaowarat et al. [65-66] and Wu et al. [72]. Because of the structure of 4-bar linkage, the common mechanical frame layout of knee assistive device is later-support [23, 30, 72] or improved-lateral-support layout [20, 53-54]. For the joint design of under-actuated mechanism, electric actuation is the common actuation method. Cable is common employed to translate the moment of motor to the 3-RRP mechanism [21], Schmidt coupling [22], customized rotary joint [14] and so on. Based on the location of cable, the common mechanical frame layouts include later-support
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[21], improved-later-support [22] and anterior/posterior-support layout [14].
3. The Sensing and control systems of knee assistive devices
In the human-device system, the controllers included human brain and external controller are working parallel to each other. The external controller can significant improve the efficiency, precision, smooth and wear comfort of the assistive device based on the sensing and control systems. According to the hierarchy, the external control system can be divided into high-level and lower-level controllers. The high-level controller aims at inferring the human motion intention and determining the corresponding
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assistance strategies. And the lower-level controller aims at controlling the actuators to implement the desired position or torque based on the information from the high-level controller. Based on the control algorithm, the control system can be categorized as position control, torque control, impedance control and other control. The position control is employed to make sure the knee
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rotation of actuator in a desired position trajectory based on the difference between the desired angle (𝜃𝑑 ) and real angle (𝜃) [47, 57]. This control algorithm is always used for patients with poor motion control. The torque control is used to rotate the knee of actuator to achieve the desire torque trajectory based on the difference between the desired torque (𝑇𝑑 ) and real torque (𝑇) [14, 45, 29]. And it is always used for patients with voluntary locomotor capability. Both the position and torque control algorithm belong to trajectory tracking approach and single closed loop control. For the impedance control, the interaction
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between human and assistive device is put into the control loop and double closed loop controller always be used as the control structure [31, 41, 50, 79]. As shown in Fig. 5, the outer loop acted as position controller, in which the desired angle is tracked. The inner loop is torque control producing the desired torque for the actuator. The link of two loops is achieved by impedance controller. The impedance control law can be written as
𝑇𝑑 = 𝐾𝑆 (𝜃𝑑 − 𝜃) + 𝐾𝐷 (𝜃𝑑̇ − 𝜃̇ ) + 𝐾𝐼 (𝜃𝑑̈ − 𝜃̈ )
(1)
where 𝐾𝑆 , 𝐾𝐷 and 𝐾𝐼 are the virtual stiffness, virtual damping and virtual inertia, respectively. Compared with position control and torque control, impedance control can not only control the position
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and the torque but also control the relationship and interaction between human and knee assistive device. Therefore, it can improve the human-machine coordination and wearing comfort, and widely implemented in recent years. Besides, there are many new control algorithms. Kardan et al. [18, 70] proposed a newly output feedback assistive control (OFAC) algorithm which did not require any information about contact force, systems accelerations or their future paths. Rifai et al. [51-52] developed a nested saturation based control which can effectively avoid the actuator’s saturation, guarantee and enhance the wearer’s security. Beyl et al. [53, 84] reported a proxy-based sliding mode control (PSMC) that can combine the trajectory tracking and a safe response of perturbations.
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Fig. 5 Block diagram of typical double-closed-loop impedance control system
Based on the input signals, the control system can be classified into human biological signal based control methods, interaction signal based control methods, and assistive device signal only based control methods. Gopura et al. [12] pointed out the categorization based on input signals is more important, since
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the input signals are essential to identify the human motion. Therefore, in the following of this section, the control systems will be reviewed and discussed by this categorization
3.1
Human biological signals based control systems of knee assistive devices
The biological signals directly reflect the human motion intention and generate prior to the corresponding motion. Therefore, the biological signal based control system can fully recognize the human motion intention with any signal delay and lose compared to the others [5]. There are two noninvasive biological signals which are always used in the knee control systems: surface
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electromyogram (sEMG) and surface electroencephalogram (sEEG).
sEMG based control system is always recognized the motion intention by building the relationship between sEMG and desired knee angle/torque. Based on the modeling methods, the sEMG based control model can be divided into sEMG-driven musculoskeletal (MS) model and neural network (NN) model. For the former, Hill-based parametric model is common employed, which need to estimate the parameters
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based on the biomechanical property of user’s muscle and joint. For example, Karavas et al. [39] described a MS model that can use sEMG to estimate the knee angle and stiffness. An impedance controller was then utilized to track the desired knee angle and stiffness to achieve the control of actuator. Huang et al. [49] reported a sEMG based biofeedback control which can achieve the nonlinear mapping between the sEMG and the muscle force. And the muscle force was then used to track the user defined
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trajectory to assist human walking rehabilitation. Pena et al. [86] developed an optimal sEMG-driven adaptive impedance control method. A simplified and optimized MS model was used to estimate the knee torque and stiffness. And an adaptive impedance control was employed to track the desired trajectory and stiffness of knee. Compared to the former, which establish an mathematic model, the latter can improve the nonlinearity of model, smoothness and efficiency of control system. For example, Jiang et al. [47] presented a four-layer feed-forward NN model to establish the mapping between sEMG and knee angle. And position control method was utilized to control the actuator to achieve estimate knee angle. Pena et al. [86] utilized a multilayer perceptron NN to map the relationship between EMG single and knee
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torque/stiffness. Mefoued et al. [87] designed a radial basis function neural network (RBFNN) that can map the nonlinearities between EMG signal and desired knee angle. A second order sliding mode control was developed to control the assistive device by using the desired knee angle. Many related researches show that the sEMG-based control system can estimate the human intention efficiently. But there are still some shortcomings. For example, the sEMG-based knee assistive device does not apply to the patients with physical cripple, paralysis, hemiplegia, etc.; sweating affects sEMG sensor measurement after a long-time exercise; the sEMG sensor are easy to off and shift after a strenuous exercise; the sEMG signal present greatly differences from subject to subject and even for one person from day to day. Because the EEG signal are generated directly by the human brain and not transmitted by the human
Journal Pre-proof nervous system, the EEG-based control systems can recognize the human intention earlier than sEMGbased systems, and EEG-based knee assistive device can apply to the patients with only brain healthy. For example, Bai et al. [88] proposed an EEG based volitional control. Seven channels (C5, C3, C1, Cz, C2, C4 and C6) which placed on the central motor areas were selected to acquire the EEG signals. Machine learning algorithms were designed to decode human volition and subsequently to command the knee-locker switch of assistive device. Villa-Parra et al. [89] developed an EEG based control method to estimate the motion intention. The EEG signals related with planning motions were implemented through both event-related synchronization/desynchronization (ERS/ERD) and slow cortical potential (SCP).
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Mercado et al. [90] reported a hybrid control method based on EEG and EMG. The intention related to the velocity change was classified by the EEG, meanwhile the amplitude of each intention was estimated by the sEMG. The support vector machine (SVM) was proposed to classify the feature vector of EEG into two speed classes, and the neural network of maximum sensibility (NNMS) was used to estimate the knee joint torque. Compared to sEMG signals, the application of EEG signals possesses more limitations. For example, the EEG signals are more difficult to capture and complex to estimate human motion intention; EEG signals are rarely used for continuous motion estimation and often used for motion classification; the wearable EEG measurement equipment is more complex and uncomfortable. Although the biological signals (sEMG and EEG) based control systems have been widely used in the
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knee assistive devices, they are still under laboratory stage and have some limitations in practical application. Firstly, the signal-to-noise ratio and accuracy of these biological signals are relatively lower because the signals are acquired from the surface of human body. Secondly, the biological signals are unstable and easily influenced by the circumstance and electrode placement. Thirdly, the wearing of these expensive.
3.2
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knee assistive device is more complex and the measurement equipment of sEMG and EEG is more
Human-device interaction signals based control systems of knee assistive
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The human-device interaction signals are critical to estimate the knee motion intention, although it occurs later than the biological signals. Some of studies directly measured the interaction force at the human-device interface to estimate the motion intention. Others utilized the ground reaction force (GRF) measured by force sensitive resistor (FSR) to estimate the motion intention. There are a lot of benefits to add the interaction force at the human-device interface to the control system. For example, the interfacial force signal based control system can effectively prevent excessive interaction force and its harm to human body when a mistake occurs in the control system; the interaction
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force in the interface of knee assistive device is relatively small, which can improve the wearing comfort of assistive device. For example, Saccares et al. [24] directly measured the interaction forces and torques as the feedback signals. A torque control method was utilized to track the desired trajectory. Shan et al. [48] utilized two FSRs, respectively located at the front and back of shank, to directly measure the interaction force. A fuzzy control algorithm was developed to enhance the assistance performance. Liao et al. [27] employed a load cell to measure the interaction force and a linear potentiometer to calculate the knee angle and angular velocity. A double closed loop control method (inner torque loop and outer position loop) was used to achieve tracking tasks. And a PSMC method was utilized to establish the relationship between two loops. Liu et al. [91] utilized the interaction force as the feedback signal of
Journal Pre-proof control strategy. Three IMUs, which placed on the front of shank part, thigh part of assistive device and the back of shoe, were used to predict the locomotion intention based on SVM. For GRF signals based control methods, finite state machine (FSM) based control algorithm is common utilized, which can divide the gait cycle into some discrete phases. Implantable foot sensors, which are inserted into shoes, are commonly implemented to acquire GRF signals and detect human gait events. For each phases, a responsive low-level control strategy is utilized to control knee assistive device. Portability and friendliness are the advantages of these control systems. For example, Maeda et al. [19] applied a pressure sensor attached to the insole to detect the gait events. For each event, a stiffness control
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algorithm was implemented to control the knee stiffness of actuator. Lerner et al. [16] utilized a FSR and encoder to detect the states of gait cycle. For each state, a PID control scheme was used to achieve the desired knee torque output. Wehbi et al. [3] employed insoles with two FSR sensors to detect the swing phase, and encoder and goniometer to detect the swing sub-phase of a gait cycle. A nonlinear impedance control with gait based desired admittance model was used to control the interaction between lower limb and assistive device. Shamaei et al. [2, 60] implemented a FSM to engage the assistance spring of quasipassive device. Insole sensors and potentiometers were used to detect gait states. Although the GRF signals based control system is widely used, there are still some limitations. For example, the gait cycle is divided into discrete phase and smooth control can’t be achieved; the discrete phase is pre-defined and
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unidentifiable condition will happen when the undefined phase appear.
Hybrid signals based control systems combine the advantages of human-device interface signal based control systems and GRF based control systems. For example, Zhou et al. [28] developed a FSM based impedance control algorithm to assist walking motion. A wearable plantar pressure measurement system with four sensors in each shoe was used to detect gait states. And a rotary sensor was used to measure
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the real knee angle and angular velocity. For each state, a double closed loop impedance control was implemented.
Many convincing evidences show that the estimated accuracy, stability, robustness and safety of control system are higher than others to use human-device interaction signal to estimate human motion intention, but there are still some inherent limitations. For example, the time delay of human-device
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interaction signal based control system is relative longer than other methods because the human-device interaction signal occurs later than the movement happens.
