- Email: [email protected]
An Experimental Neuroelectric Prosthetic Arm
IFAC
Copyright 0 IFAC Mechatronic Systems, California, USA, 2002
c:
0
[>
Publications www.eIsevier.comllocate/ifac
AN EXPERIMENTAL NEUROELECTRIC PROSTHETIC ARM
Mark B. Colton
Sanford G. Meek
University of Utah Department of Mechanical Engineering 50 S. Central Campus Dr. Rm. 2102 Salt Lake City. Utah 841 12
Abstract: In this paper, a prosthetic arm developed for use in neuroelectric control experiments is described. The motivation for neuroelectrically-controlled prostheses is presented, as wcll as an overview of the design features required for effective and natural control. The development of the prosthetic hardware and control system are described, with emphasis on the sensors used in the control laws and for sensory feedback. A method of using the frequency of efferent nerve pulses as command inputs is also described. Preliminary experimental results are presented. Copyright © 2002 IFAC Keywords : Biomedical systems, Mechanical Multivariable control, Position control, Sensors
I. INTRODUCTION
manipulators,
Command
signals,
1.1 Background Before examining neuroelectric control in greater detail, it is useful to present some background, including a look at the current state of the art in controlling prosthetic arms. By doing so, the motivation behind developing a neuroelectricallycontrolled artificial arm will be come apparent.
For the past 50 years much research has gone into improving externally powered prosthetic arms. The approaches have been varied, but in each case the objective has been the same: restoration of the function lost through amputation or congenital defect. This paper describes the development of an experimental prosthetic arm that will allow researchers to conduct experiments in neuroelectric control, i.e., controlling the prosthesis using nerve signals. Experiments using this system will allow researchers to study the nature of signals contained in peripheral nerves, better understand the mechanisms of sensory feedback, and define requirements for future neuroelectric prosthetic arms that will result in more natural control for amputees.
Myoelectric Control. Currently, the most popular method of controlling a prosthetic limb is myoelectric control, in which electromyograph (EMG) signals from the muscles in the remnant limbs are used as command inputs to the artificial arm. Electrodes are generally affixed to the surface of the skin, placed directly over the muscles from which the EMG signals are to be recruited. By flexing the muscles in the remnant limb, the amputee is able to control the joints of the artificial arm. This is how commercial
313
myoelectric arms function (Jacobsen, et al., 1975; lacobsen, et al., 1982).
Obstacles. Using nerves to control and receive sensory feedback from a prosthetic arm is not a new idea, but two key obstacles have hindered its implementation (DeLuca, 1975; lacobsen, et al., 1982): neural electrodes and computational power. These obstacles have been overcome, to a large extent, with recent advances. First, neural electrodes with suitable mechanical and electrical properties, and signal conditioning equipment have been developed at the University of Utah (Yoshida and Horch, 1993), making it possible for researchers to selectively stimulate and record from individual nerve bundles (Y oshida and Horch, 1996). Second, fast and affordable microprocessors and specialized digital signal proc~ssors (DSPs) are readily available, making the computat,?nal aspect of the neurocontrol problem less of an l~ue. Signals from multiple nerves can be rcad and processed in real-time, while simultaneously controlling an artificial arm.
The Ideal. Although considerable success has been achieved with myoelectrically controlled artificial arms, their effectiveness has been limited because they do not provide the type of natural control that was mentioned previously. An artificial arm that would achieve this ideal level of natural of control is one that would meet the following requirements: l.
2. 3.
Simultaneous control of elbow, wrist, and hand motions (DeLuca, 1975; DeLuca, 1978; lacobsen, et al., 1982; Meek, et al., 1990). Control using sites naturally related to the desired prosthetic motion (Meek, et al., 1990). Feedback of sensory data (wrist and elbow position, and hand grip forcc) in a natural form to the amputee (Shannon, 1976; DeLuca, 1978; Childress, 1980; lacobsen, et al., 1982).
Objectives. With these obstacles out of the way, researchers at the University of Utah will conduct experiments using nerve signals to control the artificial arm described in this paper. Researchers will surgically implant neural electrodes in nerve bundles in the remnant limbs of participating amputees, and use the nerve signals as command inputs to the artificial arm. These experiments will take place at hospitals throughout the United States and abroad, necessitating a portable, robust system. The experiments will help advance the understanding of the peripheral nervous system, sensory feedback, and the nature of information encoded in efferent nerve signals. The experiments may also aid in defining requirements for future neural prostheses.
