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Telerehabilitation for Fingers and Wrist Using a Hand Rehabilitation Support System and Robot Hand
Telerehabilitation for Fingers and Wrist Using a Hand Rehabilitation Support System and Robot Hand Tetsuya Mouri*, Haruhisa Kawasaki*, Takaaki Aoki**, Yutaka Nishimoto**, Satoshi Ito*, and Satoshi Ueki*** * Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan, (e-mail: {mouri, h_kawasa, satoshi}@gifu-u.ac.jp) ** Gifu University School of Medicine, Yanagido 1-1, Gifu 501-1194, Japan, (e-mail: [email protected], [email protected]) *** Virtual System Laboratory, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan, (e-mail: [email protected]) Abstract: Therapists create rehabilitation programs for patients to help them recover from and mitigate their lost abilities. This paper proposes a novel hand telerehabilitation system. The system consists of a hand rehabilitation support system for the patient, an anthropomorphic robot hand for the therapist, and a remote monitoring system for diagnosing the degree of recovery. The therapist applies the force to the robot hand. The force is then transmitted to the patient using the rehabilitation support system. An advanced robot hand and remote monitoring system using virtual reality technology have been developed. Specifications of the robot hand are shown. The safety of the system was demonstrated experimentally for healthy persons. Keywords: Robot Hand, Rehabilitation, Teleoperation, Force Control, Bilateral Control 1. INTRODUCTION The number of disabled people is increasing from year to year due to the aging of the general population. It is the job of a therapist to create rehabilitation programs for patients to aid in their recovery and to mitigate any lost abilities. Robotics researchers approach this situation in three ways: rehabilitation assistance systems, rehabilitation training/education systems, and telerehabilitation systems. Rehabilitation assistance systems (Jack et al. (2001); Bouzit et al. (2002); Loureiro et al. (2003); Mulas et al. (2005); Dovat et al. (2006); Kawasaki et al. (2007)) focus on developing systems that support disabled persons in pursuing a course of rehabilitation on their own to regain lost functions as soon as possible. Effective operation of the system requires appropriate advice from therapists corresponding to the conditions of individual patients. Rehabilitation training/education systems (Makita et al. (2005); Mouri et al. (2008a)) focus on students studying to become therapists. Students learn techniques either in school through textbooks and training with other students or at a training centre in practice with disabled persons. Most therapists tend to be located at the central areas of cities. Disabled persons of a depopulated district may therefore be unable to participate in a rehabilitation program. A telerehabilitation system that allows the patient to participate in a rehabilitation program in a remote location is therefore proposed. Many telerehabilitation systems have been utilized for rehabilitation of upper limb (David et al. (2002); Holden
et al. (2003); Duong et al. (2005); Park et al. (2007)). These systems tend to target the bending/extending of one joint. In the activities of daily life, fingers and hands work together when a patient is eating meals or opening and closing doors. Human fingers and hands have basic functions necessary to the activities of daily life. Compared with other parts of the body, the degree of freedom of the fingers is high. Therefore, the mechanism of movement and control of the system are complicated (Viorel et al. (2000); David et al. (2002); Xiu et al. (2006)). The authors have proposed a finger and hand telerehabilitation system that can greatly help in offsetting the shortage of medical practitioners in under-populated areas (Mouri et al. (2008b)). This paper presents an advanced robot hand for telerehabilitation and a remote monitoring system. Specifications of the robot hand are shown. The effectiveness of the system was evaluated experimentally. The purpose of the experiment was to verify the safety of the system. For this reason, healthy subjects were targeted in this study. 2. HAND TELEREHABILITATION 2.1 Hand Telerehabilitation System The authors proposed the concept of a finger and hand telerehabilitation system illustrated in Fig. 1. The system allows for simultaneous rehabilitation training for the forearm, hand, and fingers. The system connects therapists with patients at distant sites. It provides teaching regarding
θ
Hand rehabilitation support system
Monitor
Network
Fig. 3. Arch form of palm Patient Robot hand
Therapist
Fig. 1. Hand tele-rehabilitation system
determined the required joint torque of this system. Safety feature of the system was considered for patients. In the present study, we evaluated this system from a patient perspective, as shown in Fig. 2. The system was designed based on the normal range of motion of a finger joint. The actuator was selected based on assuring the security of the patient. In this system, the joint angle is measured by encoders on motors. Joint torque except for the wrist joint is estimated by 3-axes force sensors. Based on balancing of moment, we selected elements identical to the joint rotation axis of joints from among moment elements obtained from external products of fixed contact points and force. Details of the hand rehabilitation support system have been shown in Ref. (Kawasaki et al. (2007)). The support system satisfies our demand as listed above. 2.3 Robot Hand
Fig. 2. Hand rehabilitation support system the rehabilitation program and diagnosing the patient’s ability during the recovery process. The system consists of a hand rehabilitation support system, a robot hand, and a remote monitoring system. Therapists use the robot hand imitating a patient hand to carry out the rehabilitation program. The rehabilitation program instructs the patient via the support system. The condition of recovering functions is communicated to therapists based on the robot hand motion and information received via a remote monitoring system. 2.2 Hand Rehabilitation Support System A patient's hand is equipped with a hand rehabilitation support system. The support system moves a patient's hand compulsorily. The following functions are required of the support system by means of cooperative work with a therapist. 1) Range of motion: the support system assists with normal range of motion for the joint angle. 2) Presentation force: the system applies force to the fingers with handicapped fingers. 3) Measuring joint angle and torque: joint angle and torque are measured to diagnose the degree of recovery in the patient. The authors have developed a hand rehabilitation support system, which allows for fine motion of the hand and fingers (Kawasaki et al. (2007)). An experienced therapist