3.3
Device signals only based control systems of knee assistive devices
Compared to the others, the assistive device signal only based control methods estimate or follow the user’s motion intention only used the signals acquired from assistive devices itself. Encoder, gyroscope, accelerometer and IMU are common employed to measure the joint angle, angular velocity and angular
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accelerometer of assistive device. A dynamical model of assistive device and human body was usually utilized to achieve the control algorithm [5]. The device signals only based control systems are the primary control algorithm. And it is often used in the quasi-passive devices or the preliminary experimental stage of power devices currently. Because of no prediction of human motion intention, simple structure of sensing and control systems are the advantages of these method. For example, Elliott et al. [62] utilized encoder, gyroscope and accelerometer and Tung et al. [58] utilized an IMU to measure the angle of knee joint and to detect the gait states. A FSM method was then used to control the storing or releasing energy of spring. Wang et al. [17] employed an IMU to measure the ankle angular velocity and then detect the gait cycle. A high level
Journal Pre-proof torque control and low level current control were applied to achieve the assistive control strategy. Shepherd et al. [29] employed two encoders to provide joint angle feedback. The torque feedback can be calculated by the feedback signals from the two encoders. And a PID based toque control was used to control the output torque of actuator. Nikitczuk et al. [45] and Weinberg et al. [46] developed a non-linear adaptive PID torque control method to control the actuator of assistive device. An optical encoder and a torque sensor were used to provide joint angle and torque feedback, respectively. Santos et al. [34] used the output torque and joint angle as the feedback signals in the torque and impedance control cycle. An the actuator output torque and joint angle, respectively.
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opto-electronic incremental encoder and a magnate-resistant incremental encoder was utilized to measure There are many limitations of device signal only based control methods. For example, it is difficult to establish an accurate model of human and/or assistive device to achieve accurate control; the security, human-machine coordination and comfort of these control method are lower than others. Therefore, the application of devices signal only based control methods is relative rare currently.
4. The Performance evaluation of knee assistive devices
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The performance of knee assistive devices determines the feasibility and usability of such devices. And the design aim of a knee assistive device determines its evaluation methods and performance. According to the design aim, the knee assistive devices can be divided into providing assistive power to human knee joint in the sagittal plane and providing knee orthosis in the coronal plane. For the former,
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healthy person always be selected as the experimental subjects to evaluate the performance because of the humanitarian considerations or experimental condition restrictions. The comparison of the subjects with and without the knee assistive devices always be considered as an evaluation method. For the latter, the patients with medial knee osteoarthritis always be selected as the experimental subjects because these devices don’t work for healthy person. The comparison of the patients pre and post wearing the devices always be considered as an evaluation method. According to the measuring means, the evaluation
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methods can be divided into three main groups: gait biomechanics, muscle activity analysis and metabolic cost analysis. Although the performance of mechanical or control algorithm designs are also important, they are not taken into account in this section. For the gait biomechanical analysis, a camera motion capture system with force platforms embedded in the walkway or a treadmill are common used to collect kinematic and kinetic data, as shown in Fig. 6a and 6b. And the joint angle, torque and power can be acquired by the inverse kinematics and dynamics. Gait biomechanical analysis is an elementary evaluation method and common used to verify the performance of all kind of knee assistive devices. For the knee assistive devices which provide assistive
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power in the sagittal plane, the power reduction of human knee joint is common set as the main evaluation index. For example, the knee exoskeleton of Kamali et al. [85] can reduce the average knee power by about 30% in sit-to-stand movement. Besides, no considerable difference of other joints biomechanics between subjects with and without device is an auxiliary evaluation index. For example, Shamaei et al. [2] presented the proposed knee assistive device does not have a considerable effect on the angle and moment of ankle and hip joints in the sagittal plane. Although the above two evaluation methods are available, it can only be used to evaluate the short-term effect of knee assistive devices. To obtain more comprehensive evaluation, a long-term biomechanical evaluation should be implemented. For example, Shirota et al. [83] assessed the short-term and long-term performances of the proposed knee exoskeleton.
Journal Pre-proof The result showed that the knee exoskeleton reduced the peak knee flexion angles and peak hip extension angles after a short period of acclimation, but comparatively smaller changes occurred after 1.5 h prolonged walking. For the knee assistive devices which provide knee orthosis in the coronal plane, the reduction of knee adduction moment is common set as the mainly evaluation index. For example, the devices of Pagani et al. [73] can reduce the peak knee adduction moment by about 5-33% (4 -8 deg orthosis) for walking and 11% (8 deg orthosis) for running; the devices of Toriyama et al. [92] can reduce the peak knee adduction moment by about 11.1% for walking. For the muscle activity analysis, sEMG sensors attached to the muscle belly are used to obtain the
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sEMG data, as shown in Fig. 6a and 6b. The root mean square (RMS) of sEMG and the amplitude of normalized sEMG are the common analysis methods. Muscle activity analysis is a new developing evaluation method and can reveal the assistive rationale of devices from the muscular aspect. For the knee assistive devices which provide assistive power in the sagittal plane, reducing the effort of human knee joint is the main target. Because the biceps femoris (BF) provides extension of knee joint mainly, the reduction of RF activity is common set as the main evaluation index. For example, the knee assistive devices of Chaichaowarat et al. [66] and Shan et al. [48] can significantly reduce the RMS of sEMG and amplitude of processed sEMG, respectively. The knee assistive devices of Tung et al. [58], Ranaweera et al. [70] and Rogers et al. [59] can achieve a 30-40% reduction in peak RMS of RF, a 20% reduction in
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average sEMG amplitude of RF, and a 15% reduction in average sEMG amplitude of RF, respectively. Besides, the reductions of other muscles, such as vastus lateralis (VL), vastus medialis (VM) and biceps femoris (BF) are auxiliary evaluation indexes. For example, Sridar et al. [55] presented the proposed knee exosuit can reduce the amplitude of RF, VL and VM by about 32%, 57% and 30%, respectively. Wehbi et al. [3] reported the proposed knee assistive device can reduce the RMS of sEMG by about 44%
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for BF and 40% for VM. For the knee assistive device which provide knee orthosis in the coronal plane, the application of muscle activity analysis is relative rare. Reducing the activity ratio of knee medial muscles to knee lateral muscles is set as the main evaluation index of these assistive devices. For example, the knee assistive device of Pagani et al. [74] can reduce the ratio of medial muscles to lateral muscles
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and VL to VM by about 12.3% and 28.4%, respectively.
Fig. 6 Performance evaluation methods, a frontal view of the optical motion capture system and sEMG acquisition system; b back view of the optical motion capture system and sEMG acquisition system and c cardiopulmonary exercise test system [28]
For the metabolic cost analysis, there are varieties of test methods including cardiopulmonary exercise test, oxygen saturation test, heart rate test, blood pressure test, etc. As a comprehensive evaluation method, the metabolic cost analysis is common used to verify the performance of the assistive
Journal Pre-proof devices which provide assistive power to human knee joint in the sagittal plane. For cardiopulmonary exercise test, the metabolic cost can be acquired by measuring the rate of oxygen consumption and carbon dioxide production through a face mask, as shown in Fig. 6c. And then the total metabolic power can be calculated from the linear expressions 𝑃 = 𝐾𝑂2 𝑉𝑂2 + 𝐾𝐶𝑂2 𝑉𝐶𝑂2
(2)
where 𝐾𝑂2 and 𝐾𝐶𝑂2 are constants, and 𝑉𝑂2 and 𝑉𝐶𝑂2 represent average velocities of oxygen inhalation and carbon dioxide exhalation. The reduction of 𝑃 is common set as the evaluation index. For example, Zhou et al. [28] employed cardiopulmonary exercise test system to evaluate the
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performance of the proposed knee exoskeleton. The results showed that an average 6.21% reduction of metabolic cost when the subjects wearing exoskeleton compared with wearing same weight load. The oxygen saturation test, heart rate test and blood pressure test are all belong to indirect characterization methods of metabolic cost. The oxygen saturation can be acquired by measuring the absorption ratio of visible red and infrared light during the pulse through a finger pulse oximeter and then translating the ratio into the oxygen saturation based on the relationship between them. The heart rate and blood pressure can be acquired by using some portable test devices, such as the devices of Bieber et al. [93], Gillinov et al. [94] and Simard et al. [95]. Besides, some wearable multifunctional devices, such as the devices of Fu et al. [96], Benjamin et al. [97] and Ma et al. [98], can be employed to acquire the three physiological
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indexes at the same time. Reductions of oxygen saturation, heart rate and blood pressure between movements with and without knee assistive devices are available evaluation indexes to verify the effects of the devices. At present, the application of these physiological methods in the performance evaluation
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of knee assistive devices is still rare.
5. Discussion and Conclusions
Knee assistive devices can improve the motor ability and reduce the disease of patients efficiently. A number of researches related with the knee assistive devices have been done during the last decades. And
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many different kinds of knee assistive devices have been developed. However, these developments still have not met the requirement of the knee assistive devices on daily application. And it is hard to find a knee assistive device that can be satisfied the need of all knee injury patients completely. The existed knee assistive devices are still in the laboratory application stage and few of them are truly marketized. For the actuation and power transmission design, traditional electric actuator is still the choice of most knee assistive devices because of the higher accuracy, robustness and efficiency in comparison to pneumatic or hydraulic actuators. As a bionics actuator, SEA is a new trend in applying to knee assistive devices, which can improve the shock tolerance and system stability. But some inherent limitations of
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traditional electric actuator still not be solved, which significantly hinder the development of these devices. The limitations include high weight, bulky, stiffness and bad energy consumption. There are two important trends in the development of the actuator. Firstly, soft actuators including cable and PAM were developed in recent years. Relatively high human-device interaction and wearing comfort are the advantages of these actuator. Because the power source was commonly out of the user’s body, the weight of user’s wearing is relatively low. Therefore, soft actuator was always applied for rehabilitation training currently. Secondly, passive or quasi-passive actuators may represent a good choice for knee assistive devices. Because of no external power source and no/little control system, light weight and volume are its biggest attractive points. However, relatively low human-device coordination is usual challenges for
Journal Pre-proof its application. For the human attachment design, more and more researchers consider its importance for wearing comfort. Based on our review, the improved-lateral-support layout with 4-attachment points is a good choice for the knee assistive device because of its relatively low undesired twisting force and positive force in the human-device interface. For the knee joint alignment design, bionic knee design, such as 4-bar linage and 3-RRP mechanism, has been an important trend which can decrease the misalignment and increase wearing comfort. It is difficult to simulate the real movement of human knee joint by current devices. Therefore, developing new bionic knee joint with light weight, simple structure, self-adaption and adjustment is still a critical challenge in the future.
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For the sensing and control systems design, human biological signals, such as sEMG and sEEG, based control systems will be the future trend because the signal can be detected prior to the corresponding motion. Establishing the relationship between biological signal and joint motion is still the main challenge to improve the accuracy of human motion intention recognition. As a new developing method, the neural network based modeling method will be widely utilized in the future because of relatively high accuracy and low model complexity. Multiple biological coupling analysis is an another new trend. Through coupling the advantages of multiple biological signals, such as EMG, EEG, EOG, etc., the accuracy of human motion intention recognition can be significantly improved. In addition, another critical challenge is to improve the accuracy of signal collecting system and simple the structure of signal
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acquisition process.