Examination of past and current myoelectric arms shows that they have fallen short of meeting these requirements. First, except in limited laboratory experiments (Jerard and Jacobsen, 1980; Meek, et al., 1990), control has been limited to a single degree of freedom, largely due to practical problems (Meek, et al., 1990). Second, the muscle sites used to control myoelectric arms are not always directly related to the desired motion of the arm. For example, the user often must use the biceps and triceps to control the prosthetic hand or wrist. Third, the natural sensory feedback paths (the nerves) are absent in current myoelectric arms. This has made it necessary to use other, less natural forms of sensory feedback, such as vibrotactile and e1ectrotactile stimulators (Shannon, 1976), pneumatic pressure pads (Childress, 1980), and electromechanical actuators (Meek, et al., 1989).
2. HARDWARE The system (shown in Fig. I) is comprised of three primary components:
1.2 Neuroelectric Control
I. 2. 3.
The shortcomings of previous and current technologies illustrate the desirability of using the amputee's natural system of nerves for controlling the prosthetic arm and receiving sensory feedback. Using nerve signals has the potential for providing a more natural and effective level of control, bringing it closer to the requirements listed in the previous section. By recording signals from multiple nerve bundles, simultaneous control of the elbow, wrist, and hand will be possible. By carefully selecting the proper nerve bundles, the motion will be more naturally related to the amputee's intent. By stimulating afferent nerves, sensory feedback may be presented more naturally to the amputee.
Artificial arm. Computer and VO cards. Interface box.
2.1 Artificial Arm.
Design requirements for the artificial arm include: I.
Three independently controllable degrees of freedom, including elbow, wrist, and hand. 2. Torque sensors on the elbow and a grip force sensor on the hand.
314
Fourth, mounting hardware and external connectors were added to make the UA2 suitable for use in a laboratory setting. Grip Force Sensor. Addition of a sensor to measure the grip force of the hand presented certain difficulties. First, the shape of the fingers prohibited using metal foil strain gauges. Second, all electrical signals between the foreann and hand must pass through four slip rings in the wrist, two of which are used to control the hand motor, and one of which is a common ground, making it necessary to find a sensor whose output could be accessed using a single lead.
Fig. I. System hardware, including the artificial ann, interface box, and computer. 3. 4. 5.
A solution was found in FlexiForce™ sensors, manufactured by Tekscan. These sensors are comprised of a force-sensitive resistive element sandwiched between two plastic strips. Sensors rated at III N (25 lb) were selected, matching the force rating of the gripper. The sensors were calibrated by applying known loads and measuring the resulting resistance. The results are shown in Fig. 3. The experimental resistance-force data was approximated by a power fit, given by
Angular position sensors on the wrist and elbow joints. Good performance, including adequate bandwidth and torque capabilities. Reliability, durability, and ease of use.
Development of an entirely new device to meet all of these requirements was time and cost prohibitive, making it more feasible to select an existing arm and make the modifications necessary for neuroelectric control. Of the commercially available anns, the one that most closely met the project requirements was the Utah Arm 2 (UA2), a product of Motion Control, Inc., Salt Lake City, Utah. The UA2 is a successful myoelectrically controlled arm that includes high mechanical performance, reliability, and ease of use.
R = 6749F-l. 0976 "" 6749 ill
(1)
F
This shows that there is an approximate inverse relationship between the resistance and the applied force. A signal conditioning circuit was designed that linearizes the sensor response. The circuit is shown in Fig. 4, where Rs is the resistance of the FlexiForce sensor and U J and U2 are operational amplifiers. Note that only one sensor lead must pass through the wrist slip rings, with the other lead being connected to ground. It can be shown that V 2 is related to the sensor resistance by:
Modifications. Several modifications were necessary to make the UA2 suitable for neuroelectric control experiments. These modifications are summarized in Fig. 2. First, the standard UA2 is a myoelectric arm, whereas this project requires that the ann be controlled using nerve signals. Consequently, all internal EMG signal conditioning and control circuitry in the UA2 was deactivated and bypassed, making a direct path from the external connectors to the pulse width modulators (PWMs) of each motor and to the sensors at each joint.
(2)
Mounting hardware and elcctrical CQnnc:ctor'$
Second, the standard UA2 is equipped with only two powered joints (elbow and hand), so an additional motor and 20 IcHz PWM were added to the wrist.
tnlemal re·.... inng and
~
EMO control d cth-a:tion
Third, only two of the four required sensors are integrated into the standard UA2, requiring the addition of a position sensor (I O-turn, 10 kn, wirewound potentiometer, powered by 6 V power source in ann) at the wrist and a force sensor for the hand (to be described in a subsequent section). The wrist potentiometer was mounted externally due to internal space limitations, and coupled to the wrist motion by a toothed belt, resulting in an output of 0.34 VIrad.
