Prosthetics, Motor Control - University of Southern California · PDF fileProsthetics, Motor Control 3 4 Gerald E. Loeb and Ning Lan 5 Introduction 6 This article deals with the subset

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Prosthetics, Motor Control34 Gerald E. Loeb and Ning Lan

5 Introduction

6 This article deals with the subset of neural prosthetic interfaces that7 employ electrical stimulation to alter the function of motor systems,8 either directly or indirectly. The general biophysical considerations9 and technology are described in PROSTHETICS, NEURAL (q.v.).

10 Clinical Applications

11 Therapeutic Electrical Stimulation

12 Therapeutic electrical stimulation (TES) is electrically produced13 exercise in which the beneficial effect occurs primarily off-line as14 a result of trophic effects on muscles and perhaps the central ner-15 vous system (CNS). One simple example is periodic exercise of16 the shoulder muscles to prevent disuse atrophy after a stroke17 (Faghri et al., 1994), which otherwise often results in chronically18 painful subluxation of the joint. TES effects have also been used19 to reduce spasticity following spinal cord injury (Stefanovska et20 al., 1989), presumably by downregulating the gain of hyperactive21 spinal reflex circuits. TES systems are relatively simple to imple-22 ment because the patient chooses when and where to administer23 the treatment and does not require any immediate effects from the24 stimulation. Stimulation programs are usually devised by the care-25 giver, but some parameters may be adjusted manually by the patient26 during self-treatment sessions.

27 Neuromodulatory Stimulation

28 Neuromodulatory stimulation (NMS) involves preprogrammed29 stimulation that directly triggers or modulates a function without30 ongoing control or feedback from the patient. Perhaps the oldest31 clinically successful neural prosthesis is phrenic nerve pacing to32 provide respiration in patients with central hypoventilation (Glenn33 and Phelps, 1985). More recently, sacral nerve stimulation has been34 used successfully to empty the bladder (Brindley and Rushton,35 1990) and to reduce detrusor spasticity in patients with urge in-36 continence (Dijkema et al., 1993). NMS systems must be portable37 and reliable, but they function mostly autonomously.

38 Functional Electrical Stimulation

39 Functional electrical stimulation (FES) involves precisely con-40 trolled muscle contractions that produce specific movements re-41 quired by the patient to perform a task. Much motor prosthetic42 research has been aimed toward permitting paraplegic patients to43 walk, a high-risk, high-energy activity that requires sophisticated44 interactions among the patient’s immediate intentions, the pattern45 of stimulation applied to multiple muscles, and the ongoing move-46 ment elicited in the limbs. There have been some laboratory dem-47 onstrations of relatively complex but still crude systems that permit48 slow locomotor progress, but none is yet available clinically. The49 WalkAide is an FDA-approved (but not widely available) prosthe-50 sis that uses transcutaneous stimulation of the peroneal nerve to51 correct foot drop (Wieler et al., 1999). Research emphasis has52 shifted to FES-assisted grasp in quadriplegic patients, using resid-53 ual motor function in the proximal and contralateral limb to control54 stimulation of finger muscles (Prochazka et al., 1997; Smith et al.,55 1998; Figure 1). Most of the subsystems described in the next sec-56 tion are in development to improve on-line control of FES.

57 Subsystems

58 Muscle Stimulation


59 Most research has been performed with skin surface electrodes,60 percutaneous wire electrodes, and implanted multichannel stimu-61 lators (Figure 1). The development of advanced stimulation tech-62 niques that require less extensive surgery (e.g., intramuscular63 BIONs, Figure 2; Loeb et al., 2001) promises to improve the prac-64 ticality of FES systems that require specific and reliable control of65 large numbers of individual muscles. Electrical activation of mus-66 cles by any route does not replicate the natural orderly recruitment67 of different types of muscle fibers, which gives rise to the high68 efficiency and fatigue resistance of normal force production. How-69 ever, artificially stimulated muscles gradually undergo fiber-type70 conversions as a result of trophic effects that improve their aerobic71 capacity (Peckham, Mortimer, and Van der Meulen, 1973).72 Active muscle has complex intrinsic mechanical properties that73 complicate attempts to develop feedforward control strategies74 based on predicting joint torques and movements. Muscle force75 depends nonlinearly on the number and frequency of firing of re-76 cruited muscle units and on the length and velocity of the muscle77 fibers. Many muscles have substantial amounts of series-elastic78 connective tissue (tendon and aponeurosis), which means that the79 length and velocity of the muscle fibers depend, in turn, on the80 amount of stretch that they produce in that connective tissue, as81 well as on the trajectory of the limb. While difficult to model math-82 ematically, these complexities appear to play an important role in83 stabilizing the limb during rapid perturbations (Brown and Loeb,84 2000) and in storing and releasing energy to improve the efficiency85 of cyclical movements such as walking. Many muscles cross more86 than one joint, further complicating their effects on the overall tra-87 jectory of movements.