For the performance evaluation, many researches pointed out the knee assistive devices they proposed can significantly reduce the power of human knee joint, the effort of muscle and human metabolic cost. But the long-term performance and some integrated performance, such as wearing comfort and safety are not widely evaluated. Healthy person based performance evaluation is the limitations of current
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evaluation process. More patients participating into the evaluation experiment will improve the reliability of evaluation results. In addition, developing advanced and efficiency evaluation methods is still an important research direction.
In the future, developing more efficient, comfortable and portability knee assistive devices will still be an urgent need and a crucial challenge. For recovering the motion capabilities of patients, the assistive
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mechanism of knee assistive devices needs to be optimized. The accuracies of motion intention recognition, control algorithms and human-machine interaction need to be further improved. And a better assistive performance and better practical application need to be further provided.
6. Discussion and Conclusions The research is supported by the Fundamental Research Funds for the Central Universities (Grant No.
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31020190503004) and the 111 Project (Grant No. B13044).
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People And Smart City Innovation 111-116 (2018)
Journal Pre-proof Li Zhang is a Ph.D. candidate of Mechanical Engineering School at Northwestern Polytechnical University in Xi’an, China. He received his M.S. from Southwest Jiaotong University in 2017. His research interests include rehabilitation robots, human-robot interaction, biomechanics and biotribology. Geng Liu is a Professor and supervisor of Ph.D. students and Director of Shaanxi Engineering Laboratory for Transmissions and Controls at Northwestern Polytechnical University (NWPU) in Xi’an, China. He received his M.S. from NWPU and Ph.D. from Xi’an Jiaotong University, respectively in 1987 and 1994. His research interests include mechanical transmissions, electro-mechanical transmissions,
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low noise design of mechanical systems, human-machine interaction robot, tribology, contact mechanics and CAE.
Bing Han is a lecturer of Mechanical Engineering School at Northwestern Polytechnical University (NWPU) in Xi’an, China. He received his M.S. and Ph.D. from NWPU, respectively in 2007 and 2013. His research interests include human-machine coupling design, transmission system configuration design, and mechanical system collaborative design.
Zhe Wang is a M.S. candidate of Mechanical Engineering School at Northwestern Polytechnical University in Xi’an, China. His research interests include the human kinematics and kinetics simulation, wearable exoskeletons and robot control system.
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Han Li is a M.S. candidate of Mechanical Engineering School at Northwestern Polytechnical University in Xi’an, China. His research interests include the mechanical design and wearable exoskeletons design. Yan Jiao is a M.S. candidate of Mechanical Engineering School at Northwestern Polytechnical
Geng Liu
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Li Zhang
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University in Xi’an, China. Her research interests include the knee kinematics and kinetics simulation.
Bing Han
Zhe Wang
Journal Pre-proof Han Li
Yan Jiao
Declaration of interests ▉The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
PII: DOI: Reference:
S0921-8890(19)30320-3 https://doi.org/10.1016/j.robot.2019.103394 ROBOT 103394
To appear in:
Robotics and Autonomous Systems
Received date : 21 April 2019 Revised date : 3 October 2019 Accepted date : 2 December 2019 Please cite this article as: L. Zhang, G. Liu, B. Han et al., Assistive devices of human knee joint: A review, Robotics and Autonomous Systems (2019), doi: https://doi.org/10.1016/j.robot.2019.103394. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
© 2019 Published by Elsevier B.V.
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Assistive Devices of Human Knee Joint: A Review Li Zhang, Geng Liu*, Bing Han, Zhe Wang, Han Li, Yan Jiao Shaanxi Engineering Laboratory for Transmissions and Controls ，Northwestern Polytechnical University，Xi'an 710072，P.R.China Corresponding author: Geng Liu, Email: [email protected]
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Abstract: Knee dysfunction, such as knee osteoarthritis, meniscus injury, ligament injury, spinal cord injury and stroke, considerably impacts the normal living ability and mental health of these patients. Developing more effective knee assistive devices is in urgent need for effectively recovering their motion capabilities and improving their self-living activities. In this paper, we review and discuss the mechanical system design, sensing and control systems design, and performance evaluation of the main research advances in knee assistive devices. Firstly, in order to clearly illustrate and compare the mechanical system design, the mechanical system design is classified into four components to discuss: human attachment design, joint alignment design, actuation design and power transmission design. Then, the sensing and control systems design, which includes human biological signals based control systems,
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human-device interaction signals based control systems and device signals only based control systems, is compared and discussed. Furthermore, the performance evaluation methods and effectiveness of most of the knee assistive devices are reviewed. Finally, a discussion of the existing problems in the current studies and some recommendations for future research are presented.
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1. Introduction
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Keywords: Knee dysfunction; Knee assistive device; Wearable robot; Gait rehabilitation; Human-robot
The knee joint, as the main lower limb motor joint of human beings, is the most vulnerable and susceptible joint [1]. In the process of human motion, knee joint plays a major role in supporting body weight, assisting lower limb swing and absorbing strike shock [2]. The impairments of knee joint are the most common surgical injury which impact the normal living ability and mental health of patients [3]. The knee assistive device, as an effective physical conservative therapy, has continuously attracted scientific studies over the past years. Typical ones include brace which can limit knee movement after surgery, orthosis which can assist orthopedic of abnormal knee, exoskeleton which can assist knee motion
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based on human motion intention, etc.
In the last decade, many researchers reviewed the assistive devices from different aspects and levels. In the aspect of assistive strategies, Yan et al. [4] analyzed the lower-limb assistive strategies of poweredlocomotion augmentation exoskeletons and orthoses. In the aspect of mechanical and control systems, Huo et al. [5] reviewed the actuation systems and control strategies of lower limb wearable robots for movement assistance and rehabilitation. Mohammed et al. [6] discussed the actuated lower limb wearable robotics and, particularly, the knee joint active orthosis for movement assistance. Anam et al. [7] described the control systems of active exoskeleton based on different classification methods. In the aspect of performance, Federici et al. [8] examined the effectiveness of lower-limb powered exoskeleton
Journal Pre-proof in neuro-rehabilitation of paraplegic patients. Arazpour et al. [9] discussed the influence of orthosis on walking parameters in spinal cord injuries patients. Louie et al. [10] reviewed the influence of lowerlimb robotics exoskeletons on post-stroke rehabilitation of gait. Chang et al. [11] analyzed the powered lower-limb exoskeletons to restore gait for individuals with paraplegia. However, it is rare to systematically discuss the assistive devices from the aspect of single knee joint. To fill the gap of knowledge in the knee assistive devices and develop a more effective knee assistive device in the future study, this paper presents the review of the knee assistive devices for last decade. For developing an effective, portable and comfort knee assistive device, two main aspects usually need
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to be taken into account: physical human-robot interaction (pHRI) and cognitive human-robot interaction (cHRI). The target of pHRI design is to decrease the force and pressure which act upon the human body, whereas the cHRI design aims at increasing the human cognition and control for machine [12]. Based on the above, this paper reviews and discusses the knee assistive devices from three main components: mechanical system, the sensing and control systems, and devices performance evaluation of existing. For each aspect, we classify the knee assistive devices and provide some typical examples. The characteristics, the pros and cons of each category are provided and discussed.
The rest of this paper is organized as follows. In Section 2, the mechanical systems design of knee assistive devices is reviewed according to the human attachment design, joint alignment design, actuation
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and power transmission design. In Section 3, the sensing and control systems of knee assistive devices up to now are analyzed. In Section 4, the performance evaluation methods and effectiveness of knee assistive devices are discussed briefly. In Section 5, the limitations of the current studies and some
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recommendations for future research are presented.
2. The mechanical systems of knee assistive devices The knee assistive device is an electromechanical or a pure-mechanical structure worn by the user and matching the shape and functions of user’s knee [7]. In Fig. 1, some typical structures of knee assistive
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devices are shown. According to the working principle, the knee assistive devices can be divided into three groups: powered devices, quasi-passive devices and passive devices. For the powered devices, additional power needs to be provided from the actuators to the user’s knee at the appropriate time. For the quasi-passive devices and passive devices, no additional power is provided to the human-device system. These assistive devices mainly work by absorbing and releasing power when the knee does negative and positive work, respectively. The difference between the quasi-passive devices and passive devices is that the precise time control of energy absorbing and releasing is implemented by puremechanical structure for passive assistive devices and by electronic systems for quasi-passive assistive
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devices. According to the intended application, the knee assistive devices can be classified into three main categories: human performance augmentation, locomotion assistance and rehabilitation. The human performance augmenting devices mainly apply to healthy people. Its goal is to increase the user’s loading capability or decrease the user’s metabolism in different motion tasks [13]. The locomotion assistive devices and the rehabilitation devices mainly apply to patients with musculoskeletal or neurological disorders. Tables 1-3 give an overview of the powered, quasi-passive and passive devices of the existing exoskeletons for last decade, respectively. In each table, the mechanical systems are compared and tabulated with respect to the type of layout, actuator and power transmission, degree of freedom (DOF)
Journal Pre-proof of simulated biological knee joint, mass, range of motion (ROM) and application domain. In order to illustrate and compare the mechanical systems design of knee assistive devices better, we divided the mechanical systems into three components: human attachment design, joint alignment design, and actuation design and power transmission design. For each component, we classified the associated knee assistive devices and provide some typical examples. The characteristics, advantages and disadvantages
a
b
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of each category are provided and discussed.
c
d
e
f
Fig. 1 Typical structures of knee assistive devices, a tethered exoskeleton for knee rehabilitation [14]; b passive knee exoskeleton for cycling assistance [15]; c Elevate Robotic Ski Xo (Roam Ski Inc., USA); d robotic knee exoskeleton for treatment of crouch gait [16]; e backdrivable
2.1 Human attachment design·
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knee exoskeleton with comfort consideration [17] and f Levitation (Sping-Loaded Technology Levitation Inc., Canada)
The human attachment component transmits the assistive torque from actuator to human knee and has a significant impact on the user’s wearing comfort. Its design is based on two main considerations: the
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configuration of attachment component and the layout of the mechanical frame [17]. The former aims at distributing the assistive torque to the human body and reducing the normal pressure, and the latter wants to reduce or eliminate the undesired tangential force.
According to the number of attachment points, the attachment configuration mainly includes 2attachment points (1 on the thigh and 1 on the shank) and 4-attachment points (2 on the thigh and 2 on
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the shank). For 4-attachment points, Witte et al. [14] set the attachment straps at the top of thigh, just above the knee, the shin below, and just above the ankle. Wang et al. [17] placed the distal thigh strap and proximal calf strap at the closest distance to knee while securing knee flexion clearance. The proximal thigh strap and distal calf strap were placed near the groin and above the ankle, respectively. For 2-attachment points, Kardan et al. [18] placed the thigh strap at 0.3 m above the knee and the shank strap at 0.25 m below the knee. However, Maeda et al. [19] set the attachment straps at 0.10 m above the knee and 0.18 m below the knee, respectively. Among the compared attachment configuration in Tables 1-3, more than 50% of them used 4-attachment points as configuration. About 35% of them were
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configured 2-accachment points and only 15% of them used other configurations. The study of Wang et al. [17] indicated that the 4-attachment points were preferred than 2-attachment points for minimizing the undesirable interaction force at the attachment location. For the 4-attachment points, the study of Witte et al. [14] pointed out that the 2 points on the same body section should be located as far from each other as possible to maximize their leverage about the knee and minimize the magnitude of force applied to the user.