Fig. 2. Modifications to the standard UA2.
315
350 300 250 01200 .
6
•
5
;;:'4
•
~ 3
l ·'
o
~150
a: 100
> 2
'+ , " ' 1 ·,. .•
50 .
,-., +
... -.
•
o
o
25
50 F (N)75
100
,
.. , '
1
o .,::.. o
125
25
50
75
100
125
F(N)
Fig. 5. Conditioned grip force sensor output.
Fig. 3. Grip force sensor calibration curve.
through which the sensing element is exposed. The sensing element is covered with a rubber dome for protection and to .distribute the contact force more evenly over its surfac,. This is shown in Fig. 6.
With the approximate inverse resistance-force relationship (I) substituted into (2), V2 becomes
:.
" .
2.2 Computer and Support Hardware
The controller is implemented on a notebook computer with a 600 MHz Pentium III processor. A notebook computer was selected as the controller platform, instead of a microcontroller or DSP chip, for flexibility and ease of use. Researchers will be able to adjust experiment parameters, modify the control laws, and plot data in the laboratory. A 16-bit AID card is used for analog input, and a 13-bit DJA card provides analog output. Each card has eight digital VO channels, which are used for reading conditioned nerve signals. An overview of the complete system is shown in Fig. 7.
where k is the experimentally determined value 6749 kQ·N. Thus V2 is a linear function of the applied force, with an offset of V}. The second stage of the circuit subtracts that offset, yielding a linear relationship without an offset:
(4)
3. CONTROLLER
The values of Rf and V I were selected so that an input of 111 N (25 lb) results in an output of 5 V. Fig. 5 shows the experimentally verified input-output relationship of the circuit, with a linear fit given by VOUT = 0.0459F V
with R2
As mentioned previously, researchers at the Neuroprosthetics Lab (NPL) at the University of Utah have developed the electrodes and signal conditioning equipment to read nerve signals from efferent nerves. The polymer-based longitudinal intrafascicular electrodes (LIFEs) are stitched into selected nerve bundles in the amputee's remnant
(5)
= 0.9973, indicating good linearity.
The FlexiForce sensor was attached with adhesive to an aluminum mount machined to fit over the thumb. An aluminum sheet protects the sensor, with a hole
Fig. 4. Grip force sensor signal conditioning circuit.
Fig. 6. Detail of grip force sensor mount.
316
NPL
.- .......................... __... ............................... -- ........
Control Syrt.m
;:
Modified Utah Ann 2
feedback is the initial controller implemented for laboratory experiments, given by
i
where K and
K.t are diagonal matrices containing
proportio~al and derivative gains, respectively, 9 is a vector of joint angles, 9 0 is a vector of offsets, ~d Golf is a matrix of input gains relating the frequencies of the nerve signals to the desired joint angles. Fig. 7. An overview of the system. The I~ft~ost block shows the neural interface circuitry provided by the Neuroprosthetics Lab (NPL.
The software, written in C, allows the user to specifY at runtime the controller gains, offsets, filter cutoff frequency, input gains, and sampling rate (which, by default, is 3000 Hz). The user may also enable or disable the filter and data acquisition, select the control type (position, torque), and enter patient data.
limb. The NPL equipment converts the raw nerve signals acquired by the LIFEs into a series of pul~es to be used by the controller. This is illustrated in Fig. 8, where V.1f is a vector of raw efferent nerve signals (up to eight channels), and Deff is the vector of conditioned efferent nerve pulses. The pulses are of SOO ~ec width, with varying frequency. Sensory feedback generated by the controller, in the form of pulses, is converted into raw nerve signals used .to stimulate the afferent nerves using LIFEs. The details of the nerve signal conditioning are not discussed in this paper (Y oshida and Horch, 1996).