88 Sensory Feedback

89 In biological sensorimotor control, an order of magnitude more90 neural information comes from intramuscular proprioceptors (mus-91 cle spindle and tendon organ afferents) than goes out to control92 motor units. When this information is absent, both animals and93 humans have a great deal of difficulty making stable and accurate94 movements. For tasks requiring manipulation of objects, informa-95 tion from cutaneous mechanoreceptors is even more important.96 Most rehabilitation therapists believe that an insensate hand is ac-97 tually less useful than a paralyzed hand.98 There are three general approaches to providing sensory feed-99 back signals to implement biological-like control systems:

100 • Recording the proprioceptive and cutaneous signals that are still101 largely present in the peripheral nerves and dorsal root ganglia102 of patients with upper motor lesions. Microelectrode arrays have103 been implanted long term into these structures in animals (Loeb,104 Bak, and Duysens, 1977), but the technology is not yet robust105 enough for clinical use. Nerve cuff electrodes can record the106 aggregate activity of the large-diameter fibers in peripheral107 nerves, which can be useful in nerves with fairly homogeneous108 populations of afferents such as those innervating the digits109 (Haugland et al., 1999).110 • Affixing various electromechanical sensors to the surface of the111 skin or to worn components of the prosthetic system, such as112 braces and gloves. While useful as research tools, such external113 appliances generally result in unacceptable problems related to114 mechanical maintenance, donning time, and physical appearance.115 • Implanting artificial sensors into the sites where they are needed.116 In addition to the design problems inherent in protecting electro-117 mechanical sensors from body fluids, such systems also require118 electrical leads or wireless communication to handle data and119 power requirements from large numbers of distributed120 transducers.

122 Sensorimotor Regulation

123 It has long been known that biological systems use a form of ser-124 vocontrol. Mechanical perturbations sensed by mechanoreceptors125 give rise to specific reflex responses that tend to stabilize posture126 and force in the limb (e.g., the stretch reflex). More recently, spinal


127 neurophysiologists have made substantial progress in unraveling128 the complexities of the spinal interneuronal circuitry and its role in129 coordinating descending commands with continuous sensory130 feedback.131 The peripheral motor control system is substantially different in132 its organization from the servocontrollers used in robots. Spinal133 interneurons receive convergent input from many different modal-134 ities and origins of proprioceptive and cutaneous afferents, and they135 tend to project directly and indirectly to motor neurons controlling136 many different muscles and joints. Furthermore, most of the de-137 scending command signals from the brain that control limb move-138 ments terminate on these interneurons rather than directly on motor139 neurons. This has three important implications for the design of140 biological control systems (the ramifications for FES control re-141 main unclear):

142 • The effects of command signals are essentially continuously143 modulated by the background activity from somatosensory af-144 ferents converging on the spinal interneurons.145 • The brain can achieve a particular pattern of muscle activation146 via many different programs of interneuronal activation and in-147 hibition, with each program resulting in potentially different pat-148 terns of reflex responses to perturbations.149 • Descending pathways appear to be organized to produce various150 synergies of muscle recruitment and derecruitment rather than151 specific control of individual muscles.

153 FES control systems are starting to employ state-dependent logic154 to switch among different regulatory algorithms as different phases155 of the movement are detected from patterns in the signals from156 sensors (Kostov et al., 1995).

157 Control Systems

158 Controllers convert a given volitional command signal into a set159 of time-varying outputs from which the instantaneous intensities160 of muscle stimulation can be computed. Robotic engineering ap-161 proaches that are based on complete knowledge of the sensorimotor162 plant have proved difficult to apply to the many degrees of freedom163 to be controlled and the number and complexity of the muscles to164 be stimulated for an FES task. An alternative approach may be to165 duplicate the adaptive control strategies of the CNS, which tends166 to perform much better in the low-precision but unpredictable de-167 mands of most activities of daily living. Biological sensorimotor168 systems appear to be organized in a hierarchical manner (Loeb,169 Brown, and Cheng, 1999), in which each layer of information pro-170 cessing plays a distinctive and important role in achieving goals171 with reasonable accuracy, stability, and energy efficiency. It re-172 mains to be elucidated how a motor goal is translated into a pattern173 of muscle activation through this hierarchical process, and what the174 organizing principles behind the formation of motor programs are.175 Given a sufficiently rich set of sensory feedback and informative176 commands, it may be possible to create interneuron-like networks177 for sensorimotor regulation and to use neural networks to learn how178 to control them to achieve similar goals for FES. For this strategy179 to be acceptable clinically, however, it will have to minimize the180 sort of trial-and-error sensorimotor learning that occupies so much181 of an infant’s first few years of life.