Electric
Electric
Electric
Celebi et al. [22]
Khamar et al. [23]
Wang et al. [17]
Layout Actuator and Power transmission Sensing System
A'-3
Zhou et al. [28]
Shepherd et al. [29] Electric
A'-2
C-4
Electric
C-4
A'-4
A-2
A'-2
A-2
A'-2
Electric
Liao et al. [27]
Luo et al. [26]
25]
Electric
Electric
Ergin et al. [21]
Saccares et al. [24-
Electric
Method FSM; Force (sit to stand),
Control Method
(motor position and velocity)
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effector force)
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(knee angle and angle velocity)
reducer and elastic element)
insoles/3 IMUs (gait events detection)
belt, ball screw and springs)
along spring)
SEA (brushless DC motor, timing Rotary encoder (knee angle) and linear encoder (position
mechanisms
bar linkage and gear meshing potentiometer (knee angle and angular velocity) and pressure
Brushless DC motors, gear-rack, 5- Load cell (human-exoskeleton interaction force) and
Encoder (motor position and velocity) and goniometers
exoskeleton and shank)
Torque control
(each state)
FSM; Impedance-torque control
control
Integral sliding-mode (ISM)
Torque control
Torque control
control
Back-stepping sliding mode
—
Impedance control
control (sit/swing state)
(stand state) and impedance
1
2
1
6
4
2
2
3
2
DOF
4.1
2.3
—
3.75
3.2
—
1.4
—
3.5
Swing phase of walking and sit-
Application Domain
0-120
—
-5-90
0-120
—
—
sit-to-stand assistance
Patients (post-stroke hemiparesis)
patients
Walking/sit-stand-sit assistance of
muscle weakness patients
Walking assistance for elder or
persons or patients
sit-to-stand assistance for healthy
for patients (hemiplegic stroke)
Stance phase of walking assistance
and extension) assistance
Human movement (knee flexion
0-90 Knee flexion/extension assistance
0-90 Knee rehabilitation training
(hemiplegic)
0-120 stand-sit assistance for patients
(kg) (deg)
Mass ROM
pro of
Force-torque sensor (interaction forces and torques between
IMU sensor (gait cycle detection)
Encoder (knee angle) and current sensor (motor current)
SEA (DC servo motor, gear
and universal joint
reducer and elastic element), S2AP
SEA (brushless DC motor, gear
belt
Current motor and 2-stage timing
and 4-bar linkage
Linear actuator, curved segment
and Schmidt coupling
cable-series compression spring 3 optical encoders (deflection of two disks)
Brushed DC motor, gear reducer,
rings
Brushed DC motor, belt and 3-RRP Optical encoders (motor position) and force sensors (end-
4-bar linkage
Brushless motor, gear reducer and 3 FSRs (COP), torque sensor (output torque) and encoder damper (stand to sit), position
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Actuation
Kim et al. [20]
Study
Table 1 Comparison of active knee assistive devices
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Inc.)
Keeogo (B-Temia
Martin Inc.)
ONYX (Lockheed
(RIVEXO Inc.)
T-Xanadu One
[43]
Figueiredo et al.
Felix et al. [42]
41]
Electric
Electric
Electric
Electric
Electric
Electric
Han et al. [35-38]
Karavas et al. [39-
Electric
Electric
Electric
Santos et al. [34]
[33]
Mazumder et al.
32]
Kardan et al. [18,
Electric
Kamali et al. [31]
A'-4
A'-4
A'-4
A-4
A'-4
A
A-3
A-2
C-2
C-2
A-4
blocks) and pressure force sensor (GRF) 2 optical encoders (motor position and link deflection
Gear-rack, Ball screw and spring)
CompAct ARS (brushless motor,
Motor, gear reducer, etc.
Motor, gear reducer, etc.
Motor, gear reducer, etc.
reducer
—
Sensors detect speed, direction and angle of movement
—
torque), IMU/FSR/EMG sensors (gait event detection)
—
—
—
(motor torque), strain gauges (user-orthosis interaction Adaptive impedance control
Tele-impedance control
state)
1
1
1
1
1
1
1
1
1
1
1
DOF
5.4
64
6.0
2.02
2.1
—
2.53
2.0
—
—
—
Application Domain
—
—
—
3-98
0-120
—
—
0-95
—
—
or
patients
walking
Patients with unsteady or older
Soldiers, workers, etc.
Hiker
assistance
Patients (gait pathologies) walking
assistance for healthy or patients
Walking, sit-to-stand and squatting
assistance
Healthy
rehabilitation
Patients (Neurological disorders)
walking/sit-stand-sit assistance
Patients (stroke or paraplegia)
assistance
Healthy person or patients motion
extremity weakness patients
Sit-to-stand assistance for lower
0-120 Support body weight for worker
(kg) (deg)
Mass ROM
pro of
Potentiometer (angle and angular speed), hall effect sensors
potentiometer (pivot point position) and 6 EMG sensors
springs)
Brushless DC motor and gear
angle), encoder (motor position after reduction),
gear reducer, lever arm and 2
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3 potentiometer (knee angle and distances between brake FSM; Position control (each
Multimodal SEA (DC servo motor,
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opto-electronic incremental encoder (actuator output)
worm gear and torsion spring)
Torque and impedance control
Magneto-resistant incremental encoder (motor rotation) and
Rotary SEA (brushed DC motor,
force control (stand up/ sit down)
Position control (walking) and
(OFAC)
deflection) and 6 EMG sensors
Optical sensor (knee angle) and infra-red sensor (spring
Output feedback assistive control
Impedance control
Torque control
Control Method
pulley, ball-screw and 2 springs)
SEA (brushed DC motor, belt-
pulley, ball screw and 2 springs)
2 encoders (spring deflection and motor position)
rotation) and 4 force sensors (GRF)
pulley, ball screw and spring set)
Linear SEA (serve motor, belt-
Linear encoder (estimate the output force), encoder (ankle
Linear SEA (serve motor, belt-
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Motor, gas spring and 4-bar linkage Encoder (knee angle) and load cell (gas spring load)
Layout Actuator and Power transmission Sensing System
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Electric
Method
Actuation
Noh et al. [30]
Study
Table 1 continued
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Electric
Electric
Electric
Electric
Electric
Wehbi et al. [3]
Huang et al. [49]
Witte et al. [14]
Liu et al. [50]
Rifai et al. [51-52]
Inc.)
(Lockheed Martin
A'-5
A-2
C-4
A-4
A-4
A-4
A
Pneumatic A'-4
Electric
Shan et al. [48]
Soft exoskeleton
Electric
Jiang et al. [47]
[46]
Angular potentiometer (knee position) and 2 FSRs (estimate
the motion intention)
Encoder (knee angle), goniometer (hip angle and angular velocity) and 2 FSRs (gait phase detection)
Servo AC motor, double-tendon-
sheath transmission and pulley
Brushless DC motor, ball screw,
belt transmission and cable drive
Torque sensor (motor output torque), encoder (knee position) and 3 EMG sensors
Brushless DC motor, beading bars
and stiffness variation system
Tubular pneumatic actuators
screw and cable-pulley
Some micro-sensors
Encoder (joint angle and angular velocity)
strain gauges (cable tension)
pulley
Brushless DC motor, belt, ball
Encoder (knee angle), heel switches (foot contact) and
spring and 1 for knee angle) and EMG sensor
Servo motor, 2 bowden cables and
gear, torsion spring)
2 potentiometers (1 for input displacement of the torsion
—
Nested saturation based control
EMG based impedance control
Torque control
impedance control
feedback control and zero
Hybrid of direct EMG bio-
Active impedance control
Fuzzy control
EMG based position control
1
1
1
6
1
1
1
1
1
1
DOF
2.3
—
—
0.76
0.84
—
2.1
—
3.18
3.2
—
0-120
—
0-120
—
—
—
—
—
—
(kg) (deg)
Mass ROM
pro of
for predicting knee angle)
screw
re-
Encoder (knee angle) and 5 EMG sensors (neural network
Motor, belt transmission and ball
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compression sensors (exoskeleton output torque)
actuator (BTSA) (DC motor, bevel
state)
acceleration), torque sensor (patients torque) and 2 Torque control
Optical encoder (knee angle, angular velocity and angular
sensor (exoskeleton output torque)
brake
Back-drivable torsion spring
Control Method
FSR (foot ground contact), encoder (knee angle) and torque FSM; Torque control (each
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electro-rheological fluid based
A-4
chain sprocket
Motor, planetary gear system and
Electric
Brushless motor, gear reducer and
and Weinberg et al.
A'-4
Layout Actuator and Power Transmission Sensing System
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Electric
Method
Actuation
Nikitczuk et al. [45]
44]
Lerner et al. [16,
Study
Table 1 continued
Soldiers
rehabilitation
Lower limb assistance and
Motor function rehabilitation
assistance
Healthy or patients walking
Patients walking rehabilitation
patients with lower limb weakness
Walking swing assistance for
walking assistance
Patients (impaired motion ability)
—
swing phase of walking assistance
Stroke patients stance flexion and
children with crouch gait from CP
Knee extension assistance for
Application Domain
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pneumatic
[57]
solenoid valve
2 PAMs and high-speed on/off
Pneumatic B-4
Chandrapal et al.
2 PPAMs and 4-bar linkage
regulator and 4 (2 pairs) PAMs
compressor,
lP
Encoder (knee angle) and 6 EMG sensors
re-
2 pressure sensors (GRF) and IMUs (knee angle)
Position control
pressure control (low level)
Stiffness (high level) and binary
control
Proxy-based sliding mode
Stiffness control
Torque control
Control Method
1
3
1
1
1
DOF
—
0.16
4.5
0.8
9.0
—
—
0-90
0-74
—
(kg) (deg)
Mass ROM
pro of
knee angle) and 2 force sensors (exoskeleton output torque)
2 pressure sensors (PPAMs pressure), encoder (exoskeleton
Pressure sensor (ground contact events detection)
Many sensors detect the human motion intention
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Air
Soft-inflatable actuators
Pneumatic A'-4
Pneumatic B-2
Sridar et al. [55-56] Pneumatic C-2
Knaepen et al. [54]
Beyl et al. [53]
Maeda et al. [19]
Inc.)
Fan-shaped airbag
Layout Actuator and Power Transmission Sensing System
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Method
Actuation
Xo (Roam Robotics Pneumatic A'-2
Elevate Robotic Ski
Study
Table 1 continued
Disabled persons assistance
assistance
Patients (stroke) walking
assistance
Patients (SCI/MS) walking
assistance
Healthy person or patients walking
Skier
Application Domain
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Dollar et al. [64]
A'-4
Elliott et al. [61-63] A'-2
60]
A-2
A-2
Rogers et al. [59]
Shamaei et al. [2,
A'-2
Tung et al. [58]
Electric actuator and wrap
Transmission
Actuator and Power
Knee extension of stance phase (assistance)
2.