4. INITIAL RESULTS Initial experiments have been performed that verifY the capabilities of the arm/controller system. Fig. 10 shows the position control step responses of the elbow with input pulse trains of various frequencies . Note that the response lag increases with decreasing input frequency (increasing period between pulses). Fig. 11 shows the results of an elbow position control experiment, in which simulated nerve signals (derived from EMG signals) were used to contr~1 the elbow position. The uppermost plot shows the mput pulse trains. The second plot shows the frequency of the input pulses, as measured by the controller. The third plot shows the filtered version of the frequency trace. The fourth plot shows the voltage applied to the elbow motor, and the last plot shows the desired and actual position. Note that the controller gains chosen for this experiment were too high, resulting in motor saturation. Similar experiments have been performed in which all three joints were successfully controlled simultaneously using simulated nerve inputs. Other researchers at the University of Utah have used this system in control experiments. Fukuyama (2002) obtained a nonlinear experimental model of the system. Abbott (2001) used the present system in developing a novel method for. predicting limit cycles in systems controlled uSing pulsefrequency-modulated inputs. NPL researchers are
As shown in Fig. 7, the controller reads the eight conditioned pulse streams using the digital 110 lines of the cards described previously. It has been shown (Yoshida and Horch, 1993) that the frequency of these signals determines the desired motion of the prosthetic arm, so the controller must first measure the frequency of each channel of pulse streams by measuring the period between the leading edges, and calculate the frequency from the reciprocal of the period. These frequencies are passed through a Slh_ order low-pass digital Butterworth filter to attenuate noise and undesired spurious nerve activity. These eight filtered frequency signals serve as the command inputs to the arm controller. The system is illustrated in Fig. 9, where feff is the vector of nerve frequencies, f"err are the filtered frequencies (command inputs), and Vm are the motor voltages. A linear MIMO proportional controller with velocity Raw Nerve
NPL
Conditioned Nerve Signals
F~quency
/Idf
Neural Inlerface
Calculation f ...
ii= Irr,
Digital
~
Controller
Artificial Ann
•• 0
(See (6»
Conditioning Circuits
Fig.8. The nerve conditioning process, in which raw signals are converted into pulse streams.
Fig. 9. Signal conditioning and control system, shown without sensory feedback.
317
I.
2. 3.
-==
- -
'10 O0H, Nz
Adopting more advanced control schemes using the methods proposed by Abbott (200 I) and Fukuyama (2002). Designing the controller using Simulink® and implementing it with Real-Time Workshop®. Eventually using microcontrollers or DSPs to handle all signal processing and control.
I REFERENCES
l---7. .~~---! 0 ';----':0",-----'-;:-;;---;:',----:-,-~,=-,--,,..,--~
- 100
11~ )
Abbott, 1. (2001). Design tools for pulse-frequencymodulated control systems: error analysis and limit-cycle prediction. M.S. Thesis, University of Utah. Childress, D. (l'~80). Closed-loop control in prosthetic systemsthistorical perspective. Ann. Biomed. Eng. 8,293:303. DeLuca, C. (1975). Considerations for using the nerve signal as a control source for above-elbow prostheses. Proc. Fifth Int. Symp, Ext. Cont. Human Extremeties, 101-109. DeLuca, C. (1978). Control of upper-limb prostheses: a case for neuroelectric control. 1. Med. Eng, Tech. 2, 57-61. Fukuyama, A. (2001). M.S. Thesis work in progress. Jacobsen, S., R. Jerard and D. Knutti (1975). Devclopment and control of the Utah Arm. Proc. Fifth Int. Symp. Ext. Cont. Human Extremities. Jacobsen, S., D. Knutti, R. Johnson and H. Sears (1982). Development of the Utah Artificial Arm. IEEE Trans. Biomed. Eng. BME-29, 249-269. Jerard,R. and S. Jacobsen (1980). Laboratory evaluation of a unified theory for simultaneous multiple axis artificial arm control. ASME Trans. 102,199-207. Meek, S., S. Jacobsen and P. Goulding (1989). Extended physiologic taction: design and evaluation of a proportional force feedback system. 1. Rehabil. Res. & Dev. 26, 53-62. Meek, S., J. Wood and S. Jacobsen (1990). Modelbased, multi-muscle EMG control of upperextremity prostheses. In: Multiple Muscle Systems: Biomechanics and Movement Organization (1. Winters and S. Wood, Eds.), ch. 21. Springer-Verlag, New York. Shannon, G. (1976). A comparison of alternative means of providing sensory feedback on upper limb prostheses. Med. & Bioi. Eng., 289-294. Yoshida, K. and K. Horch (1993). Selective stimulation of peripheral nerve fibers using dual intrafascicular electrodes. IEEE Trans. Biomed. Eng. 40, 492-494. Yoshida, K. and K. Horch (1996). Closed-loop control of ankle position using muscle afferent feedback with functional neuromuscular stimulation. IEEE Trans. Biomed. Eng. 43, 167176.
Fig. 10. Elbow position control step responses. currently using the system in experiments using actual nerve signals. Initial results look promising.
5. CONCLUSION An artificial arm for use in neuroelectric control experiments has been presented, and some of its important features detailed. The system is portable, flexible, and robust, all of which are requirements for neuroelectric control experiments in the field. Four arms have been produced, and are in use by NPL researchers. Future work may include:
Fig. 11. Example of elbow position control. From the top: input pulse stream, measured frequency, filtered frequency, motor voltage, elbow position.