182 Command Signals

183 Command signals convey the intent of the user to the control sys-184 tem of the prosthetic device. The control system senses and inter-185 prets the user’s intent and computes an appropriate pattern of mus-186 cle stimulation. The controllers of all current FES systems and187 motorized artificial limbs obtain command signals from the my-188 oelectrical activity or mechanical motion produced by those mus-189 cles that the subject can still control voluntarily. There is a general190 paradox in prosthetic motor control, however: the higher the level191 of the injury, the more degrees of freedom the prosthetic system192 must control, but the fewer the sources of voluntary command sig-193 nals. For FES control of grasp, contralateral shoulder motion and194 residual wrist movement have been used to provide relatively sim-


195 ple commands (Smith et al., 1998). Subjects are able to produce196 reasonably high information rates on one channel by modulating197 rapidly among several distinguishable positions, probably because198 these muscles are still equipped with proprioceptive feedback.199 Other command sources, such as EMG and voice, tend to be slower200 and/or less precise. It remains to be seen whether systems can be201 designed to command the multiple simultaneous degrees of free-202 dom involved in tasks such as coordinated reach and grasp.203 An alternative approach to sensing residual voluntary muscle204 activity is to record command signals directly from the CNS above205 the level of the lesion. Attempts to record “brainwaves” via EEG206 and gross electrocortical electrodes have produced only very low207 data rates (McFarland, McCane, and Wolpaw, 1998), probably be-208 cause they reflect the aggregate activity of millions of neurons car-209 rying very different signals simultaneously. Chronic unit recording210 techniques have been used for many years as a research tool to211 understand the role of the sensorimotor cortex in controlling natural212 motor behaviors; technologies feasible for clinical use are starting213 to emerge (Rousche and Normann, 1998). Limited functional use214 of such signals has been demonstrated in animals (Chapin et al.,215 1999) but the ultimate potential is likely to depend on the repre-216 sentation of complex movement in the brain, a subject that is still217 hotly debated by neurophysiologists. For example, it has been var-218 iously proposed that the primary motor cortex (Brodman’s area 4)219 contains a representation of the desired position in space of the220 hand, the angles of the joints required to achieve a desired posture,221 the amount of force required from the individual muscles, and the222 states of the spinal interneurons. These have very different impli-223 cations for the design of a controller required to respond to and224 interpret such command signals.

225 General Conclusions

226 At one extreme, motor prostheses require only very simple exercise227 of one or a few muscles. At the other extreme, they require so-228 phisticated bidirectional interfaces with the patient and on-line so-229 lution of problems in motor coordination that are normally solved230 by complex and poorly understood circuitry in the brain and spinal231 cord. FES applications provide particularly interesting challenges232 to our theoretical understanding of the normal roles of muscles,233 proprioceptors, spinal reflex pathways, and trajectory planning by234 the brain. They have also sparked attempts to reconcile traditional235 engineering approaches to the control of robotic manipulators with236 the very different but still obscure strategies for adaptive sensori-237 motor control in living organisms.

238 Roadmap: Applications; Mammalian Motor Control239 Related Reading: Motor Control, Biological and Theoretical; Motoneuron240 Recruitment; Muscle Models; Prosthetics, Neural; Prosthetics, Sensory241 Systems

242 References243 Brindley, G. S., and Rushton, D. N., 1990, Long-term follow-up of patients244 with sacral anterior root stimulator implants, Paraplegia, 28:469–475.245 Brown, I. E., and Loeb, G. E., 2000, A reductionist approach to creating246 and using neuromusculoskeletal models, in Neuro-Control of Posture247 and Movement (J. Winters and P. Crago, Eds.), New York: Springer.248 Verlag, pp. 148–163.249 Chapin, J. K., Moxon, K. A., Markowitz, R. S., and Nicolelis, M. A. L.,250 1999, Real-time control of a robot arm using simultaneously recorded251 neurons in the motor cortex, Nature Neurosci., 2:664–670.252 Dijkema, H. E., Weil, E. H. J., Mijs, P. T., and Janknegt, R. A., 1993,253 Neuromodulation of sacral nerves for incontinence and voiding dys-254 functions: Clinical results and complications, Eur. Urol., 24:72–76.255 Faghri, P. D., Rodger, M. M., Glaser, R. M., Bors, J. G., Ho, C., and256 Akuthota, P., 1994, The effects of functional electrical stimulation on257 shoulder subluxation, arm function recovery, and shoulder pain in hem-258 iplegic stroke patients, Arch. Phys. Med. Rehabil., 75:73–79.259 Glenn, W. W. L., and Phelps, M. L., 1985, Diaphragm pacing by electrical260 stimulation of the phrenic nerve, Neurosurgery, 17:974–984.261 Haugland, M., Lickel, A., Haase, J., and Sinkjaer, T., 1999, Control of FES262 thumb force using slip information obtained from the cutaneous electro-263 neurogram in quadriplegic man. IEEE Trans. Rehabil. Eng. 7(2):215–264 227.