Knee flexion of stance phase (storing energy)
Knee extension of stance phase (assistance)
1.
2. carriage
ball screw and spring
Brushed DC motor, belt,
acceleration),
1
Finite state machine 1
—
re-
1
pro of
(knee angle) and foot switch (ground contact Position control
Encoder (motor position), potentiometer
events)
DOF
Finite state machine 1
Control Method
angular velocity) and Finite state machine 1
(vertical
encoder (exoskeletal knee angle)
gyroscope (hip
Accelerometer
and insole sensors (gait cycle detection)
Rotary potentiometer (knee velocity sign)
Foot switch (heel-ground contact detection)
IMU (the absolute angle of the thigh link)
Sensing System
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Clutch, planetary gear and
tendon and pulley
Stiffness switching module,
Air spring
2. Knee extension of terminal stance (assistance) elastic strips
1. Knee flexion of early stance (storing energy)
Weight acceptance phase assistance
Knee flexion of stance phase (storing energy)
1.
spring
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Knee flexion assistance
Layout Walking Principle
Study
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Table 2 Comparison of quasi-passive knee assistive devices
2.5
0.71
2.45
—
1.2
(kg)
0-97
0-130
—
—
—
(deg)
Mass ROM
walking,
stairs
(musculoskeletal
assistance
Healthy
assistance
Healthy
persons
person
running
running
disorders) walking assistance
Patients
Descent assistance
descent-ascent assistance
Patients
Application Domain
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C
A
A'-4
B-2
Jun et al. [71]
Wu et al. [72]
Pagani et al. [73-74]
Moyer et al. [75]
to the knee
Three-points bending; Apply abduction or adduction moment
to the knee
Three-points bending; Apply abduction or adduction moment
3. Free swing knee extension
deg) and swing knee flexion (30-55 deg)
2. Releasing energy in stance knee extension (exceeds 5
—
—
4-bar linkage and gas spring
1
1
1
1
1
1
3
1
2
DOF
—
—
0.73
0.14
0.9
0.99
—
—
0.7
(kg)
FSM: Finite state machine FSR: force sensitive resistor IMU: inertial measurement unit
DOF: degree of freedom of simulated biological knee joint
ROM: range of motion of knee assistive devices in sagittal plane
SEA: series elastic actuator
—
—
—
—
0-120
—
—
0-160
0-120
(deg)
Mass ROM
pro of
band (PEB)
forces and torques at knee joint
re-
Parallel coupled compliant plate (PCCP) and pennate elastic
Hybrid mechanism (rigid and flexible) provides assistive
1. Storing energy in stance knee flexion (5-30 deg)
springs
4. Knee extension exceeds 60 deg (releasing energy)
lP 4 Springs, 4 wire cables, 4 pulley disks and 4 smaller return
Liquid spring, cord and gear
Srew-jack, compression spring and universal joint
Teflon spring, eccentric pulley and compression spring
4-bar configuration and planar spiral spring
Actuator and Power Transmission
3. Knee flexion exceeds 60 deg (storing energy)
2. Knee extension (releasing energy)
1. Knee flexion (storing energy)
—
2. Knee extension (releasing energy)
1. Knee flexion (storing energy)
2. Knee extension exceeds 60 deg (releasing energy)
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1. Knee flexion exceeds 60 deg (storing energy)
Walking Principle
Types of layouts: A lateral-support layout; A' improved-lateral-support layout; B two-sides-support layout; C anterior/posterior-support layout; 2/3/4/5 numbers of attachment points
B-4
Ranaweera et al. [70]
Technology Inc.)
A'-4
A-4
A-4
B-4
Layout
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Levitation (Sping-Loaded
Saleem et al. [69]
Li et al. [68]
Yuan et al. [67]
66]
Chaichaowarat et al. [65-
Study
Table 3 Comparison of passive knee assistive devices
Knee osteoarthritis (KOA) patients
Knee osteoarthritis (KOA) patients
Paraplegics walking assistance
mobility of user
Protect knee from injury and increases
Healthy person squat lifting assistance
bearing workers, athletes, etc.
KOA or knee injured patients, load-
Overweight person walking assistance
assistance
Weight bearing walking and climbing
Healthy person cycling assistance
Application Domain
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Journal Pre-proof According to the location of support, the layout of mechanical frame can be divided into four categories: lateral-support layout, improved-lateral-support layout, two-side-support layout and anterior/ posterior-support layout, as shown in Fig. 2. Lateral-support layout means the mechanical frame on thelateral side of lower limb, such as the researches from Wehbi et al. [3], Felix et al. [76-77], Nikitczuk et al. [45] and Shan et al. [48]. The deficiency of this layout is that the undesired twisting force can be generated when the assistive torque transmits through soft attachment components, such as straps. Improved-lateral-support layout resolves the problem by extending the mechanical frame from lateral to anterior and/or posterior, such as the devices from Rifai et al. [51-52], Luo et al. [26], Celebi et al. [22],
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ONYX (Lockheed Martin Inc.), Keeogo (B-Temia Inc.), Levitation (Sping-Loaded Technology Levitation Inc.) and Elevate Robotic Ski Xo (Roam Ski Inc.). Two-side-support layout also resolves the problem by adding a mechanical frame on the middle side of lower limb. But compared with improvedlateral-support layout, this layout may produce interference between two legs according to the researches from Chandrapal et al. [57] and Ranaweera et al. [70]. Anterior/posterior-support layout means the mechanical frame on the anterior or posterior side of lower limb, such as the researchers from Kardan et al. [18, 32], Liao et al. [27], Sridar et al. [55-56]. Compared with others, the structure of this layout is relatively complicated. Among the compared mechanical frame layout in Tables 1-3, more than 38% layout used lateral-support layout. Improved-lateral-support layout was utilized by 40% and only about
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10% layout used two-side-support layout. For anterior and posterior support layout, the percentage was
a
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about 12%.
b
c
d
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Fig. 2 The mechanical frame layout of knee assistive devices, a lateral-support layout; b improved-lateral-support layout; c two-side-support layout and d anterior/posterior-support layout [17]
In order to enhance security and comfort, other issues related to attachment design should be considered. Firstly, the frame should be held firmly on the lower limb and can be adjusted to fit the user’s height and limb’s length. Secondly, the cushioning pads should be placed at the interface to avoid direct contact and guarantee wear comfort. Finally, the areas in which muscle bellies may protrude should be avoid, most notably around the hamstrings and gastrocnemius.
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2.2 The knee joint alignment design According to the human physiological property, the main motion of human knee is the flexion-extension in sagittal plane. Thus, one purely rotary DOF is always used as the design of device knee [43-44, 57, 72]. Reducing the weight and simplifying the complexity of the mechanical structure and control systems are the advantages of this design. But according to the researchers from Celebi et al [22] and Kim et al [20], the real knee joint moves with a polycentric motion in sagittal plane, i.e. the center of rotation changes during the rotation, as shown in Fig. 3a. The trajectory of the center of knee joint seems to be a J-shaped curve in sagittal plane [78]. Thus, one purely rotary DOF would cause misalignment between
Journal Pre-proof human and device knee joint. As the misalignment appears, the human-device system becomes mechanically over-constrained, causing an undesired tangential force at the attachment locations and excessive internal force at the human knee [17]. These forces would scratch the user’s skin and become
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a source of loose binding force.
Fig. 3 Schematic representation of knee polycentric motion and bionic knee mechanism, a knee polycentric motion in sagittal plane [22]; b 4-bar linkage [20]; c 5-bar linkage [27]; d 3-RRP mechanism [21]; e Schmidt coupling [22] and f soft exosuit [15-56]
There are many methods that have been applied to decrease the misalignment. Firstly, one-DOFmechanism always be utilized to improve the alignment. Tucker et al. [79-80], Kim et al. [20], Khamar
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et al. [23], and Chaichaowarat et al. [65-66] implemented 4-bar linkage to similar the polycentric motion of human knee, as shown in Fig. 3b. The rolling joint mechanism was utilized in the study of Wang et al. [17]. 5-bar linkage and gear meshing mechanisms were selected in the studies of Liao et al. [27], Zhou et al. [28] and Li et al. [68], as shown in Fig. 3c. Secondly, an under-actuated mechanism was employed in many studies. It provides free movement of knee rotation center without affecting the transmission of assistive torque. Celebi et al. [22] designed an AssistOn-knee which implemented an under-actuated Schmidt coupling as the underlying mechanism, as shown in Fig. 3e. This mechanism aims at self– aligning by accommodating the passive translation of the instantaneous center of rotation of the knee.
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Ergin et al. [21] selected 3-RRP mechanism as the underlying mechanism for implementation of selfaligning knee, as shown in Fig. 3d. This structure allowed translational movements of the knee on the sagittal plane along with the rotation. Lastly, the designs with soft structure also effectively minimize or eliminate misalignment. Sridar et al. [55-56] developed a soft-inflatable knee exosuit that can enhance the compliance between human and device, as shown in Fig. 3f. In order to improve the alignment in all three planes, the design of 6 DOFs was always be utilized. Saccares et al. [24-25] designed an iT-knee that possesses the features of 6 DOFs and self-aligning. The iT-knee used two articulated parallelograms mechanism and a universal joint to overcome the misalignment between the human knee and device knee. Witte et al. [14] developed a simple rotary joints
Journal Pre-proof for alignment in three planes. The flexion and extension of device were actively controlled. But the small displacements in other 5 DOFs were allowed through high compliance in uncontrolled directions. Among the compared DOF design of knee assistive devices in Table 1-3, more than 77% of them has not considered the alignment problems. One-DOF-mechanism and under-actuated mechanism were utilized by about 11% and 4% to solve the knee alignment problems, respectively. And only 8% of them used other methods to achieve knee alignment.
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2.3 The actuation and power transmission design The actuation and power transmission mainly determine the performances of knee assistive devices, such as the efficiency, output torque, and portability [5]. Thus, the design of these two parts is crucial significance for the assistive devices. Based on the method of actuation, knee assistive devices can be classified into three categories: active actuation, quasi-passive actuation, and passive actuation. Among the compared actuation methods in Tables 1-3, the active, quasi-passive and passive actuation were utilized by about 73%, 10% and 17%, respectively. For quasi-passive or passive actuation, a spring controlled by clutch are usually employed for actuation. And the gear drives, ball screws, cable drives or belt drives are usually employed for power transmission. Based on the type of actuators, active actuation
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can be divided into electric actuation, pneumatic actuation, and hydraulic actuation. In electric actuation, the power transmission can be implemented by gear drives, ball screws, cable drives, belt drives or hybrid drives. In pneumatic actuation, pneumatic cylinder and pneumatic artificial muscle (PAM) are usually employed for power transmission. Along with oil pump, the power transmission of hydraulic actuation can be implemented by hydraulic piston and cylinder [12]. Among the compared active actuation
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methods in Table 1, more than 83% of them used electric actuation and 17% of them used pneumatic actuation.