Therapists use the robot hand to carry out the rehabilitation program. The robot hand imitates the hand of the disabled person. The following function is required of the robot in cooperative work with a therapist. 1) Humanlike: the robot hand must have geometrical and kinematical features as close as possible to a human hand. 2) Imitating the disabled person: it must be possible to imitate handicaps such as joint contracture. 3) Measuring joint angle and torque: joint angle and torque are estimated to transmit to outlying support system. The authors have developed a robot hand for rehabilitation training and education (Mouri et al. (2008a)). The appearance of the robot hand is very close to that of a human. Problems have occurred among the robot hands developed until now, including a) an uncomfortable size and form of the palm, and b) the noise of the potentiometer. It is difficult to reduce the full length of the robot hand. 4 fingers are not arranged at a plane, but it is considered as the palm made into arch form. This arrangement reduces the sense of incongruity of the form of a hand. The joint angle is measured by the encoder so as to improve the measurements. The system examined in the present study, however, provides a solution to these problems. The new robot hand was developed in order to improve upon its mechanism in this paper. The arch form of the palm changes with finger postures. The arch form of the palm of the human of three typical states is considered. The three states are: 1) extending the fingers; 2) relaxing the fingers; and 3) bending the fingers. The arch
1st ENC 1st motor
1st joint
worm gear 4th ENC 4th motor 3rd motor
2nd motor 2nd ENC 57.6
194.8 14
57.5 1st link
Robot Hand
Support System
3rd POT 37.8 2nd link
27.9 3rd link
Φ14
2nd joint
3rd joint
4th joint
(a) Mechanism of fingers
Fig. 5. Remote monitoring system 1.22 times the length of the Japanese human hand from elbow to fingertip. Experienced therapists determined the required joint torque of the robot hand to imitate disabled persons. Distributed tactile sensors, which can detect the contact position and force, can be mounted on the surfaces of the fingers and palm. Joint torque is estimated by the contact position and force. Joint torque is selected as moment element identical to the joint rotation axis of joints from among moment elements. 2.4 Remote Monitoring System
(b) Appearance Fig. 4. Developed robot hand angle of two straight lines, θ , which connects the finger root of an index finger, the middle finger and a ring finger, and a little finger at the dorsum of the hand is measured (Fig. 3). A subject is taken as 10 adult men. The measurement results showed average values of 33.1, 28.7, and 25.5 [deg], and standard deviations of 5.9, 5.1, and 4.0 [deg], respectively, for the extending state, the relaxing state, and the bending state. The therapist commented that the arch angle generally decreased in order of the bending state, the relaxing state, and the extending state. The comments of the therapist and the experimental results are in agreement. In hand rehabilitation, the relaxed state is considered to be the fundamental posture. The design value of the arch of the palm is set as 28.7 [deg]. Figure 4 shows the mechanical structure of the fingers and the appearance of the developed robot hand. The link lengths of the fingers and forearm are similar to their human counterparts as follows, based on statistical data (Research Institute of Human Engineering for Quality Life (1996)). The robot hand consists of a hand and a forearm and is driven by built-in servomotors with magnetic encoders. All five fingers have a common mechanism. Each finger has 4 joints, and the forearm has 2 joints. The joint angle can be measured by the incremental encoders mounted on the DC motors and a potentiometer mounted on 3rd joint. The previous robot hand (Mouri et al. (2008a)) uses a potentiometer for measuring the finger's joint angle. The resolution of the potentiometer is low, and its measurement is contaminated with noise. For this reason, we use a potentiometer as little as possible. Due to space considerations, the 3rd joint of the fingers uses a potentiometer. The hand has 22 joints with 22 DOF, and is
A remote monitoring system is required for the therapist to assess the degree of recovery for the patient. As such, the system must satisfy the requirements listed below. 1) Display of hand condition: the system must provide information regarding the patient condition and rehabilitation motion for the therapist. 2) Recording data: the joint angle, angular velocity, and torque must be recorded to determine the degree of recovery. We developed the remote monitoring system shown in Fig. 5. The system can display fingers and the hand using a virtual reality technique and record such data as the joint angle, angular velocity, and torque of both the hand rehabilitation support system and the robot hand. Note that the robot hand and the support system use a right hand and a left hand under many situations, respectively. Adduction and abduction allow for symmetrical operation. 3. CONTROL SYSTEM In order to demonstrate the effectiveness of the telerehabilitation system, we constructed a master slave system, as shown in Fig. 6. The robot hand and the hand rehabilitation support system are the master and slave, respectively. A therapist makes a rehabilitation motion by means of the robot hand. The rehabilitation motion is then translated to the joint angle. The joint angle is transmitted to the patient through the hand rehabilitation support system. The joint torque of the patient, which is measured by force sensors of the hand rehabilitation support system, is fed back to the therapist through the robot hand. Measurement information for the support system and robot hand are sent to the remote monitoring system through TCP/IP.