265 Kostov, A., Andrews, B. J., Popovic, D., Stein, R. B., and Armstrong, W.266 W., 1995, Machine learning in control of functional electrical stimulation267 systems for locomotion, IEEE Trans. Biomed. Eng., 42:541–551.268 Loeb, G. E., Bak, M. J., and Duysens, J., 1977, Long-term unit recording269 from somatosensory neurons in the spinal ganglia of the freely walking270 cat, Science, 197:1192–1194.271 Loeb, G. E., Brown, I. E., and Cheng, E., 1999, A hierarchical foundation272 for models of sensorimotor control, Exp. Brain Res., 126:1–18.273 Loeb, G. E., Peck, R. A., Moore, W. H., and Hood, K., 2001, BION system274 for distributed neural prosthetic interfaces, Med. Eng. Phys. 23:9–18. �275 McFarland, D. J., McCane, L. M., and Wolpaw, J. R., 1998, EEG-based276 communication and control: Short-term role of feedback, IEEE Trans.277 Rehabil. Eng., 6:7–11.278 Peckham, P. H., Mortimer, J. T., and Van der Meulen, J. P., 1973, Physi-279 ologic and metabolic changes in white muscle of cat following induced280 exercise, Brain Res., 50:424–429.281 Prochazka, A., Gauthier, M., Wieler, M., and Kenwell, Z., 1997, The bionic282 glove: An electrical stimulator garment that provides controlled grasp283 and hand opening in quadriplegia, Arch. Phys. Med. Rehabil., 78:608–284 614.285 Rousche, P. J., and Normann, R. A., 1998, Chronic recording capability of286 the Utah Intracortical Electrode Array in cat sensory cortex, J. Neurosci.287 Methods, 82:1–15.288 Smith, B., Tang, Z., Johnson, M. W., Pourmehdi, S., Gazdik, M. M., Buck-289 ett, J. R., and Peckham, P. H., 1998, Externally powered, multichannel,290 implantable stimulator-telemeter for control of paralyzed muscle, IEEE291 Trans. Biomed. Eng., 45:463–475. �292 Stefanovska, A., Vodovnik, L., Gros, N., Rebersek, S., and Acimovic-293 Janezic, R., 1989, FES and spasticity, IEEE Trans. Biomed. Eng.,294 36:738–745.295 Wieler, M., Stein, R. B., Ladouceur, M. Whittaker, M., Smith, A. W., Naa-296 man, S., Barbeau, H., Bugaresti, J., and Aimone, E. 1999, Multicenter297 evaluation of electrical stimulation systems for walking, Arch. Phys.298 Med. Rehabil., 80:495–500.

299


303 AQ 1: Author: Pls. provide permission from manufac-304 ture to use this illustration.305


308The NeuroControl Freehand System

Electrode

Electrodes

Electrode Leads

Implanted Stimulator

Transmitting CoilExternalController

Shoulder Position Sensor

309310 Figure 1. Freehand multichannel implanted stimulation system, approved by the U.S. Food and Drug Administration for control of grasp in spinal cord–311 injured patients. Voluntary shoulder motion detected by the external sensor triggers a stimulation control program that is transmitted to the implanted stimulator312 and routed to epimysial electrodes implanted near the nerve entry zones of various muscles operating the wrist and digits. (From Smith et al., 1998; photograph313 courtesy of the manufacturer, NeuroControl Corp., Cleveland, Ohio.)

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317

318319 Figure 2. BION injectable microstimulators, now in clinical trials of TES to prevent shoulder subluxation due to muscle atrophy following stroke. Each320 implant (2 mm diameter � 16 mm long) receives power and digital command signals from an amplitude-modulated 2 MHz magnetic field created by an321 externally worn controller and transmitter coil. Each command specifies the address of one BION, the stimulus current (0.2–30 mA, in 30 steps), and the322 pulse width (4–514 ls, in 512 steps).323324

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Prosthetics, Motor Control - University of Southern California · PDF fileProsthetics, Motor Control 3 4 Gerald E. Loeb and Ning Lan 5 Introduction 6 This article deals with the subset