In addition, there are various types of power sources used in knee assistive devices, such as direct driving motor, tendon mechanism and electric material power source, as shown in Fig. 4. As the most widely used power source, the direct driving motor can directly translate the rotational moment of motor
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into the assistive moment of knee joint, or translate the rotational movement of motor into linear movement of electric putter and then into the rotational movement of knee joint through some mechanical transmission devices, as shown in Figs. 4a and 4b. Larger volume, higher weight, limited energy supply and poor wearability are the drawbacks of this power source. As a soft, light weight and comfortable method, the power source of tendon mechanism can utilize the external devices including spring, artificial muscle, cable and so on to simulate the function of human tendon. For the quasi-passive or passive device, spring or elastic band is always utilized, which can absorb power from knee joint when the knee dose negative work and release it to knee joint when the knee dose positive work, as shown in Fig. 4c. For the
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pneumatic and electric devices, artificial muscle and cable is always employed, respectively, which simulate the contraction of human flexor or extensor muscle-tendon unit and provide power for the knee movement, as shown in Figs. 4d and 4e. Besides, for better providing assistive moment, the direction of artificial muscle and cable imitate the direction of internal muscle-tendon unit. The electric material is a new developing power source in recent years, which can convert other forms of power into electric power by using new materials and then provide assistive power for the knee assistive devices. The common used electric material power sources include piezoelectric materials, triboelectric materials, solar energy materials, etc., as shown in Fig. 4f. Unlimited energy supply is the biggest advantage of this power source.
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c
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e
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Fig. 4 The power sources of assistive devices, a direct driving motor-rotary [39]; b direct driving motor-linear [18]; c tendon mechanismspring [62]; d tendon mechanism-artificial muscle (Lockheed Martin Inc.); e tendon mechanism-cable [81] and f electric material power source [82]
The electric actuation is commonly used for the knee assistive devices because of the higher controllability and precision. Based on the type of drive, the electric actuation can be divided into rigid drives, soft drives and hybrid drives. As a traditional electric actuation method, rigid drive is common utilized to transmit the motor power into knee joint. For example, Lerner et al. [16, 44] implemented a
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chain-sprocket mechanism and Nikitcauk et al. [45] implemented planetary gear system and electrorheological fluid based brake to transmit the power from motor to knee joint. Based on the linkage mechanisms, Tucker et al. [79-80] and Shirota et al. [83] used worm gear and 4-bar linkage, Kim et al. [20] used gear reducer and 4-bar linkage, Khamar et al. [23] used curved segment and 4-bear linkage, and Liao et al. [27] used gear-rack, 5-bar linkage and gear meshing mechanisms to implement the power
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transmission. Although the rigid drive is widely used, there are still some inherent limitations, such as bulky, heavy weight, bad human-device physical interaction and wearing uncomforted. As a newly developing electric method, the soft drive possesses high back-drivability, lightweight and wearing comfort, and can solve the limitations of rigid drive. For example, Shan et al. [48] employed driving pulley, double-tendon-sheath and driven pulley and Witte et al. [14] applied 2 Bowden cables (located
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on the anterior and posterior side of the leg) to transmit the power. Bearing the tension but not bearing the stress is the limitation of these drive method. The hybrid drives can combine the advantages of the above methods. For example, Rifai et al. [51-52] and Wehbi et al. [3] used belt, ball screw and cablepulley and Ergin et al. [21] employed belt and self-adjusted 3-RRP rings to transmit the power. Because of the inherently compliance, light weight and high power-weight ratio, pneumatic actuation can provide a better human-robot interaction. This actuation commonly be used as a tethered device for rehabilitation training. And the control unit, air compressor and pump are always out of the human body. The method of providing air power source is an important issue in pneumatic actuation. Based on the
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actuation mode, pneumatic actuators can be classified into rotary actuators and linear actuators. For the former, Sridar et al. [55-56] developed a soft-inflatable exosuit with I cross-section to provide knee extension assistance in the swing phase of gait. Elevate Robotic Ski Xo designed by Roam Robotics Inc. utilized fan-shaped airbag to provide knee power for skiers. For the latter, Beyl et al. [53, 84] and Knaepen et al. [54] employed pleated PAMs and 4-bar linkages, Maeda et al. [19] employed an agonistantagonist PAMs system and a soft knee exoskeleton designed by Lockheed Martin Inc. employed tubular pneumatic actuators to transmit the motor power into knee joint. The hydraulic actuation is usually designed for load carrying devices because of the high power-weight ratio. But due to the complexity and huge volume of oil supply system, hydraulic actuation is very seldom used in knee assistive devices.
Journal Pre-proof In order to enhance the back-drivability of actuation and decrease the output mechanical impedance, the series elastic actuator (SEA) has been employed in many knee assistive devices. SEA is a bionics device in which an elastic element is intentionally introduced in series between actuator’s driver and output. The advantages of this configuration include lower reflected inertia, shock tolerance, and more accurate stable force control in unconstrained environments [5]. Based on the type of elastic element, SEA can be divided into linear SEA and rotary SEA. For the former, Kamali et al. [27, 85], Kardan et al. [18, 32] and Mazumder et al. [33] developed a SEA which consisted of serve motor, belt-pulley, ball screw and two springs, Saccares et al. [24-25] developed a SEA which equipped with brushless DC motor,
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Harmonic drive gearbox and flexible elements, and Han et al. [35-38] developed a multimodal SEA that combined the motor, spring, brake and clutch elements to achieve the power transmission. Compared with the linear SEA, the rotary SEA can transmit the motor moment into the knee rotary moment directly. For example, Santos et al. [34] designed a rotary SEA which included a DC motor, a worm gear and a torsion spring, Luo et al. [26] designed a SEA which equipped with servo motor, gear reducer and elastic element. Shepherd et al. [29] developed a SEA which included brushless DC motor, timing belt, ball screw and fiberglass beam spring. Huang et al. [49] designed a backdrivable torsion spring actuator which consisted of DC motor, bevel gear and torsion spring to transmit the motor moment into knee rotary moment directly.
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Because of the simple structure, little volume and light weight, the quasi-passive and passive actuation are very popular in recent years. For the quasi-passive actuation, Elliott et al. [61-63] employed a clutchspring knee assistive device that could store energy in early stance phase of running and release energy in terminal stance phase of running. Dollar et al. [64] used spring carriage and Rogers et al. [59] used air spring to store and release energy in stance phase. Shamaei et al. [2, 60] utilized a spring based stiffness
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switching module, tendon and pulley to provide weight acceptance phase assistance. For the passive actuation, Chaichaowarat et al. [65-66] adopted planar spiral spring and 4-bar linkage to store and release energy when the knee angle exceeded 60 deg. Yuan et al. [67] and Li et al. [68] used Teflon spring, eccentric pulley and compression spring to achieve weight bearing assistance. Ranaweera et al. [70] implemented spring, wire cable, pulley disk and smaller return spring, to store the biomechanical energy
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dissipated at the knee during decent phase and release the harnessed energy in the ascent phase in a squatting cycle. Wu et al. [72] implemented a 4-bar linkage and gas spring to store energy in knee flexion of stance phase and release energy in knee extension of stance phase and knee flexion of swing. Levitation designed by Sping-Loaded Technology Inc. utilized a liquid spring to store energy when the knee flexed and release it when the knee extended.
2.4 The interrelationship of the three components
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The designs of three components, especially the designs of joint alignment and actuation-power transmission, influence each other. Therefore, understanding the interrelationship of these three designs is an important issue in the mechanical design of knee assistive devices. For the joint design of one purely rotary DOF, electric actuation, pneumatic actuation, quasi-passive actuation and passive actuation are all common actuation methods. Among them, SEA as a new developing electric actuation is commonly implemented in recent years. For the joint design of 4-bar linkage, the driving part is connected to the driving bar of 4-bar linkage and the coupler bar is connected to the user’s shank, and then generates the knee joint motion. The common actuation methods for a 4bar linkage joint include electric actuation, such as the devices of Kim et al. [20], Khamar et al. [23] and
Journal Pre-proof Noh et al. [30], pneumatic actuation, such as the devices of Beyl et al. [53] and Knaepen et al. [54], and passive actuation, such as the devices of Chaichaowarat et al. [65-66] and Wu et al. [72]. Because of the structure of 4-bar linkage, the common mechanical frame layout of knee assistive device is later-support [23, 30, 72] or improved-lateral-support layout [20, 53-54]. For the joint design of under-actuated mechanism, electric actuation is the common actuation method. Cable is common employed to translate the moment of motor to the 3-RRP mechanism [21], Schmidt coupling [22], customized rotary joint [14] and so on. Based on the location of cable, the common mechanical frame layouts include later-support
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[21], improved-later-support [22] and anterior/posterior-support layout [14].
3. The Sensing and control systems of knee assistive devices
In the human-device system, the controllers included human brain and external controller are working parallel to each other. The external controller can significant improve the efficiency, precision, smooth and wear comfort of the assistive device based on the sensing and control systems. According to the hierarchy, the external control system can be divided into high-level and lower-level controllers. The high-level controller aims at inferring the human motion intention and determining the corresponding
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assistance strategies. And the lower-level controller aims at controlling the actuators to implement the desired position or torque based on the information from the high-level controller. Based on the control algorithm, the control system can be categorized as position control, torque control, impedance control and other control. The position control is employed to make sure the knee
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rotation of actuator in a desired position trajectory based on the difference between the desired angle (𝜃𝑑 ) and real angle (𝜃) [47, 57]. This control algorithm is always used for patients with poor motion control. The torque control is used to rotate the knee of actuator to achieve the desire torque trajectory based on the difference between the desired torque (𝑇𝑑 ) and real torque (𝑇) [14, 45, 29]. And it is always used for patients with voluntary locomotor capability. Both the position and torque control algorithm belong to trajectory tracking approach and single closed loop control. For the impedance control, the interaction
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between human and assistive device is put into the control loop and double closed loop controller always be used as the control structure [31, 41, 50, 79]. As shown in Fig. 5, the outer loop acted as position controller, in which the desired angle is tracked. The inner loop is torque control producing the desired torque for the actuator. The link of two loops is achieved by impedance controller. The impedance control law can be written as
𝑇𝑑 = 𝐾𝑆 (𝜃𝑑 − 𝜃) + 𝐾𝐷 (𝜃𝑑̇ − 𝜃̇ ) + 𝐾𝐼 (𝜃𝑑̈ − 𝜃̈ )
(1)
where 𝐾𝑆 , 𝐾𝐷 and 𝐾𝐼 are the virtual stiffness, virtual damping and virtual inertia, respectively. Compared with position control and torque control, impedance control can not only control the position
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and the torque but also control the relationship and interaction between human and knee assistive device. Therefore, it can improve the human-machine coordination and wearing comfort, and widely implemented in recent years. Besides, there are many new control algorithms. Kardan et al. [18, 70] proposed a newly output feedback assistive control (OFAC) algorithm which did not require any information about contact force, systems accelerations or their future paths. Rifai et al. [51-52] developed a nested saturation based control which can effectively avoid the actuator’s saturation, guarantee and enhance the wearer’s security. Beyl et al. [53, 84] reported a proxy-based sliding mode control (PSMC) that can combine the trajectory tracking and a safe response of perturbations.