318
Copyright 0 IFAC Mechatronic Systems, California, USA, 2002
c:
0
[>
Publications www.eIsevier.comllocate/ifac
AN EXPERIMENTAL NEUROELECTRIC PROSTHETIC ARM
Mark B. Colton
Sanford G. Meek
University of Utah Department of Mechanical Engineering 50 S. Central Campus Dr. Rm. 2102 Salt Lake City. Utah 841 12
Abstract: In this paper, a prosthetic arm developed for use in neuroelectric control experiments is described. The motivation for neuroelectrically-controlled prostheses is presented, as wcll as an overview of the design features required for effective and natural control. The development of the prosthetic hardware and control system are described, with emphasis on the sensors used in the control laws and for sensory feedback. A method of using the frequency of efferent nerve pulses as command inputs is also described. Preliminary experimental results are presented. Copyright © 2002 IFAC Keywords : Biomedical systems, Mechanical Multivariable control, Position control, Sensors
I. INTRODUCTION
manipulators,
Command
signals,
1.1 Background Before examining neuroelectric control in greater detail, it is useful to present some background, including a look at the current state of the art in controlling prosthetic arms. By doing so, the motivation behind developing a neuroelectricallycontrolled artificial arm will be come apparent.
For the past 50 years much research has gone into improving externally powered prosthetic arms. The approaches have been varied, but in each case the objective has been the same: restoration of the function lost through amputation or congenital defect. This paper describes the development of an experimental prosthetic arm that will allow researchers to conduct experiments in neuroelectric control, i.e., controlling the prosthesis using nerve signals. Experiments using this system will allow researchers to study the nature of signals contained in peripheral nerves, better understand the mechanisms of sensory feedback, and define requirements for future neuroelectric prosthetic arms that will result in more natural control for amputees.
Myoelectric Control. Currently, the most popular method of controlling a prosthetic limb is myoelectric control, in which electromyograph (EMG) signals from the muscles in the remnant limbs are used as command inputs to the artificial arm. Electrodes are generally affixed to the surface of the skin, placed directly over the muscles from which the EMG signals are to be recruited. By flexing the muscles in the remnant limb, the amputee is able to control the joints of the artificial arm. This is how commercial
313
myoelectric arms function (Jacobsen, et al., 1975; lacobsen, et al., 1982).
Obstacles. Using nerves to control and receive sensory feedback from a prosthetic arm is not a new idea, but two key obstacles have hindered its implementation (DeLuca, 1975; lacobsen, et al., 1982): neural electrodes and computational power. These obstacles have been overcome, to a large extent, with recent advances. First, neural electrodes with suitable mechanical and electrical properties, and signal conditioning equipment have been developed at the University of Utah (Yoshida and Horch, 1993), making it possible for researchers to selectively stimulate and record from individual nerve bundles (Y oshida and Horch, 1996). Second, fast and affordable microprocessors and specialized digital signal proc~ssors (DSPs) are readily available, making the computat,?nal aspect of the neurocontrol problem less of an l~ue. Signals from multiple nerves can be rcad and processed in real-time, while simultaneously controlling an artificial arm.
The Ideal. Although considerable success has been achieved with myoelectrically controlled artificial arms, their effectiveness has been limited because they do not provide the type of natural control that was mentioned previously. An artificial arm that would achieve this ideal level of natural of control is one that would meet the following requirements: l.
2. 3.
Simultaneous control of elbow, wrist, and hand motions (DeLuca, 1975; DeLuca, 1978; lacobsen, et al., 1982; Meek, et al., 1990). Control using sites naturally related to the desired prosthetic motion (Meek, et al., 1990). Feedback of sensory data (wrist and elbow position, and hand grip forcc) in a natural form to the amputee (Shannon, 1976; DeLuca, 1978; Childress, 1980; lacobsen, et al., 1982).
Objectives. With these obstacles out of the way, researchers at the University of Utah will conduct experiments using nerve signals to control the artificial arm described in this paper. Researchers will surgically implant neural electrodes in nerve bundles in the remnant limbs of participating amputees, and use the nerve signals as command inputs to the artificial arm. These experiments will take place at hospitals throughout the United States and abroad, necessitating a portable, robust system. The experiments will help advance the understanding of the peripheral nervous system, sensory feedback, and the nature of information encoded in efferent nerve signals. The experiments may also aid in defining requirements for future neural prostheses.
Examination of past and current myoelectric arms shows that they have fallen short of meeting these requirements. First, except in limited laboratory experiments (Jerard and Jacobsen, 1980; Meek, et al., 1990), control has been limited to a single degree of freedom, largely due to practical problems (Meek, et al., 1990). Second, the muscle sites used to control myoelectric arms are not always directly related to the desired motion of the arm. For example, the user often must use the biceps and triceps to control the prosthetic hand or wrist. Third, the natural sensory feedback paths (the nerves) are absent in current myoelectric arms. This has made it necessary to use other, less natural forms of sensory feedback, such as vibrotactile and e1ectrotactile stimulators (Shannon, 1976), pneumatic pressure pads (Childress, 1980), and electromechanical actuators (Meek, et al., 1989).
2. HARDWARE The system (shown in Fig. I) is comprised of three primary components:
1.2 Neuroelectric Control
I. 2. 3.
The shortcomings of previous and current technologies illustrate the desirability of using the amputee's natural system of nerves for controlling the prosthetic arm and receiving sensory feedback. Using nerve signals has the potential for providing a more natural and effective level of control, bringing it closer to the requirements listed in the previous section. By recording signals from multiple nerve bundles, simultaneous control of the elbow, wrist, and hand will be possible. By carefully selecting the proper nerve bundles, the motion will be more naturally related to the amputee's intent. By stimulating afferent nerves, sensory feedback may be presented more naturally to the amputee.
Artificial arm. Computer and VO cards. Interface box.
2.1 Artificial Arm.
Design requirements for the artificial arm include: I.
Three independently controllable degrees of freedom, including elbow, wrist, and hand. 2. Torque sensors on the elbow and a grip force sensor on the hand.
314
Fourth, mounting hardware and external connectors were added to make the UA2 suitable for use in a laboratory setting. Grip Force Sensor. Addition of a sensor to measure the grip force of the hand presented certain difficulties. First, the shape of the fingers prohibited using metal foil strain gauges. Second, all electrical signals between the foreann and hand must pass through four slip rings in the wrist, two of which are used to control the hand motor, and one of which is a common ground, making it necessary to find a sensor whose output could be accessed using a single lead.
Fig. I. System hardware, including the artificial ann, interface box, and computer. 3. 4. 5.
A solution was found in FlexiForce™ sensors, manufactured by Tekscan. These sensors are comprised of a force-sensitive resistive element sandwiched between two plastic strips. Sensors rated at III N (25 lb) were selected, matching the force rating of the gripper. The sensors were calibrated by applying known loads and measuring the resulting resistance. The results are shown in Fig. 3. The experimental resistance-force data was approximated by a power fit, given by
Angular position sensors on the wrist and elbow joints. Good performance, including adequate bandwidth and torque capabilities. Reliability, durability, and ease of use.
Development of an entirely new device to meet all of these requirements was time and cost prohibitive, making it more feasible to select an existing arm and make the modifications necessary for neuroelectric control. Of the commercially available anns, the one that most closely met the project requirements was the Utah Arm 2 (UA2), a product of Motion Control, Inc., Salt Lake City, Utah. The UA2 is a successful myoelectrically controlled arm that includes high mechanical performance, reliability, and ease of use.
R = 6749F-l. 0976 "" 6749 ill
(1)
F
This shows that there is an approximate inverse relationship between the resistance and the applied force. A signal conditioning circuit was designed that linearizes the sensor response. The circuit is shown in Fig. 4, where Rs is the resistance of the FlexiForce sensor and U J and U2 are operational amplifiers. Note that only one sensor lead must pass through the wrist slip rings, with the other lead being connected to ground. It can be shown that V 2 is related to the sensor resistance by:
Modifications. Several modifications were necessary to make the UA2 suitable for neuroelectric control experiments. These modifications are summarized in Fig. 2. First, the standard UA2 is a myoelectric arm, whereas this project requires that the ann be controlled using nerve signals. Consequently, all internal EMG signal conditioning and control circuitry in the UA2 was deactivated and bypassed, making a direct path from the external connectors to the pulse width modulators (PWMs) of each motor and to the sensors at each joint.
(2)
Mounting hardware and elcctrical CQnnc:ctor'$
Second, the standard UA2 is equipped with only two powered joints (elbow and hand), so an additional motor and 20 IcHz PWM were added to the wrist.
tnlemal re·.... inng and
~
EMO control d cth-a:tion
Third, only two of the four required sensors are integrated into the standard UA2, requiring the addition of a position sensor (I O-turn, 10 kn, wirewound potentiometer, powered by 6 V power source in ann) at the wrist and a force sensor for the hand (to be described in a subsequent section). The wrist potentiometer was mounted externally due to internal space limitations, and coupled to the wrist motion by a toothed belt, resulting in an output of 0.34 VIrad.
Fig. 2. Modifications to the standard UA2.
315
350 300 250 01200 .
6
•
5
;;:'4
•
~ 3
l ·'
o
~150
a: 100
> 2
'+ , " ' 1 ·,. .•
50 .
,-., +
... -.
•
o
o
25
50 F (N)75
100
,
.. , '
1
o .,::.. o
125
25
50
75
100
125
F(N)
Fig. 5. Conditioned grip force sensor output.
Fig. 3. Grip force sensor calibration curve.
through which the sensing element is exposed. The sensing element is covered with a rubber dome for protection and to .distribute the contact force more evenly over its surfac,. This is shown in Fig. 6.
With the approximate inverse resistance-force relationship (I) substituted into (2), V2 becomes
:.
" .
2.2 Computer and Support Hardware
The controller is implemented on a notebook computer with a 600 MHz Pentium III processor. A notebook computer was selected as the controller platform, instead of a microcontroller or DSP chip, for flexibility and ease of use. Researchers will be able to adjust experiment parameters, modify the control laws, and plot data in the laboratory. A 16-bit AID card is used for analog input, and a 13-bit DJA card provides analog output. Each card has eight digital VO channels, which are used for reading conditioned nerve signals. An overview of the complete system is shown in Fig. 7.
where k is the experimentally determined value 6749 kQ·N. Thus V2 is a linear function of the applied force, with an offset of V}. The second stage of the circuit subtracts that offset, yielding a linear relationship without an offset:
(4)
3. CONTROLLER
The values of Rf and V I were selected so that an input of 111 N (25 lb) results in an output of 5 V. Fig. 5 shows the experimentally verified input-output relationship of the circuit, with a linear fit given by VOUT = 0.0459F V
with R2
As mentioned previously, researchers at the Neuroprosthetics Lab (NPL) at the University of Utah have developed the electrodes and signal conditioning equipment to read nerve signals from efferent nerves. The polymer-based longitudinal intrafascicular electrodes (LIFEs) are stitched into selected nerve bundles in the amputee's remnant
(5)
= 0.9973, indicating good linearity.
The FlexiForce sensor was attached with adhesive to an aluminum mount machined to fit over the thumb. An aluminum sheet protects the sensor, with a hole
Fig. 4. Grip force sensor signal conditioning circuit.
Fig. 6. Detail of grip force sensor mount.
316
NPL
.- .......................... __... ............................... -- ........
Control Syrt.m
;:
Modified Utah Ann 2
feedback is the initial controller implemented for laboratory experiments, given by
i
where K and
K.t are diagonal matrices containing
proportio~al and derivative gains, respectively, 9 is a vector of joint angles, 9 0 is a vector of offsets, ~d Golf is a matrix of input gains relating the frequencies of the nerve signals to the desired joint angles. Fig. 7. An overview of the system. The I~ft~ost block shows the neural interface circuitry provided by the Neuroprosthetics Lab (NPL.
The software, written in C, allows the user to specifY at runtime the controller gains, offsets, filter cutoff frequency, input gains, and sampling rate (which, by default, is 3000 Hz). The user may also enable or disable the filter and data acquisition, select the control type (position, torque), and enter patient data.
limb. The NPL equipment converts the raw nerve signals acquired by the LIFEs into a series of pul~es to be used by the controller. This is illustrated in Fig. 8, where V.1f is a vector of raw efferent nerve signals (up to eight channels), and Deff is the vector of conditioned efferent nerve pulses. The pulses are of SOO ~ec width, with varying frequency. Sensory feedback generated by the controller, in the form of pulses, is converted into raw nerve signals used .to stimulate the afferent nerves using LIFEs. The details of the nerve signal conditioning are not discussed in this paper (Y oshida and Horch, 1996).
4. INITIAL RESULTS Initial experiments have been performed that verifY the capabilities of the arm/controller system. Fig. 10 shows the position control step responses of the elbow with input pulse trains of various frequencies . Note that the response lag increases with decreasing input frequency (increasing period between pulses). Fig. 11 shows the results of an elbow position control experiment, in which simulated nerve signals (derived from EMG signals) were used to contr~1 the elbow position. The uppermost plot shows the mput pulse trains. The second plot shows the frequency of the input pulses, as measured by the controller. The third plot shows the filtered version of the frequency trace. The fourth plot shows the voltage applied to the elbow motor, and the last plot shows the desired and actual position. Note that the controller gains chosen for this experiment were too high, resulting in motor saturation. Similar experiments have been performed in which all three joints were successfully controlled simultaneously using simulated nerve inputs. Other researchers at the University of Utah have used this system in control experiments. Fukuyama (2002) obtained a nonlinear experimental model of the system. Abbott (2001) used the present system in developing a novel method for. predicting limit cycles in systems controlled uSing pulsefrequency-modulated inputs. NPL researchers are
As shown in Fig. 7, the controller reads the eight conditioned pulse streams using the digital 110 lines of the cards described previously. It has been shown (Yoshida and Horch, 1993) that the frequency of these signals determines the desired motion of the prosthetic arm, so the controller must first measure the frequency of each channel of pulse streams by measuring the period between the leading edges, and calculate the frequency from the reciprocal of the period. These frequencies are passed through a Slh_ order low-pass digital Butterworth filter to attenuate noise and undesired spurious nerve activity. These eight filtered frequency signals serve as the command inputs to the arm controller. The system is illustrated in Fig. 9, where feff is the vector of nerve frequencies, f"err are the filtered frequencies (command inputs), and Vm are the motor voltages. A linear MIMO proportional controller with velocity Raw Nerve
NPL
Conditioned Nerve Signals
F~quency
/Idf
Neural Inlerface
Calculation f ...
ii= Irr,
Digital
~
Controller
Artificial Ann
•• 0
(See (6»
Conditioning Circuits
Fig.8. The nerve conditioning process, in which raw signals are converted into pulse streams.
Fig. 9. Signal conditioning and control system, shown without sensory feedback.
317
I.
2. 3.
-==
- -
'10 O0H, Nz
Adopting more advanced control schemes using the methods proposed by Abbott (200 I) and Fukuyama (2002). Designing the controller using Simulink® and implementing it with Real-Time Workshop®. Eventually using microcontrollers or DSPs to handle all signal processing and control.
I REFERENCES
l---7. .~~---! 0 ';----':0",-----'-;:-;;---;:',----:-,-~,=-,--,,..,--~
- 100
11~ )
Abbott, 1. (2001). Design tools for pulse-frequencymodulated control systems: error analysis and limit-cycle prediction. M.S. Thesis, University of Utah. Childress, D. (l'~80). Closed-loop control in prosthetic systemsthistorical perspective. Ann. Biomed. Eng. 8,293:303. DeLuca, C. (1975). Considerations for using the nerve signal as a control source for above-elbow prostheses. Proc. Fifth Int. Symp, Ext. Cont. Human Extremeties, 101-109. DeLuca, C. (1978). Control of upper-limb prostheses: a case for neuroelectric control. 1. Med. Eng, Tech. 2, 57-61. Fukuyama, A. (2001). M.S. Thesis work in progress. Jacobsen, S., R. Jerard and D. Knutti (1975). Devclopment and control of the Utah Arm. Proc. Fifth Int. Symp. Ext. Cont. Human Extremities. Jacobsen, S., D. Knutti, R. Johnson and H. Sears (1982). Development of the Utah Artificial Arm. IEEE Trans. Biomed. Eng. BME-29, 249-269. Jerard,R. and S. Jacobsen (1980). Laboratory evaluation of a unified theory for simultaneous multiple axis artificial arm control. ASME Trans. 102,199-207. Meek, S., S. Jacobsen and P. Goulding (1989). Extended physiologic taction: design and evaluation of a proportional force feedback system. 1. Rehabil. Res. & Dev. 26, 53-62. Meek, S., J. Wood and S. Jacobsen (1990). Modelbased, multi-muscle EMG control of upperextremity prostheses. In: Multiple Muscle Systems: Biomechanics and Movement Organization (1. Winters and S. Wood, Eds.), ch. 21. Springer-Verlag, New York. Shannon, G. (1976). A comparison of alternative means of providing sensory feedback on upper limb prostheses. Med. & Bioi. Eng., 289-294. Yoshida, K. and K. Horch (1993). Selective stimulation of peripheral nerve fibers using dual intrafascicular electrodes. IEEE Trans. Biomed. Eng. 40, 492-494. Yoshida, K. and K. Horch (1996). Closed-loop control of ankle position using muscle afferent feedback with functional neuromuscular stimulation. IEEE Trans. Biomed. Eng. 43, 167176.
Fig. 10. Elbow position control step responses. currently using the system in experiments using actual nerve signals. Initial results look promising.
5. CONCLUSION An artificial arm for use in neuroelectric control experiments has been presented, and some of its important features detailed. The system is portable, flexible, and robust, all of which are requirements for neuroelectric control experiments in the field. Four arms have been produced, and are in use by NPL researchers. Future work may include:
Fig. 11. Example of elbow position control. From the top: input pulse stream, measured frequency, filtered frequency, motor voltage, elbow position.
318