Torque
Torque Hand rehabilitation support
TCP/IP Controller
TCP/IP Monitor
Controller
Robot hand (Therapist)
system (Patient)
Joint angle
Joint angle
Fig. 6. Control system Control input of the hand rehabilitation support system by the position control is given by u s = k sP (θ h − θ s ) − k sD θ&s ,
(1)
where θh and θ s are the joint angles of the robot hand and the hand rehabilitation support system, respectively. k sP is the position feedback gain, and k sD is the velocity feedback gain. Each finger joint of the hand rehabilitation support system is controlled to independently follow each finger joint of the robot hand. The control input of the robot hand by the force control is given by
uh = k hP (τ s − τ h ) + k hI ∫ (τ s − τ h )dt + f r (θ h ) ,
(2)
where τ h is the joint torque estimated by the distributed tactile sensor of the robot hand, τ s is the joint torque measured by the hand rehabilitation support system, k hP is the torque feedback gain, k hI is the torque integral feedback
gain, and f r (•) is the friction compensation term. Each finger joint of the robot hand is controlled to independently follow the joint torque τ s of the support system. The joint torque is estimated by 3-axes force sensors and distributed tactile sensors. However, there is no force sensor in the wrist of the support system. The desired joint torque of the wrist of the robot hand is controlled as zero. Equations (1) and (2) are traditional bilateral controllers for teleoperation. In general, a time delay in communications must be considered in such a system. This paper ignores the time delay because its aim is to confirm the performance and safety of the hand telerehabilitation system. Our system is installed in a single room. 4. EXPERIMENTS We constructed the telerehabilitation system. The hand rehabilitation support system and the robot hand are controlled by position control and force control, respectively. Two controllers are connected through the remote monitoring system. The operating system is a real-time OS (ART-Linux) in both the support systems and the robot hand. The controller has a safety circuit and emergency stop switches for out of control. The sampling time of the control is 1 [ms]. The remote monitoring system saves the data for the support system and the robot hand at intervals of 15 [ms]. To evaluate
the possibility of a hand telerehabilitation system, we make experiment in characteristic evaluation and psychological evaluation. In experiments, the feedback gain matrices of fingers were set as k sP = diag {10.0, 50.0, 50.0} , k sD = diag {1.0, 1.0, 1.0} , k hP = diag{0.01, 0.05, 0.20, 0.05} , k hI = diag{0.005, 0.01, 0.1, 0.1} . The feedback gain matrices of wrist were set as k sP = diag {20.0, 10.0} , k sD = diag {1.0, 1.0} , k hP = diag {0.05, 0.02} , k hI = diag {0.001, 0.001} .
4.1 Teaching Rehabilitation Motion Experiments were carried out in order to confirm whether the motion carried out by the robot hand was transmitted to the hand rehabilitation support system. The reference joint angle of the support system is generated by a robot hand using subject A who plays a role as a therapist. The hand of the subject B, who plays a role as a patient, is equipped with the support system. The subject A is asked to open and close the robot hand. The subject B is requested to relax the hand so as to follow the movement of the support system. Responses of the MP joint of the middle finger and palmar flexion/dorsiflexion of wrist are shown in Fig. 7 (a) and (b), respectively. Figure 7 (c) shows the joint torque response of the support system and the robot hand at the middle finger. Its input to the human finger is not very large. The joint torque was contaminated with noise, although the low pass filter was used. These figures show that the human finger driven by the support system follows the robot hand motion very well.
4.2 Psychological Evaluation A psychological evaluation was carried out for subjects using the proposed telerehabilitation system. The purpose of the evaluation was to ensure the safety of those using the proposed system. In the present study, we targeted healthy persons. One subject would make the rehabilitation motion with the robot hand. The rehabilitation motion would then teach the other subject through the hand rehabilitation support system. In the experiment, we asked subjects whether the tension of the fingers and hands was relieved. The following motions were performed in the experiments: 1) Fingers adduction/abduction 2) Fingers anteflexion/retroflexion
Table 1. Psychological evaluation
Angle (deg)
90
30 0
suppot adduction/abduction suppot flexion/extension robot adduction/abduction robot flexion/extension
-30 145
150
155
160
(a) Finger joint angle
60 Angle (deg)
Robot hand
Motion
2.5 ± 0.8
2.3 ± 0.5
Response
2.7 ± 1.0
2.7 ± 0.8
3.3 ± 1.5
2.8 ± 1.3
Thumb Force
Time (sec)
support system
robot hand
Finger
40 20 0 -20 20
40
Time (sec)
60
80
Wrist
(b) Wrist joint angle 0.10
Torque (Nm)
Support system
60
suppot adduction/abduction suppot flexion/extension robot adduction/abduction robot flexion/extension
0.05
Safety
0.00 -0.05 -0.10 145
150
155
160
Time (sec) (c) Finger joint torque Fig. 7. Experimental result 3) Making resistance 4) Hand pronation/spination 5) Palmar flexion/dorsiflexion There were 6 adult male subjects, all of whom were healthy. All subjects were beginners at operating the robot hand and the hand support system. After the experiments were performed, a questionnaire was administered with a five-step evaluation method regarding the supporting system and the thumb of the robot hand from the perspective of tracking trajectory, response, force assistance/presentation, operability, tactile feeling, the intuitive sense of security before use, and the sense of security in use. Higher scores correspond to
Manipurability
2.0 ± 0.0
Hand feeling
2.0 ± 0.9
Motion
2.3 ± 0.8
2.5 ± 0.5
Response
2.5 ± 1.0
2.7 ± 0.8
Force
3.2 ± 1.5
3.3 ± 0.8
Manipurability
2.5 ± 0.5
Hand feeling
2.2 ± 0.8
Motion
3.7 ± 1.0
3.2 ± 1.0
Response
4.0 ± 1.3
3.2 ± 1.3
Force
4.5 ± 0.5
Manipurability
3.2 ± 1.2
Hand feeling
2.3 ± 0.5
Secure feeling
1.8 ± 0.8
2.3 ± 0.5
Safety measure
3.5 ± 1.0
3.8 ± 1.0
4.0 ± 0.6
2.3 ± 1.0
3.5 ± 1.0
2.5 ± 0.8
Awful feeling before experiment Awful feeling while experimenting
positive evaluations. Table 1 shows the average values and standard deviations for the experimental results. The hand rehabilitation support system has no force sensors for measuring the joint torque of the wrist. The robot hand is controlled by force control which sets the desired torque of the wrist as zero. Therefore, force evaluation of the wrist was not evaluated. The hand rehabilitation support system has high evaluation because of the system’s well-tried safety (Kawasaki et al. (2007)). Because the force sensors are unstable, the system runs out of control at times. Subjects feel something is wrong with the thumb motion because the thumb mechanism of the support system is different from that of the robot hand. Because subjects cannot get information regarding the finger/wrist training, the subjects become frightened. As such, the system would benefit from a navigation system to guide the rehabilitation motion. The robot hand has high evaluation at the wrist and safety. Fingers and thumb, however, have a low evaluation. Motion,
response, and force have a low evaluation because the joint torque measurement is low resolution and is contaminated with noise. Tactile impressions of artificial skin are not sufficient. We will therefore attempt to address this problem in the robot. The experimental results successfully show the telerehabilitation system, as described above. Based on these evaluation results, we also consider how the system could achieve objectives for the telerehabilitation system. We ignore the communication delay. The control method must be improved in the future taking into consideration the time delay. 5. CONCLUSIONS This paper has proposed concepts for a finger and hand telerehabilitation system. The system consists of a hand rehabilitation support system, a robot hand, and a remote monitoring system. The advanced robot hand and the remote monitoring system have been developed, and specifications of the robot hand have been given. To evaluate the effectiveness of the system the experiment was carried out with healthy persons. Human hand motion driven by the support system was found to follow the motion of the robot hand very well. The joint torque input to the human finger was not found to be very large. The system was shown to operate safely. In the future, we will further improve the control method taking into consideration the communication delay and develop the navigation system so that it provides information to the patient. ACKNOWLEDGMENTS This research was aided in part by a “Development of Rehabilitation Assistance Robots and Applications” of NEDO's Human Assistance Robot Application Technology Development Program and Japan Society for the Promotion of Science (JSPS), Grant-in-Aid for Scientific Research (B) (18760183). The authors would like to express our thanks to the Rehabilitation Robot Hand Group for their support and offer special thanks to Mr. Shimomura, Mr. Ishigure, and Mr. Mizumoto for assisting in the experiments. REFERENCES Bouzit, M., Burdea, G., Popescu, G., and Boian, R. (2002). The Rutgers Master II-New Design Force-Feedback Glove. IEEE/ASME Trans. on Mechatronics, Vol. 7, No. 2, pp. 256-263. David, J. R., Clifton, T. P., Jeff, A. N., and Christopher, C. P. (2002). Web-Based Telerehabilitation for the Upper Extremity After Stroke. IEEE Trans. on Neural Systems and Rehabilitation Engineering, Vol. 10, No. 2, pp. 102108. Dovat, L., Lambercy, O., Ruffieux, Y., Chapuis, D., Gassert, R., Bleuler, H., Teo, CL. and Burdet, E. (2006). A Haptic Knob for Rehabilitation of Stroke Patients. Proc. of The 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 977-982.
Duong, M. D., Teraoka, C., Imamura, T., Miyoshi, T., and Terashima, K. (2005). Master-Slave System with Teleoperation for Rehabilitation. Proc. of the 16th IFAC World Congress. Holden, M. K., Dyar, T., Schwamm, L., and Bizzi, E. (2003). Home-based telerehabilitation using a virtual environment system. 2nd International Workshop on Virtual Rehabilitation, pp. 4–12. Jack, D., Boian, R., Merians, A. S., Tremaine, M., Burdea, G. C., Adamovich, S. V., Recce, M. and Poinzne, H. (2001). Virtual Reality-Enhanced Stroke Rehabilitation. IEEE Trans. on Neural Systems and Rehabilitation Engineerging, Vol. 8, No. 3 pp. 308-318. Kawasaki, H., Ito, S., Ishigure, Y., Nishimoto, Y., Aoki, T., Mouri, T., Sakaeda, H. and Abe, M. (2007). Development of Hand Motion Assist Robot for Rehabilitation Therapy by Patient Self-Motion Control. Proc. of IEEE 9th International Conference on Rehabilitation Robotics, pp. 234-240. Loureiro, R. C. V., Collin, C. F., and Harwin, W. S. (2003). Robot Aided Therapy: Challenges Ahead for Upper Limb Stroke Rehabilitation. Proc. of the 13th International Conference on Artificial Reality and Telexistance, pp. 215-221. Makita, S., Uraoka, S., Masutani, Y., and Nishimura, A. (2005). Development of Leg Robot for Training of Physical Therapist Students. Proc. of 23st annual conference of Robotics Society of Japan, 1J34 (in Japanese). Mouri, T., Kawasaki, H., Nishimoto, Y., Aoki, T., Ishigure, Y., and Tanahashi, M. (2008a). Robot Hand Imitating Disabled Person for Education/Training of Rehabilitation. Journal of Robotics and Mechatronics, Vol.20, No.2, pp. 280-288. Mouri T., Kawasaki H., Aoki T., Nishimoto Y., Ito S., and Ueki S. (2008b). Tele-rehabilitation for Fingers and Hand. 15th International Conference on Automatic Control, pp.965-967. Mulas, M., Folgheraiter, M., and Gini, G.. (2005). An EMGControlled Exoskeleton for Hand Rehabilitation. 9th International Conference on Rehabilitation Robotics, pp. 371-374. Park, H. S., Wu, Y. N., Ren, Y., and Zhang, L. Q. (2007). A Tele-Assessment System for Evaluating Elbow Spasticity in Patients with Neurological Impairments. Proc. of the 2007 IEEE 10th International Conference on Rehabilitation Robotics, pp. 917-922. Research Institute of Human Engineering for Quality Life. (1996) Human Body dimensions Data for Ergonomic Design. Japan Publication Service Co., Japan. Viorel, G. P., Grigore, C. B., Mourad, B., and Vincent, R. H. (2000). A Virtual-Reality-Based Telerehabilitation System with Force Feedback. IEEE Trans. on Information Technology In Biomedicine, Vol. 4, No. 1, pp. 45-51. Xiu, Z., Kitagawa, A., Tsukagoshi, H., Liu, C., Ido, M., and Wu, P. (2006). Internet-based Tele-rehabilitation System -Bilateral Tele-control with Variable Time Delay. Proc. of The 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5208-5213.
et al. (2003); Duong et al. (2005); Park et al. (2007)). These systems tend to target the bending/extending of one joint. In the activities of daily life, fingers and hands work together when a patient is eating meals or opening and closing doors. Human fingers and hands have basic functions necessary to the activities of daily life. Compared with other parts of the body, the degree of freedom of the fingers is high. Therefore, the mechanism of movement and control of the system are complicated (Viorel et al. (2000); David et al. (2002); Xiu et al. (2006)). The authors have proposed a finger and hand telerehabilitation system that can greatly help in offsetting the shortage of medical practitioners in under-populated areas (Mouri et al. (2008b)). This paper presents an advanced robot hand for telerehabilitation and a remote monitoring system. Specifications of the robot hand are shown. The effectiveness of the system was evaluated experimentally. The purpose of the experiment was to verify the safety of the system. For this reason, healthy subjects were targeted in this study. 2. HAND TELEREHABILITATION 2.1 Hand Telerehabilitation System The authors proposed the concept of a finger and hand telerehabilitation system illustrated in Fig. 1. The system allows for simultaneous rehabilitation training for the forearm, hand, and fingers. The system connects therapists with patients at distant sites. It provides teaching regarding
θ
Hand rehabilitation support system
Monitor
Network
Fig. 3. Arch form of palm Patient Robot hand
Therapist
Fig. 1. Hand tele-rehabilitation system
determined the required joint torque of this system. Safety feature of the system was considered for patients. In the present study, we evaluated this system from a patient perspective, as shown in Fig. 2. The system was designed based on the normal range of motion of a finger joint. The actuator was selected based on assuring the security of the patient. In this system, the joint angle is measured by encoders on motors. Joint torque except for the wrist joint is estimated by 3-axes force sensors. Based on balancing of moment, we selected elements identical to the joint rotation axis of joints from among moment elements obtained from external products of fixed contact points and force. Details of the hand rehabilitation support system have been shown in Ref. (Kawasaki et al. (2007)). The support system satisfies our demand as listed above. 2.3 Robot Hand
Fig. 2. Hand rehabilitation support system the rehabilitation program and diagnosing the patient’s ability during the recovery process. The system consists of a hand rehabilitation support system, a robot hand, and a remote monitoring system. Therapists use the robot hand imitating a patient hand to carry out the rehabilitation program. The rehabilitation program instructs the patient via the support system. The condition of recovering functions is communicated to therapists based on the robot hand motion and information received via a remote monitoring system. 2.2 Hand Rehabilitation Support System A patient's hand is equipped with a hand rehabilitation support system. The support system moves a patient's hand compulsorily. The following functions are required of the support system by means of cooperative work with a therapist. 1) Range of motion: the support system assists with normal range of motion for the joint angle. 2) Presentation force: the system applies force to the fingers with handicapped fingers. 3) Measuring joint angle and torque: joint angle and torque are measured to diagnose the degree of recovery in the patient. The authors have developed a hand rehabilitation support system, which allows for fine motion of the hand and fingers (Kawasaki et al. (2007)). An experienced therapist
Therapists use the robot hand to carry out the rehabilitation program. The robot hand imitates the hand of the disabled person. The following function is required of the robot in cooperative work with a therapist. 1) Humanlike: the robot hand must have geometrical and kinematical features as close as possible to a human hand. 2) Imitating the disabled person: it must be possible to imitate handicaps such as joint contracture. 3) Measuring joint angle and torque: joint angle and torque are estimated to transmit to outlying support system. The authors have developed a robot hand for rehabilitation training and education (Mouri et al. (2008a)). The appearance of the robot hand is very close to that of a human. Problems have occurred among the robot hands developed until now, including a) an uncomfortable size and form of the palm, and b) the noise of the potentiometer. It is difficult to reduce the full length of the robot hand. 4 fingers are not arranged at a plane, but it is considered as the palm made into arch form. This arrangement reduces the sense of incongruity of the form of a hand. The joint angle is measured by the encoder so as to improve the measurements. The system examined in the present study, however, provides a solution to these problems. The new robot hand was developed in order to improve upon its mechanism in this paper. The arch form of the palm changes with finger postures. The arch form of the palm of the human of three typical states is considered. The three states are: 1) extending the fingers; 2) relaxing the fingers; and 3) bending the fingers. The arch
1st ENC 1st motor
1st joint
worm gear 4th ENC 4th motor 3rd motor
2nd motor 2nd ENC 57.6
194.8 14
57.5 1st link
Robot Hand
Support System
3rd POT 37.8 2nd link
27.9 3rd link
Φ14
2nd joint
3rd joint
4th joint
(a) Mechanism of fingers
Fig. 5. Remote monitoring system 1.22 times the length of the Japanese human hand from elbow to fingertip. Experienced therapists determined the required joint torque of the robot hand to imitate disabled persons. Distributed tactile sensors, which can detect the contact position and force, can be mounted on the surfaces of the fingers and palm. Joint torque is estimated by the contact position and force. Joint torque is selected as moment element identical to the joint rotation axis of joints from among moment elements. 2.4 Remote Monitoring System
(b) Appearance Fig. 4. Developed robot hand angle of two straight lines, θ , which connects the finger root of an index finger, the middle finger and a ring finger, and a little finger at the dorsum of the hand is measured (Fig. 3). A subject is taken as 10 adult men. The measurement results showed average values of 33.1, 28.7, and 25.5 [deg], and standard deviations of 5.9, 5.1, and 4.0 [deg], respectively, for the extending state, the relaxing state, and the bending state. The therapist commented that the arch angle generally decreased in order of the bending state, the relaxing state, and the extending state. The comments of the therapist and the experimental results are in agreement. In hand rehabilitation, the relaxed state is considered to be the fundamental posture. The design value of the arch of the palm is set as 28.7 [deg]. Figure 4 shows the mechanical structure of the fingers and the appearance of the developed robot hand. The link lengths of the fingers and forearm are similar to their human counterparts as follows, based on statistical data (Research Institute of Human Engineering for Quality Life (1996)). The robot hand consists of a hand and a forearm and is driven by built-in servomotors with magnetic encoders. All five fingers have a common mechanism. Each finger has 4 joints, and the forearm has 2 joints. The joint angle can be measured by the incremental encoders mounted on the DC motors and a potentiometer mounted on 3rd joint. The previous robot hand (Mouri et al. (2008a)) uses a potentiometer for measuring the finger's joint angle. The resolution of the potentiometer is low, and its measurement is contaminated with noise. For this reason, we use a potentiometer as little as possible. Due to space considerations, the 3rd joint of the fingers uses a potentiometer. The hand has 22 joints with 22 DOF, and is
A remote monitoring system is required for the therapist to assess the degree of recovery for the patient. As such, the system must satisfy the requirements listed below. 1) Display of hand condition: the system must provide information regarding the patient condition and rehabilitation motion for the therapist. 2) Recording data: the joint angle, angular velocity, and torque must be recorded to determine the degree of recovery. We developed the remote monitoring system shown in Fig. 5. The system can display fingers and the hand using a virtual reality technique and record such data as the joint angle, angular velocity, and torque of both the hand rehabilitation support system and the robot hand. Note that the robot hand and the support system use a right hand and a left hand under many situations, respectively. Adduction and abduction allow for symmetrical operation. 3. CONTROL SYSTEM In order to demonstrate the effectiveness of the telerehabilitation system, we constructed a master slave system, as shown in Fig. 6. The robot hand and the hand rehabilitation support system are the master and slave, respectively. A therapist makes a rehabilitation motion by means of the robot hand. The rehabilitation motion is then translated to the joint angle. The joint angle is transmitted to the patient through the hand rehabilitation support system. The joint torque of the patient, which is measured by force sensors of the hand rehabilitation support system, is fed back to the therapist through the robot hand. Measurement information for the support system and robot hand are sent to the remote monitoring system through TCP/IP.
Torque
Torque Hand rehabilitation support
TCP/IP Controller
TCP/IP Monitor
Controller
Robot hand (Therapist)
system (Patient)
Joint angle
Joint angle
Fig. 6. Control system Control input of the hand rehabilitation support system by the position control is given by u s = k sP (θ h − θ s ) − k sD θ&s ,
(1)
where θh and θ s are the joint angles of the robot hand and the hand rehabilitation support system, respectively. k sP is the position feedback gain, and k sD is the velocity feedback gain. Each finger joint of the hand rehabilitation support system is controlled to independently follow each finger joint of the robot hand. The control input of the robot hand by the force control is given by
uh = k hP (τ s − τ h ) + k hI ∫ (τ s − τ h )dt + f r (θ h ) ,
(2)
where τ h is the joint torque estimated by the distributed tactile sensor of the robot hand, τ s is the joint torque measured by the hand rehabilitation support system, k hP is the torque feedback gain, k hI is the torque integral feedback
gain, and f r (•) is the friction compensation term. Each finger joint of the robot hand is controlled to independently follow the joint torque τ s of the support system. The joint torque is estimated by 3-axes force sensors and distributed tactile sensors. However, there is no force sensor in the wrist of the support system. The desired joint torque of the wrist of the robot hand is controlled as zero. Equations (1) and (2) are traditional bilateral controllers for teleoperation. In general, a time delay in communications must be considered in such a system. This paper ignores the time delay because its aim is to confirm the performance and safety of the hand telerehabilitation system. Our system is installed in a single room. 4. EXPERIMENTS We constructed the telerehabilitation system. The hand rehabilitation support system and the robot hand are controlled by position control and force control, respectively. Two controllers are connected through the remote monitoring system. The operating system is a real-time OS (ART-Linux) in both the support systems and the robot hand. The controller has a safety circuit and emergency stop switches for out of control. The sampling time of the control is 1 [ms]. The remote monitoring system saves the data for the support system and the robot hand at intervals of 15 [ms]. To evaluate
the possibility of a hand telerehabilitation system, we make experiment in characteristic evaluation and psychological evaluation. In experiments, the feedback gain matrices of fingers were set as k sP = diag {10.0, 50.0, 50.0} , k sD = diag {1.0, 1.0, 1.0} , k hP = diag{0.01, 0.05, 0.20, 0.05} , k hI = diag{0.005, 0.01, 0.1, 0.1} . The feedback gain matrices of wrist were set as k sP = diag {20.0, 10.0} , k sD = diag {1.0, 1.0} , k hP = diag {0.05, 0.02} , k hI = diag {0.001, 0.001} .
4.1 Teaching Rehabilitation Motion Experiments were carried out in order to confirm whether the motion carried out by the robot hand was transmitted to the hand rehabilitation support system. The reference joint angle of the support system is generated by a robot hand using subject A who plays a role as a therapist. The hand of the subject B, who plays a role as a patient, is equipped with the support system. The subject A is asked to open and close the robot hand. The subject B is requested to relax the hand so as to follow the movement of the support system. Responses of the MP joint of the middle finger and palmar flexion/dorsiflexion of wrist are shown in Fig. 7 (a) and (b), respectively. Figure 7 (c) shows the joint torque response of the support system and the robot hand at the middle finger. Its input to the human finger is not very large. The joint torque was contaminated with noise, although the low pass filter was used. These figures show that the human finger driven by the support system follows the robot hand motion very well.
4.2 Psychological Evaluation A psychological evaluation was carried out for subjects using the proposed telerehabilitation system. The purpose of the evaluation was to ensure the safety of those using the proposed system. In the present study, we targeted healthy persons. One subject would make the rehabilitation motion with the robot hand. The rehabilitation motion would then teach the other subject through the hand rehabilitation support system. In the experiment, we asked subjects whether the tension of the fingers and hands was relieved. The following motions were performed in the experiments: 1) Fingers adduction/abduction 2) Fingers anteflexion/retroflexion
Table 1. Psychological evaluation
Angle (deg)
90
30 0
suppot adduction/abduction suppot flexion/extension robot adduction/abduction robot flexion/extension
-30 145
150
155
160
(a) Finger joint angle
60 Angle (deg)
Robot hand
Motion
2.5 ± 0.8
2.3 ± 0.5
Response
2.7 ± 1.0
2.7 ± 0.8
3.3 ± 1.5
2.8 ± 1.3
Thumb Force
Time (sec)
support system
robot hand
Finger
40 20 0 -20 20
40
Time (sec)
60
80
Wrist
(b) Wrist joint angle 0.10
Torque (Nm)
Support system
60
suppot adduction/abduction suppot flexion/extension robot adduction/abduction robot flexion/extension
0.05
Safety
0.00 -0.05 -0.10 145
150
155
160
Time (sec) (c) Finger joint torque Fig. 7. Experimental result 3) Making resistance 4) Hand pronation/spination 5) Palmar flexion/dorsiflexion There were 6 adult male subjects, all of whom were healthy. All subjects were beginners at operating the robot hand and the hand support system. After the experiments were performed, a questionnaire was administered with a five-step evaluation method regarding the supporting system and the thumb of the robot hand from the perspective of tracking trajectory, response, force assistance/presentation, operability, tactile feeling, the intuitive sense of security before use, and the sense of security in use. Higher scores correspond to
Manipurability
2.0 ± 0.0
Hand feeling
2.0 ± 0.9
Motion
2.3 ± 0.8
2.5 ± 0.5
Response
2.5 ± 1.0
2.7 ± 0.8
Force
3.2 ± 1.5
3.3 ± 0.8
Manipurability
2.5 ± 0.5
Hand feeling
2.2 ± 0.8
Motion
3.7 ± 1.0
3.2 ± 1.0
Response
4.0 ± 1.3
3.2 ± 1.3
Force
4.5 ± 0.5
Manipurability
3.2 ± 1.2
Hand feeling
2.3 ± 0.5
Secure feeling
1.8 ± 0.8
2.3 ± 0.5
Safety measure
3.5 ± 1.0
3.8 ± 1.0
4.0 ± 0.6
2.3 ± 1.0
3.5 ± 1.0
2.5 ± 0.8
Awful feeling before experiment Awful feeling while experimenting
positive evaluations. Table 1 shows the average values and standard deviations for the experimental results. The hand rehabilitation support system has no force sensors for measuring the joint torque of the wrist. The robot hand is controlled by force control which sets the desired torque of the wrist as zero. Therefore, force evaluation of the wrist was not evaluated. The hand rehabilitation support system has high evaluation because of the system’s well-tried safety (Kawasaki et al. (2007)). Because the force sensors are unstable, the system runs out of control at times. Subjects feel something is wrong with the thumb motion because the thumb mechanism of the support system is different from that of the robot hand. Because subjects cannot get information regarding the finger/wrist training, the subjects become frightened. As such, the system would benefit from a navigation system to guide the rehabilitation motion. The robot hand has high evaluation at the wrist and safety. Fingers and thumb, however, have a low evaluation. Motion,
response, and force have a low evaluation because the joint torque measurement is low resolution and is contaminated with noise. Tactile impressions of artificial skin are not sufficient. We will therefore attempt to address this problem in the robot. The experimental results successfully show the telerehabilitation system, as described above. Based on these evaluation results, we also consider how the system could achieve objectives for the telerehabilitation system. We ignore the communication delay. The control method must be improved in the future taking into consideration the time delay. 5. CONCLUSIONS This paper has proposed concepts for a finger and hand telerehabilitation system. The system consists of a hand rehabilitation support system, a robot hand, and a remote monitoring system. The advanced robot hand and the remote monitoring system have been developed, and specifications of the robot hand have been given. To evaluate the effectiveness of the system the experiment was carried out with healthy persons. Human hand motion driven by the support system was found to follow the motion of the robot hand very well. The joint torque input to the human finger was not found to be very large. The system was shown to operate safely. In the future, we will further improve the control method taking into consideration the communication delay and develop the navigation system so that it provides information to the patient. ACKNOWLEDGMENTS This research was aided in part by a “Development of Rehabilitation Assistance Robots and Applications” of NEDO's Human Assistance Robot Application Technology Development Program and Japan Society for the Promotion of Science (JSPS), Grant-in-Aid for Scientific Research (B) (18760183). The authors would like to express our thanks to the Rehabilitation Robot Hand Group for their support and offer special thanks to Mr. Shimomura, Mr. Ishigure, and Mr. Mizumoto for assisting in the experiments. REFERENCES Bouzit, M., Burdea, G., Popescu, G., and Boian, R. (2002). The Rutgers Master II-New Design Force-Feedback Glove. IEEE/ASME Trans. on Mechatronics, Vol. 7, No. 2, pp. 256-263. David, J. R., Clifton, T. P., Jeff, A. N., and Christopher, C. P. (2002). Web-Based Telerehabilitation for the Upper Extremity After Stroke. IEEE Trans. on Neural Systems and Rehabilitation Engineering, Vol. 10, No. 2, pp. 102108. Dovat, L., Lambercy, O., Ruffieux, Y., Chapuis, D., Gassert, R., Bleuler, H., Teo, CL. and Burdet, E. (2006). A Haptic Knob for Rehabilitation of Stroke Patients. Proc. of The 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 977-982.
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