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Fig. 5 Block diagram of typical double-closed-loop impedance control system
Based on the input signals, the control system can be classified into human biological signal based control methods, interaction signal based control methods, and assistive device signal only based control methods. Gopura et al. [12] pointed out the categorization based on input signals is more important, since
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the input signals are essential to identify the human motion. Therefore, in the following of this section, the control systems will be reviewed and discussed by this categorization
3.1
Human biological signals based control systems of knee assistive devices
The biological signals directly reflect the human motion intention and generate prior to the corresponding motion. Therefore, the biological signal based control system can fully recognize the human motion intention with any signal delay and lose compared to the others [5]. There are two noninvasive biological signals which are always used in the knee control systems: surface
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electromyogram (sEMG) and surface electroencephalogram (sEEG).
sEMG based control system is always recognized the motion intention by building the relationship between sEMG and desired knee angle/torque. Based on the modeling methods, the sEMG based control model can be divided into sEMG-driven musculoskeletal (MS) model and neural network (NN) model. For the former, Hill-based parametric model is common employed, which need to estimate the parameters
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based on the biomechanical property of user’s muscle and joint. For example, Karavas et al. [39] described a MS model that can use sEMG to estimate the knee angle and stiffness. An impedance controller was then utilized to track the desired knee angle and stiffness to achieve the control of actuator. Huang et al. [49] reported a sEMG based biofeedback control which can achieve the nonlinear mapping between the sEMG and the muscle force. And the muscle force was then used to track the user defined
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trajectory to assist human walking rehabilitation. Pena et al. [86] developed an optimal sEMG-driven adaptive impedance control method. A simplified and optimized MS model was used to estimate the knee torque and stiffness. And an adaptive impedance control was employed to track the desired trajectory and stiffness of knee. Compared to the former, which establish an mathematic model, the latter can improve the nonlinearity of model, smoothness and efficiency of control system. For example, Jiang et al. [47] presented a four-layer feed-forward NN model to establish the mapping between sEMG and knee angle. And position control method was utilized to control the actuator to achieve estimate knee angle. Pena et al. [86] utilized a multilayer perceptron NN to map the relationship between EMG single and knee
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torque/stiffness. Mefoued et al. [87] designed a radial basis function neural network (RBFNN) that can map the nonlinearities between EMG signal and desired knee angle. A second order sliding mode control was developed to control the assistive device by using the desired knee angle. Many related researches show that the sEMG-based control system can estimate the human intention efficiently. But there are still some shortcomings. For example, the sEMG-based knee assistive device does not apply to the patients with physical cripple, paralysis, hemiplegia, etc.; sweating affects sEMG sensor measurement after a long-time exercise; the sEMG sensor are easy to off and shift after a strenuous exercise; the sEMG signal present greatly differences from subject to subject and even for one person from day to day. Because the EEG signal are generated directly by the human brain and not transmitted by the human
Journal Pre-proof nervous system, the EEG-based control systems can recognize the human intention earlier than sEMGbased systems, and EEG-based knee assistive device can apply to the patients with only brain healthy. For example, Bai et al. [88] proposed an EEG based volitional control. Seven channels (C5, C3, C1, Cz, C2, C4 and C6) which placed on the central motor areas were selected to acquire the EEG signals. Machine learning algorithms were designed to decode human volition and subsequently to command the knee-locker switch of assistive device. Villa-Parra et al. [89] developed an EEG based control method to estimate the motion intention. The EEG signals related with planning motions were implemented through both event-related synchronization/desynchronization (ERS/ERD) and slow cortical potential (SCP).
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Mercado et al. [90] reported a hybrid control method based on EEG and EMG. The intention related to the velocity change was classified by the EEG, meanwhile the amplitude of each intention was estimated by the sEMG. The support vector machine (SVM) was proposed to classify the feature vector of EEG into two speed classes, and the neural network of maximum sensibility (NNMS) was used to estimate the knee joint torque. Compared to sEMG signals, the application of EEG signals possesses more limitations. For example, the EEG signals are more difficult to capture and complex to estimate human motion intention; EEG signals are rarely used for continuous motion estimation and often used for motion classification; the wearable EEG measurement equipment is more complex and uncomfortable. Although the biological signals (sEMG and EEG) based control systems have been widely used in the
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knee assistive devices, they are still under laboratory stage and have some limitations in practical application. Firstly, the signal-to-noise ratio and accuracy of these biological signals are relatively lower because the signals are acquired from the surface of human body. Secondly, the biological signals are unstable and easily influenced by the circumstance and electrode placement. Thirdly, the wearing of these expensive.
3.2
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knee assistive device is more complex and the measurement equipment of sEMG and EEG is more
Human-device interaction signals based control systems of knee assistive
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The human-device interaction signals are critical to estimate the knee motion intention, although it occurs later than the biological signals. Some of studies directly measured the interaction force at the human-device interface to estimate the motion intention. Others utilized the ground reaction force (GRF) measured by force sensitive resistor (FSR) to estimate the motion intention. There are a lot of benefits to add the interaction force at the human-device interface to the control system. For example, the interfacial force signal based control system can effectively prevent excessive interaction force and its harm to human body when a mistake occurs in the control system; the interaction
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force in the interface of knee assistive device is relatively small, which can improve the wearing comfort of assistive device. For example, Saccares et al. [24] directly measured the interaction forces and torques as the feedback signals. A torque control method was utilized to track the desired trajectory. Shan et al. [48] utilized two FSRs, respectively located at the front and back of shank, to directly measure the interaction force. A fuzzy control algorithm was developed to enhance the assistance performance. Liao et al. [27] employed a load cell to measure the interaction force and a linear potentiometer to calculate the knee angle and angular velocity. A double closed loop control method (inner torque loop and outer position loop) was used to achieve tracking tasks. And a PSMC method was utilized to establish the relationship between two loops. Liu et al. [91] utilized the interaction force as the feedback signal of
Journal Pre-proof control strategy. Three IMUs, which placed on the front of shank part, thigh part of assistive device and the back of shoe, were used to predict the locomotion intention based on SVM. For GRF signals based control methods, finite state machine (FSM) based control algorithm is common utilized, which can divide the gait cycle into some discrete phases. Implantable foot sensors, which are inserted into shoes, are commonly implemented to acquire GRF signals and detect human gait events. For each phases, a responsive low-level control strategy is utilized to control knee assistive device. Portability and friendliness are the advantages of these control systems. For example, Maeda et al. [19] applied a pressure sensor attached to the insole to detect the gait events. For each event, a stiffness control
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algorithm was implemented to control the knee stiffness of actuator. Lerner et al. [16] utilized a FSR and encoder to detect the states of gait cycle. For each state, a PID control scheme was used to achieve the desired knee torque output. Wehbi et al. [3] employed insoles with two FSR sensors to detect the swing phase, and encoder and goniometer to detect the swing sub-phase of a gait cycle. A nonlinear impedance control with gait based desired admittance model was used to control the interaction between lower limb and assistive device. Shamaei et al. [2, 60] implemented a FSM to engage the assistance spring of quasipassive device. Insole sensors and potentiometers were used to detect gait states. Although the GRF signals based control system is widely used, there are still some limitations. For example, the gait cycle is divided into discrete phase and smooth control can’t be achieved; the discrete phase is pre-defined and
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unidentifiable condition will happen when the undefined phase appear.
Hybrid signals based control systems combine the advantages of human-device interface signal based control systems and GRF based control systems. For example, Zhou et al. [28] developed a FSM based impedance control algorithm to assist walking motion. A wearable plantar pressure measurement system with four sensors in each shoe was used to detect gait states. And a rotary sensor was used to measure
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the real knee angle and angular velocity. For each state, a double closed loop impedance control was implemented.
Many convincing evidences show that the estimated accuracy, stability, robustness and safety of control system are higher than others to use human-device interaction signal to estimate human motion intention, but there are still some inherent limitations. For example, the time delay of human-device
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interaction signal based control system is relative longer than other methods because the human-device interaction signal occurs later than the movement happens.
3.3
Device signals only based control systems of knee assistive devices
Compared to the others, the assistive device signal only based control methods estimate or follow the user’s motion intention only used the signals acquired from assistive devices itself. Encoder, gyroscope, accelerometer and IMU are common employed to measure the joint angle, angular velocity and angular
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accelerometer of assistive device. A dynamical model of assistive device and human body was usually utilized to achieve the control algorithm [5]. The device signals only based control systems are the primary control algorithm. And it is often used in the quasi-passive devices or the preliminary experimental stage of power devices currently. Because of no prediction of human motion intention, simple structure of sensing and control systems are the advantages of these method. For example, Elliott et al. [62] utilized encoder, gyroscope and accelerometer and Tung et al. [58] utilized an IMU to measure the angle of knee joint and to detect the gait states. A FSM method was then used to control the storing or releasing energy of spring. Wang et al. [17] employed an IMU to measure the ankle angular velocity and then detect the gait cycle. A high level
Journal Pre-proof torque control and low level current control were applied to achieve the assistive control strategy. Shepherd et al. [29] employed two encoders to provide joint angle feedback. The torque feedback can be calculated by the feedback signals from the two encoders. And a PID based toque control was used to control the output torque of actuator. Nikitczuk et al. [45] and Weinberg et al. [46] developed a non-linear adaptive PID torque control method to control the actuator of assistive device. An optical encoder and a torque sensor were used to provide joint angle and torque feedback, respectively. Santos et al. [34] used the output torque and joint angle as the feedback signals in the torque and impedance control cycle. An the actuator output torque and joint angle, respectively.
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opto-electronic incremental encoder and a magnate-resistant incremental encoder was utilized to measure There are many limitations of device signal only based control methods. For example, it is difficult to establish an accurate model of human and/or assistive device to achieve accurate control; the security, human-machine coordination and comfort of these control method are lower than others. Therefore, the application of devices signal only based control methods is relative rare currently.
4. The Performance evaluation of knee assistive devices
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The performance of knee assistive devices determines the feasibility and usability of such devices. And the design aim of a knee assistive device determines its evaluation methods and performance. According to the design aim, the knee assistive devices can be divided into providing assistive power to human knee joint in the sagittal plane and providing knee orthosis in the coronal plane. For the former,
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healthy person always be selected as the experimental subjects to evaluate the performance because of the humanitarian considerations or experimental condition restrictions. The comparison of the subjects with and without the knee assistive devices always be considered as an evaluation method. For the latter, the patients with medial knee osteoarthritis always be selected as the experimental subjects because these devices don’t work for healthy person. The comparison of the patients pre and post wearing the devices always be considered as an evaluation method. According to the measuring means, the evaluation
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methods can be divided into three main groups: gait biomechanics, muscle activity analysis and metabolic cost analysis. Although the performance of mechanical or control algorithm designs are also important, they are not taken into account in this section. For the gait biomechanical analysis, a camera motion capture system with force platforms embedded in the walkway or a treadmill are common used to collect kinematic and kinetic data, as shown in Fig. 6a and 6b. And the joint angle, torque and power can be acquired by the inverse kinematics and dynamics. Gait biomechanical analysis is an elementary evaluation method and common used to verify the performance of all kind of knee assistive devices. For the knee assistive devices which provide assistive
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power in the sagittal plane, the power reduction of human knee joint is common set as the main evaluation index. For example, the knee exoskeleton of Kamali et al. [85] can reduce the average knee power by about 30% in sit-to-stand movement. Besides, no considerable difference of other joints biomechanics between subjects with and without device is an auxiliary evaluation index. For example, Shamaei et al. [2] presented the proposed knee assistive device does not have a considerable effect on the angle and moment of ankle and hip joints in the sagittal plane. Although the above two evaluation methods are available, it can only be used to evaluate the short-term effect of knee assistive devices. To obtain more comprehensive evaluation, a long-term biomechanical evaluation should be implemented. For example, Shirota et al. [83] assessed the short-term and long-term performances of the proposed knee exoskeleton.
Journal Pre-proof The result showed that the knee exoskeleton reduced the peak knee flexion angles and peak hip extension angles after a short period of acclimation, but comparatively smaller changes occurred after 1.5 h prolonged walking. For the knee assistive devices which provide knee orthosis in the coronal plane, the reduction of knee adduction moment is common set as the mainly evaluation index. For example, the devices of Pagani et al. [73] can reduce the peak knee adduction moment by about 5-33% (4 -8 deg orthosis) for walking and 11% (8 deg orthosis) for running; the devices of Toriyama et al. [92] can reduce the peak knee adduction moment by about 11.1% for walking. For the muscle activity analysis, sEMG sensors attached to the muscle belly are used to obtain the
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sEMG data, as shown in Fig. 6a and 6b. The root mean square (RMS) of sEMG and the amplitude of normalized sEMG are the common analysis methods. Muscle activity analysis is a new developing evaluation method and can reveal the assistive rationale of devices from the muscular aspect. For the knee assistive devices which provide assistive power in the sagittal plane, reducing the effort of human knee joint is the main target. Because the biceps femoris (BF) provides extension of knee joint mainly, the reduction of RF activity is common set as the main evaluation index. For example, the knee assistive devices of Chaichaowarat et al. [66] and Shan et al. [48] can significantly reduce the RMS of sEMG and amplitude of processed sEMG, respectively. The knee assistive devices of Tung et al. [58], Ranaweera et al. [70] and Rogers et al. [59] can achieve a 30-40% reduction in peak RMS of RF, a 20% reduction in
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average sEMG amplitude of RF, and a 15% reduction in average sEMG amplitude of RF, respectively. Besides, the reductions of other muscles, such as vastus lateralis (VL), vastus medialis (VM) and biceps femoris (BF) are auxiliary evaluation indexes. For example, Sridar et al. [55] presented the proposed knee exosuit can reduce the amplitude of RF, VL and VM by about 32%, 57% and 30%, respectively. Wehbi et al. [3] reported the proposed knee assistive device can reduce the RMS of sEMG by about 44%
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for BF and 40% for VM. For the knee assistive device which provide knee orthosis in the coronal plane, the application of muscle activity analysis is relative rare. Reducing the activity ratio of knee medial muscles to knee lateral muscles is set as the main evaluation index of these assistive devices. For example, the knee assistive device of Pagani et al. [74] can reduce the ratio of medial muscles to lateral muscles
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and VL to VM by about 12.3% and 28.4%, respectively.
Fig. 6 Performance evaluation methods, a frontal view of the optical motion capture system and sEMG acquisition system; b back view of the optical motion capture system and sEMG acquisition system and c cardiopulmonary exercise test system [28]
For the metabolic cost analysis, there are varieties of test methods including cardiopulmonary exercise test, oxygen saturation test, heart rate test, blood pressure test, etc. As a comprehensive evaluation method, the metabolic cost analysis is common used to verify the performance of the assistive
Journal Pre-proof devices which provide assistive power to human knee joint in the sagittal plane. For cardiopulmonary exercise test, the metabolic cost can be acquired by measuring the rate of oxygen consumption and carbon dioxide production through a face mask, as shown in Fig. 6c. And then the total metabolic power can be calculated from the linear expressions 𝑃 = 𝐾𝑂2 𝑉𝑂2 + 𝐾𝐶𝑂2 𝑉𝐶𝑂2
(2)
where 𝐾𝑂2 and 𝐾𝐶𝑂2 are constants, and 𝑉𝑂2 and 𝑉𝐶𝑂2 represent average velocities of oxygen inhalation and carbon dioxide exhalation. The reduction of 𝑃 is common set as the evaluation index. For example, Zhou et al. [28] employed cardiopulmonary exercise test system to evaluate the
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performance of the proposed knee exoskeleton. The results showed that an average 6.21% reduction of metabolic cost when the subjects wearing exoskeleton compared with wearing same weight load. The oxygen saturation test, heart rate test and blood pressure test are all belong to indirect characterization methods of metabolic cost. The oxygen saturation can be acquired by measuring the absorption ratio of visible red and infrared light during the pulse through a finger pulse oximeter and then translating the ratio into the oxygen saturation based on the relationship between them. The heart rate and blood pressure can be acquired by using some portable test devices, such as the devices of Bieber et al. [93], Gillinov et al. [94] and Simard et al. [95]. Besides, some wearable multifunctional devices, such as the devices of Fu et al. [96], Benjamin et al. [97] and Ma et al. [98], can be employed to acquire the three physiological
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indexes at the same time. Reductions of oxygen saturation, heart rate and blood pressure between movements with and without knee assistive devices are available evaluation indexes to verify the effects of the devices. At present, the application of these physiological methods in the performance evaluation
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of knee assistive devices is still rare.
5. Discussion and Conclusions
Knee assistive devices can improve the motor ability and reduce the disease of patients efficiently. A number of researches related with the knee assistive devices have been done during the last decades. And
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many different kinds of knee assistive devices have been developed. However, these developments still have not met the requirement of the knee assistive devices on daily application. And it is hard to find a knee assistive device that can be satisfied the need of all knee injury patients completely. The existed knee assistive devices are still in the laboratory application stage and few of them are truly marketized. For the actuation and power transmission design, traditional electric actuator is still the choice of most knee assistive devices because of the higher accuracy, robustness and efficiency in comparison to pneumatic or hydraulic actuators. As a bionics actuator, SEA is a new trend in applying to knee assistive devices, which can improve the shock tolerance and system stability. But some inherent limitations of
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traditional electric actuator still not be solved, which significantly hinder the development of these devices. The limitations include high weight, bulky, stiffness and bad energy consumption. There are two important trends in the development of the actuator. Firstly, soft actuators including cable and PAM were developed in recent years. Relatively high human-device interaction and wearing comfort are the advantages of these actuator. Because the power source was commonly out of the user’s body, the weight of user’s wearing is relatively low. Therefore, soft actuator was always applied for rehabilitation training currently. Secondly, passive or quasi-passive actuators may represent a good choice for knee assistive devices. Because of no external power source and no/little control system, light weight and volume are its biggest attractive points. However, relatively low human-device coordination is usual challenges for
Journal Pre-proof its application. For the human attachment design, more and more researchers consider its importance for wearing comfort. Based on our review, the improved-lateral-support layout with 4-attachment points is a good choice for the knee assistive device because of its relatively low undesired twisting force and positive force in the human-device interface. For the knee joint alignment design, bionic knee design, such as 4-bar linage and 3-RRP mechanism, has been an important trend which can decrease the misalignment and increase wearing comfort. It is difficult to simulate the real movement of human knee joint by current devices. Therefore, developing new bionic knee joint with light weight, simple structure, self-adaption and adjustment is still a critical challenge in the future.
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For the sensing and control systems design, human biological signals, such as sEMG and sEEG, based control systems will be the future trend because the signal can be detected prior to the corresponding motion. Establishing the relationship between biological signal and joint motion is still the main challenge to improve the accuracy of human motion intention recognition. As a new developing method, the neural network based modeling method will be widely utilized in the future because of relatively high accuracy and low model complexity. Multiple biological coupling analysis is an another new trend. Through coupling the advantages of multiple biological signals, such as EMG, EEG, EOG, etc., the accuracy of human motion intention recognition can be significantly improved. In addition, another critical challenge is to improve the accuracy of signal collecting system and simple the structure of signal
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acquisition process.
For the performance evaluation, many researches pointed out the knee assistive devices they proposed can significantly reduce the power of human knee joint, the effort of muscle and human metabolic cost. But the long-term performance and some integrated performance, such as wearing comfort and safety are not widely evaluated. Healthy person based performance evaluation is the limitations of current
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evaluation process. More patients participating into the evaluation experiment will improve the reliability of evaluation results. In addition, developing advanced and efficiency evaluation methods is still an important research direction.
In the future, developing more efficient, comfortable and portability knee assistive devices will still be an urgent need and a crucial challenge. For recovering the motion capabilities of patients, the assistive
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mechanism of knee assistive devices needs to be optimized. The accuracies of motion intention recognition, control algorithms and human-machine interaction need to be further improved. And a better assistive performance and better practical application need to be further provided.
6. Discussion and Conclusions The research is supported by the Fundamental Research Funds for the Central Universities (Grant No.
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31020190503004) and the 111 Project (Grant No. B13044).
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People And Smart City Innovation 111-116 (2018)
Journal Pre-proof Li Zhang is a Ph.D. candidate of Mechanical Engineering School at Northwestern Polytechnical University in Xi’an, China. He received his M.S. from Southwest Jiaotong University in 2017. His research interests include rehabilitation robots, human-robot interaction, biomechanics and biotribology. Geng Liu is a Professor and supervisor of Ph.D. students and Director of Shaanxi Engineering Laboratory for Transmissions and Controls at Northwestern Polytechnical University (NWPU) in Xi’an, China. He received his M.S. from NWPU and Ph.D. from Xi’an Jiaotong University, respectively in 1987 and 1994. His research interests include mechanical transmissions, electro-mechanical transmissions,
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low noise design of mechanical systems, human-machine interaction robot, tribology, contact mechanics and CAE.
Bing Han is a lecturer of Mechanical Engineering School at Northwestern Polytechnical University (NWPU) in Xi’an, China. He received his M.S. and Ph.D. from NWPU, respectively in 2007 and 2013. His research interests include human-machine coupling design, transmission system configuration design, and mechanical system collaborative design.
Zhe Wang is a M.S. candidate of Mechanical Engineering School at Northwestern Polytechnical University in Xi’an, China. His research interests include the human kinematics and kinetics simulation, wearable exoskeletons and robot control system.
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Han Li is a M.S. candidate of Mechanical Engineering School at Northwestern Polytechnical University in Xi’an, China. His research interests include the mechanical design and wearable exoskeletons design. Yan Jiao is a M.S. candidate of Mechanical Engineering School at Northwestern Polytechnical
Geng Liu
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Li Zhang
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University in Xi’an, China. Her research interests include the knee kinematics and kinetics simulation.
Bing Han
Zhe Wang
Journal Pre-proof Han Li
Yan Jiao
Declaration of interests ▉The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: