186 IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 12, NO. 2, JUNE 2004

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1 186 IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 12, NO. 2, JUNE 2004 Evidence for Improved Muscle Activation Patterns After Retraining of Reaching Movements with the MIME Robotic System in Subjects with Post-Stroke Hemiparesis Peter S. Lum, Member, IEEE, Charles G. Burgar, Member, IEEE, and Peggy C. Shor Abstract Previously, we reported that chronic stroke subjects had signicant improvements in isometric strength, free reaching extent, and clinical evaluations of function after training in the mirror-image movement enabler (MIME) robotic device. Our primary goal in this analysis was to investigate the hypothesis that the robotic training promoted improved muscle activation patterns. To this end, we examined the interaction forces, kinematics, and electromyograms recorded during training of eight dferent movement patterns in active-constrained mode. In this mode, the robot constrained the reaching movements to be toward the target, and the movement velocity was proportional to the force produced along the trajectory. Thirteen chronic stroke subjects trained in MIME for 24 1-h sessions over an eight-week period. Work output was signicantly increased by week five in all eight movement patterns. Low-level subjects increased their extent of reach, while high-level subjects increased their speed. Directional errors in force production were reduced in six of eight movement patterns. Electromyographic data provided evidence for improved muscle activation patterns in the four movement patterns that started at tabletop level and ended at shoulder level. In contrast, there was no evidence of improved muscle activation patterns in any of the tabletop movements, with increased activation of antagonists in two movement patterns. This dichotomy may have been related to compensation at the shoulder girdle during movements that remained at tabletop level. A simple biomechanical model will be introduced to demonstrate the likelihood of this possibility. Index Terms Arm, electromyography (EMG), movement, rehabilitation, robotics, stroke. I. INTRODUCTION SEVERAL research groups are actively developing robotic devices to assist retraining of upper-limb movement after stroke [1]. Extensive clinical testing with the MIT-Manus robot has shown that robotic training can signicantly improve clinical outcomes when used as an adjunct to conventional treatment in acute and subacute patients [2], [3]. Training with the ARMguide device produced measurable benefits in chronic subjects, although similar benefits were observed with unassisted repetitive reaching [4]. Previously, we reported the results of a clinical trial of the mirror-image movement enabler (MIME) robotic device in chronic stroke subjects [5], [6]. The MIME system is composed of a robot manipulator that applies forces to the affected forearm during goal-directed movements. When compared to subjects who received conventional treatment, subjects who received robot training had advantages in both clinical and biomechanical measures. While these results are promising, additional study is needed to determine the physiological changes that the robotic training is producing. In this analysis, we investigated the hypothesis that the robotic training promoted improved muscle activation patterns. To this end, we examined the interaction forces, kinematics, and electromyograms (EMGs) collected during training of eight dferent movement patterns in the active-constrained training mode. The MIME system can deliver four dferent training modes. We chose to analyze data from the active-constrained mode because subjects received this training consistently in each session and performance feedback was provided to motivate maximal performance. To demonstrate the presence of improved muscle activation patterns, we examined changes in maximal work output, directional error in force production, and the corresponding EMG patterns. We evaluated gains in movement extent in low-level subjects, and gains in movement speed in high-level subjects. We also examined the possibility that the repetitive training decreased the passive stfness in the limb, which could contribute to improved active movements. We addressed this hypothesis by analyzing changes over the course of the training period in the work required to move the passive limb. Finally, we compared the relative effectiveness of the eight dferent movement patterns in terms of kinetic, kinematic, and EMG variables. Manuscript received December 6, 2002; revised December 18, This work was supported by the Department of Veterans Affairs under Merit Review Grant B2056RA and Grant B2156T. P. S. Lum is with the Hunter Holmes McGuire VA Medical Center, Richmond, VA USA, and also with Virginia Commonwealth University, Richmond, VA USA ( plum@vcu.edu). C. G. Burgar is with the Physical Medicine and Rehabilitation Service, Central Texas Veterans Health Care System, Temple, TX USA. P. C. Shor is with the Rehabilitation Research and Development Center, VA Palo Alto Health Care System, Palo Alto, CA USA. Digital Object Identier /TNSRE A. MIME Robotic System II. METHODS A complete description of the MIME system has been reported previously [5]. Briefly, subjects were seated in a wheelchair in front of a height-adjustable table. Straps and a contoured seat (Jay Medical) limited torso movement, and the affected limb was strapped to a forearm splint that restricted /04$ IEEE

2 LUM et al.: EVIDENCE FOR IMPROVED MUSCLE ACTIVATION PATTERNS 187 Fig. 1. Picture of the splint, force/torque sensor, and the robot end effector. Also shown is a QuickSTOP device (QS-100, Applied Robotics Inc., Glenville, NY) that stops the robot the interaction torques exceeds a critical level. The black straps serve as mechanical stops to keep the robot in a safe workspace. wrist and hand movement (Fig. 1). A Puma 560 robot manipulator (Staubli Corporation, Duncan, SC) was attached to the splint and applied forces to the limb during reaching movements. The robot s six degrees-of-freedom allowed movements within a large range of positions and orientations in three-dimensional (3-D) space. The forces and torques between the robot and the affected limb were measured by a six-axis sensor (Delta , ATI Industrial Automation, 0.25 N resolution). The MIME system is capable of four modes of robot-assistance [5]. In this analysis, we focus on data collected in the passive and active-constrained modes. In passive mode, the subject relaxed as the robot moved the limb toward a target with a predetermined trajectory. In active-constrained mode, the robot provided a viscous resistance in the direction of the desired movement and spring-like restoring forces in all other directions as the subject attempted to reach toward the target with maximal effort. Each subject s treatment sessions varied depending on the subject s level and progress; however, these two modes were consistently delivered to each subject in each session. B. Subjects Subject inclusion criteria consisted of a diagnosis of a single cerebrovascular accident (CVA) more than six months ago. Subjects had completed all formal outpatient therapy but continued with any home-based exercise regime or community-based stroke programs they were enrolled in at the time of intake into the study. Subjects were excluded from the study they exhibited any upper extremity joint pain or range-of-motion limitations that would limit their ability to complete the protocols. Subjects with any unstable cardiovascular, orthopedic, or neurological conditions were also excluded. Thirteen subjects completed the study. These subjects had a mean age of years and mean chronicity of months. Nine subjects had right hemiparesis and four had left hemiparesis. Twelve of the 13 subjects were male. Mean score on the Fugl Meyer test of motor function was Fig. 2. Top view of the four tabletop movement patterns. Thin lines represent the start positions, and thick lines are the final positions. The shoulder level versions of these movements started at tabletop level and end with the hand at shoulder level [7]. Mean score on the functional independence measure was [8]. Mean score on the sensation portion of the Fugl Meyer test was C. Procedures Over a two-month period, subjects received 24 1-h treatment sessions supervised by a single occupational therapist. Each session was composed of 50 min of robot-assisted movement, and 5 min of tone normalization and limb positioning before and after the robot treatment. All protocols were approved by the local institutional review committee and informed consent was obtained from all subjects. In the robotic training, emphasis was placed on targeted reaching movements that started close to the body and ended further away (Fig. 2). Therefore, elbow extension was a component of all movements. Four point-to-point reaching directions were trained: forward-medial (shoulder flexion, adduction), directly forward (shoulder flexion), forward-lateral (shoulder flexion, abduction, external rotation), and directly lateral (abduction, external rotation). For each of these four directions, targets could be located at tabletop

3 188 IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 12, NO. 2, JUNE 2004 Fig. 3. Representative data from a single subject in week one (left panel) and week eight (right panel). The movement was the forward-lateral reach up to shoulder level and the robot was in active-constrained mode. The shaded regions mark sections of the trial where there was no movement toward the target. These regions were not included in the calculations of work and average force directional error. By week eight, this subject had increased levels of force directed toward the target and reductions in force directional error. or shoulder level. These eight targeted reaching movements formed a core set of movements. Subjects practiced all of these movements in each session. All subjects spent approximately 20 min in active-constrained mode in each session. Since the high-level subjects completed the eight movement patterns in a shorter amount of time than low-level subjects, the remainder of the time was used to practice movements to eye-level. Data from the outward reaches were analyzed, while data collected during the return movements were not considered. In each session, movement patterns were repeated three times in passive mode and then two to six times in active-constrained mode. In passive mode, subjects were instructed to relax and to let the robot move the arm. During passive movements, the velocity of the hand was held constant at 8 cm/s. In activeconstrained mode, low-level subjects were instructed to reach as far as possible, and high-level subjects were instructed to move as fast as possible. It may have been possible to train low-level subjects to move faster, but we felt it was more important to focus on extending the range of active reach. Feedback of the fraction of the movement completed, or the time to complete three repetitions was used to track and motivate performance. Movements were kept well within each subject s passive range-of-motion. Physical targets were used to indicate the end point of the reach. In the first and last two training sessions, disposable pediatric electrocardiographic electrodes (model P-4, 2.2-cm diameter, 1-cm spacing; Lead-Lok Inc., Sandpoint, ID) were used to record surface EMG over several muscles. Biceps, triceps (long head), pectoralis major, anterior deltoid, middle deltoid, posterior deltoid, and infraspinatus were all monitored. Electrode placement was in accordance with recommendations by Perotto et al. [9]. Edge-to-edge spacing of electrodes was set at 1 cm. An eight-channel Viking IIe EMG system (Nicolet Biomedical; Madison, WI) was used to amply and view the EMG traces during the testing. EMG data was bandpass filtered between 20 and 500 Hz and collected at 1000 Hz. Because of technical problems, EMG data was available for only 11 of 13 subjects. D. Data Reduction and Analysis The robot s joint encoders and the force/torque sensor were sampled at 200 Hz. Standard algorithms converted these data into a fixed x-y-z coordinate frame. Fig. 3 illustrates typical data during an active-constrained reach. To calculate work, the force component in the direction of movement was calculated at each sample point and multiplied by the distance moved toward the target since the last sample point. This quantity was summed over the entire trajectory to calculate the work done (1) work (1)

4 LUM et al.: EVIDENCE FOR IMPROVED MUSCLE ACTIVATION PATTERNS 189 where force vector; unit vector in the direction of movement; distance between the start position of the hand and the position at sample point. To calculate the average force directional error (FDE) for a movement, the angle between the force vector recorded by the sensor and the vector aligned with the direction of movement was calculated at each sample point and averaged across the trajectory [10]. Since subjects often rested between efforts within a single active-constrained reach (Fig. 3, left column), the averaging was only performed over periods when there was movement toward the target. Hence, the largest possible FDE was 90. To compensate for increased movement extent over the course of the sessions, the minimum distance that a subject achieved was determined for all trials of a given movement pattern. The FDE was calculated over this minimum distance for all of the subject s trials even in later sessions the subject could move much farther (2) FDE Additional analysis of the kinematic data was performed after separating the subjects into a low and a high-level group. Seven subjects were classied as low-level based on their inability to complete four or more movement patterns in active-constrained mode, and the remaining six subjects were classied as highlevel. Kinematic data from the low-level group was analyzed for extent increases over the course of the training, while data from the high-level group was analyzed for decreases in movement time. At intake, scores on the Fugl Meyer test of motor function were for the low-level group and for the high-level group. Scores on the functional independence measure were for the low-level group and for the high-level group. The two groups were statistically dferent on the Fugl Meyer (t-test, ) but no dferent on the functional independence measure. Metrics from repeated trials were averaged to provide a metric that represented performance for that session. In some cases, subjects performed two movements in early sessions and more repetitions in later sessions. In these cases, only the first two movements were considered in the later sessions. In high-level subjects, warm-up trials in active-constrained mode preceded trials under the fast as possible instruction. These warm-up trials were not included in the analysis. In order to minimize the effects of day-to-day variability, performance metrics for all sessions in a week were averaged to yield a weekly assessment of performance. Across-subject statistical analysis of weekly performance changes was performed using repeated-measures analysis of variance (rm-anova), with week number entered as the within-subject factor. Following a signicant within-subject effect in the rm-anova, post hoc contrast analysis compared performance in week one to performance in subsequent weeks. This contrast analysis (2) consisted of a series of paired t-tests, with a Bonferoni correction for multiple comparisons. All data sets passed the Kolmogorov Smirnov test of normality. To calculate the average EMG amplitudes during a movement, the root-mean-square (RMS) EMG level was calculated over the periods of time during which there was movement toward the target. In some low-level subjects, there were periods of EMG activity but no movement. These periods of activity were also included in the RMS average for the trial. To determine these periods, the EMG data was divided into 100-ms windows. A window was included in the overall averaging in any of the seven muscles, the RMS EMG level within the window was greater than three times the RMS EMG level recorded during the corresponding passive movement. Electrocardiographic artacts were removed from the data using a method that has been described elsewhere [11]. Values from repeated trials were averaged to provide a value representative of the session. EMG data was analyzed with rm-anova, with the session number entered as the within-subject factor. Post hoc contrasts compared the mean EMG level in the first two sessions with the mean level in the last two sessions. III. RESULTS When averaged across all subjects, there was an increase in work output during movements in active-constrained mode, while the work required to move the limb passively did not change (Fig. 4). In active-constrained movements, rm-anova found a signicant change in work output over the eight-week training period for all eight patterns Post hoc contrasts found that work output was signicantly increased as early as week three in five of eight movements (tabletop level: forward, forward-lateral, forward-medial; shoulder level: forward, forward-medial), while work was signicantly increased in all movements by week five In four of eight movement patterns (tabletop level: forward-lateral, lateral; shoulder level: forward-lateral, lateral), there were signicant gains at week eight relative to week four, indicating that there were continued gains in the second month of treatment. The work more than doubled in two movement patterns (forward shoulder level, forward-lateral tabletop) and the mean increase was 2.8 Nm. There was no correlation between improved work output and Fugl Meyer scores at intake into the study. For all movements, rm-anova found that there was no signicant change over the eight-week period in the work required to move the limb passively. The subgroup of low-level subjects increased their extent of reach with training. Two patterns of improvement were observed. For the subject depicted in the left column of Fig. 5, no movement was possible in week one, but partial completion was possible at week eight. Agonist muscles (triceps, middle deltoid) that were only weakly activated in week one were activated more strongly by week eight. This subject also showed a marked decrease in antagonistic activity in biceps. For the subject depicted in the right column of Fig. 5, agonist muscles (triceps, middle deltoid) were activated in week one, resulting in partial completion of the movement. However, beyond a certain arm posture, activation of agonists was impaired. At week

5 190 IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 12, NO. 2, JUNE 2004 Fig. 4. Group averages of the work in two movement patterns during active-constrained and passive movements. A solid symbol indicates that the average work per trial during that week was signicantly dferent from week one (post hoc contrast, P <0.05). Active work was signicantly increased by week three in both movement patterns. Fig. 5. Kinematics and EMG in two low-level subjects during the forward-lateral movement to shoulder level. (Left column) For this subject in week one, no movement was possible, and biceps was activated. By week eight, the movement was partially completed with increased EMG amplitude in triceps and middle deltoid. (Right column) For this subject in week one, activation of triceps and middle deltoid was possible but only up to a certain arm posture. By week eight, activation of these agonists was possible over nearly the entire movement range but in distinct bursts. eight, activation of these agonists was possible over nearly the entire movement range but in distinct bursts. RM-ANOVA found that extent was signicantly increased in five of eight movement patterns Signicant increases in the shoulder level patterns were 8.8 cm for forward, 11.4 cm for forward-lateral, and 10.0 cm for lateral. Signicant increases in the tabletop patterns were 12.4 cm for forward-lateral and 16.5 cm for lateral. There was no improvement in the forward tabletop or the forward-medial movement to shoulder level. All subjects could complete the forward-medial tabletop movement at week one. High-level subjects increased their speed of movement with training. When considering this subgroup of six subjects, rm-anova found that movement time was decreased signicantly over the course of the eight-week training period in all but the lateral movement pattern By week eight, average reductions ranged from 22% to 45%. Two patterns of improvement are illustrated in Fig. 6. The subject in the left column of Fig. 6 had dficulty maintaining force in the direction of movement and velocity in the second and third repetition. The subject in the right column of Fig. 6 had dficulty maintaining a smooth trajectory. In both subjects, improved speed and smoothness were apparent in all three repetitions after training. The FDE metric had signicant changes over the course of the eight-week training period in both the passive and active-constrained modes. In active-constrained mode, signicant reductions in FDE were present in all of the shoulder level movements and two tabletop patterns (forward-lateral and forward-medial) Decreases ranged from 7.8 to 14.6, with an average decrease across movements patterns of In passive mode, signicant reductions in FDE were present in the forward-lateral tabletop and the forward shoulder level movements. For these passive movements, a reduction in FDE implies a reduction in the end point forces orthogonal to the movement direction. Since there were no changes in the work metric for any of the passive movements, forces along the direction of movement did not change signicantly and were not the cause of the changes in FDE. Analysis of the EMG data during the passive mode movements indicated that in many cases, low levels of activation were present in several muscles. Thus, it was not possible to discount the possibility that muscle activations contributed to the interaction forces and torques recorded during the passive movements. For active-constrained mode, Table I summarizes the signicant

6 LUM et al.: EVIDENCE FOR IMPROVED MUSCLE ACTIVATION PATTERNS 191 Fig. 6. Patterns of improvement in two high-level subjects in the forward-lateral movement up to shoulder level. The top row is hand position and the second row is the force in the direction of movement. In week one, the subject in the left column had dficulty maintaining velocity in the second and third repetition, while the subject in the right column had dficulty maintaining a smooth trajectory. In week eight, both subjects produced three repetitions with improved speed and smoothness. TABLE I EMG AMPLITUDE INCREASES AFTER TRAINING across-subject changes in EMG amplitudes in the last two sessions compared with the first two sessions. In all shoulder level movement patterns, there was increased EMG amplitude in at least one agonist muscle These changes were consistent with the gains in work output depicted in Fig. 4. In contrast, in the tabletop movement patterns, there were two cases

7 192 IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 12, NO. 2, JUNE 2004 of increased EMG amplitude in antagonist muscles 0.05 but no cases of increased EMG amplitude in agonists. There were no signicant decreases in antagonist activity in any of the movement patterns. IV. DISCUSSION AND CONCLUSION These results are consistent with our previous reports of signicant gains in upper limb function, strength, and free reaching extent due to robotic training with MIME [5]. These results are also consistent with a previous study with MIT-Manus that reported similar gains in chronic stroke subjects who received progressive resistance exercise, which is similar to the active-constrained mode used in this study [12]. This analysis extends previous reports by providing evidence of physiological change in the form of improved muscle activation patterns during activeconstrained training. The reductions in FDE, rapid increases in work output, and increased agonist EMG amplitudes provide positive evidence for improved muscle activation patterns. Since subjects spent only one third of the treatment hour in active-constrained mode, the activities in the remaining two thirds of the hour could have contributed to the performance gains we observed during active-constrained movements. For example, it is possible that the effectiveness of active-constrained mode was enhanced by the other robotic modes and the 10 min of tone normalization and positioning provided by the therapist. However, it is unlikely that that primary stimulus for the gains was the tone normalization provided by the therapist or the passive robot mode. These therapies target reduction of passive stfness in the limb, and the work required to move the passive limb did not change over the eight-week period. Thus, the gains were not due to a reduction of limb stfness or spasticity. The robotic training may have produced a type of neural adaptation similar to what is observed in the early stages of strength training. Active-constrained mode is a form of strength training, and subjects spent approximately 20 min each day on resisted, maximal effort movements. Improvements were signicant by week three in five movements, and by week five, there was signicant improvement in all movements. In shoulder-level movements, EMG amplitudes were increased in several agonist muscles. Rapid increases in maximum work output accompanied by increased EMG amplitudes have been observed in numerous studies of high-resistance strength training. These rapid gains in the first month of training are predominantly due to neural adaptation and not muscle hypertrophy. This was first demonstrated with surface EMG [13] and later with T2-weighted magnetic resonance imaging [14]. Furthermore, neural adaptation has been reported during strength training in patients with other types of neuromuscular disorders [15], [16]. While the mechanisms which underlie neural adaptation are unclear (see [17] for review), the result is increased force output from muscles accompanied by increased amplitude of surface EMG. Both of these positive changes were apparent in our data, supporting the hypothesis that neural adaptation resulted from the robotic training. There were increased agonist EMG amplitudes in all shoulder level movements but increased antagonist EMG amplitudes in two of four tabletop movement patterns. However, work Fig. 7. Compensation with the shoulder girdle in the forward movement at tabletop level. Combined protraction and depression produces forces that are transmitted through the humerus and elbow to the force sensor. output was increased in all the movements. We hypothesize that compensation with shoulder girdle movement contributed to the work increases during tabletop movements and compromised the effectiveness of the tabletop movement patterns for producing neuro-rehabilitation. The increased activation of antagonist muscles in the tabletop patterns may have been an attempt to increase stfness at the glenohumeral and elbow joints in order to increase the effectiveness of the compensation. After stroke, compensation with trunk and shoulder girdle movement is a common strategy during reaching [18]. The effectiveness of trunk movement was limited by restraints; however, the shoulder girdle was free to contribute to the reaching movements. If subjects could improve performance during the training with compensation, the task no longer required activation of the target muscle groups at the elbow and glenohumeral joints, and improvement in these areas would not be expected. This hypothesis is consistent with evidence that restraint of the trunk and shoulder girdle can increase the active range of elbow extension and shoulder flexion during reaching in stroke subjects [19]. While compensation may have played a role, it is unlikely that it produced all of the increased work output since this subject group also had signicant improvements in maximal torque output when individual joints were isolated [5]. In future studies of robot-assisted movement, care should be taken to choose movements and modes of training that limit the effectiveness of compensation and requires activation of the target muscle groups. A simple biomechanical model can explain why shoulder girdle compensation can compromise the effectiveness of the tabletop movements but not the shoulder level movements. Consider the simple tabletop movement depicted in Fig. 7. A positive force at the sensor aligned with the direction of movement is all that is required to move. Normally, this would be done with shoulder flexion and elbow extension. However, positive force can be generated at the sensor with shoulder girdle and trunk movement. Since the forearm is not free to rotate about an axis orthogonal to the plane of movement, shoulder protraction and depression can produce forces along the length of the humerus, which are then transmitted through the elbow to the forearm and the sensor. Thus, movement can occur without torque at the gleno-humeral or elbow joints. These compensatory forces must be larger than the negative forces at the sensor due to gravity

8 LUM et al.: EVIDENCE FOR IMPROVED MUSCLE ACTIVATION PATTERNS 193 loading of the limb, but for tabletop movements, these gravity forces are small. In shoulder level movements, force can also be transmitted to the sensor with shoulder girdle movement, but now, these compensatory forces must overcome much larger negative forces at the sensor due to gravity acting on the limb. This is apparent when considering that the work required to move the passive limb in the forward shoulder-level movement was approximately four times larger than the work required in the forward tabletop movement (Fig. 4). One of the primary advantages of active-constrained mode is that it reduces task dficulty by allowing practice of reaching movements with certain aspects of the task removed. For example, in low-level subjects, active-constrained mode removes the time-critical aspects of the movement. Throughout the movement, the arm is supported in position until force is produced in the desired direction. Thus, subjects typically moved up a repeatable point in the trajectory and stopped either due to inability to maintain contractions [20], inability to activate muscles in that posture [11], [21], [22], increasing gravity loading and passive limb stfness [23], hyperexcitable stretch reflexes [24], [25], co-contraction of antagonists [26], [27], or impaired interjoint coordination [28], [29]. As the robot supported the limb at that posture, subjects rested and then attempted to move again, usually managing to move closer to the target. This cycle of attempts and rest could last well over a minute per movement. Thus, the time-critical aspects of the reaching movement were removed and subjects could practice force generation in limb postures that could not normally be attained without external assistance. If subjects were to attempt the same reach without the robot, there would be a partial movement toward the target, then the limb would fall back to the table. This hypothesis is consistent with evidence that providing support for a limb against gravity can improve performance of movements in the horizontal plane [30], [31]. Once the subject can achieve the target with a series of attempts separated by rest, the time-critical aspect of the reach is then returned to the task by instructing subjects to move as fast as possible and providing feedback of movement time. Subjects are then motivated to blend the bursts together, and eventually, the timing and amplitude of the muscle activations would approach those needed for free reaching movements. Active-constrained mode also partially reduces the effects of limb stfness and inertia. In free reaching, limb stfness can produce movements of the hand orthogonal to the path. In active-constrained mode, the robot prevents movement in these directions. In free reaching, active torques at the joints produce accelerations in proportion to the inertia of the limb segments. In active-constrained mode, the viscous resistance to movement maintains accelerations at a lower level than in free movements, thereby reducing the effects of limb inertia. All of these changes serve to make the reaching task less dficult and allow practicing subcomponents of the task in isolation. As the training proceeds, these aspects of the movement can be returned to the task incrementally. For example, the impedance of the end point can be increased, allowing greater deflections of the hand away from the path and requiring subjects to control forces orthogonal to the path. The viscous resistance to movement can be reduced, allowing for higher accelerations and larger inertial forces. Compared to the large work increases, the decreases in FDE were signicant but modest in size. This might be explained by the method by which the metric was calculated. Even a subject increased movement extent during the sessions, the FDE metric was only calculated over the portion of the movement that was completed initially. Therefore, in many cases, the movement distances were only a few centimeters. Because of this limitation, extent of movement is a more useful metric for low-level subjects. In high-level subjects, FDE was also only modestly improved. This might be explained by the fact that movement time was the feedback parameter used to motivate increases in performance, and no indication of FDE was provided to the subjects during the training. Thus, high-level subjects had impressive improvements in movement speed but only modest improvements in FDE. Thus, the dferences in protocol between low and high-level subjects was a major factor in the types of improvement observed. It is possible that the same results could have been achieved with Constraint-Induced (CI) Movement therapy [32]. However, there are distinct dferences between CI therapy and robotic therapy that suggest that they are not interchangeable. CI treatment has been found to be highly effective in mild or moderately impaired subjects [33], [34]. The treatment involves repeating an unassisted task as many times as possible within a certain time period. In low-level subjects that cannot complete the task, the CI therapy approach would be to choose tasks that are easier. The robotic approach is to allow practice and completion of the desired task (e.g., reaching) by applying physical guidance and assistance. Thus, we believe robotic treatment might be best suited for low and moderate-level subjects or in acute and subacute subjects as spontaneous recovery is proceeding. The most effective outcomes might result from a combined use of robotic and CI therapy. Robotic treatment would be an effective means of training subjects to recover greater levels of physical motor ability. After a certain level of motor ability was achieved, CI therapy would be an effective method of translating this ability into functional use of the limb outside of the laboratory environment. ACKNOWLEDGMENT The authors would like to thank the subjects who so generously volunteered their time and the staff members at the Rehabilitation Research and Development Center, VA Palo Alto Health Care System, for their technical support. REFERENCES [1] P. S. Lum, D. J. Reinkensmeyer, R. Mahoney, W. Z. Rymer, and C. G. Burgar, Clinical considerations in the use of robotic devices for movement therapy following stroke, Top. Stroke Rehab., vol. 8, no. 4, pp , [2] B. T. Volpe, H. I. Krebs, N. Hogan, O.T. R. L. Edelstein, C. Diels, and M. Aisen, A novel approach to stroke rehabilitation: Robot-aided sensorimotor stimulation, Neurology, vol. 54, no. 10, pp , [3] H. I. Krebs, B. T. Volpe, M. L. Aisen, and N. Hogan, Increasing productivity and quality of care: Robot-aided neuro-rehabilitation, J. 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9 194 IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 12, NO. 2, JUNE 2004 [5] P. S. Lum, C. G. Burgar, P. C. Shor, M. Majmundar, and M. Van der Loos, Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke, Arch. Phys. Med. Rehab., vol. 83, no. 7, pp , [6] C. G. Burgar, P. S. Lum, P. C. Shor, and H. F. M. Van der Loos, Development of robots for rehabilitation therapy: The Palo Alto VA/Stanford experience, J. Rehab. Res. Dev., vol. 37, no. 6, pp , [7] A. R. Fugl-Meyer, L. Jaasko, I. Leyman, S. Olsson, and S. Steglind, The post-stroke hemiplegic patient. 1. A method for evaluation of physical performance, Scand. J. Rehab. Med., vol. 7, no. 1, pp , [8] B. B. Hamilton, J. A. Laughlin, R. C. Fiedler, and C. V. Granger, Interrater reliability of the 7-level functional independence measure, Scand. J. Rehab. Med., vol. 26, no. 3, pp , [9] A. Perotto and E. F. Delagi, Anatomical Guide for the Electromyographer: The Limbs and Trunk, third ed. Springfield, IL: Charles. S. Thomas, 1996, pp [10] P. S. Lum, C. G. Burgar, D. E. Kenney, and H. F. Van der Loos, Quantication of force abnormalities during passive and active-assisted upper-limb reaching movements in post-stroke hemiparesis, IEEE Trans. Biomed. Eng., vol. 46, pp , June [11] P. S. Lum, C. G. Burgar, and P. C. Shor, Evidence for strength imbalances as a signicant contributor to abnormal synergies in hemiparetic subjects, Muscle Nerve., vol. 27, no. 2, pp , [12] S. E. Fasoli, H. I. Krebs, J. Stein, W. R. Frontera, and N. Hogan, Effects of robotic therapy on motor impairment and recovery in chronic stroke, Arch. Phys. Med. Rehab., vol. 84, no. 4, pp , [13] T. Moritani and H. A. devries, Neural factors versus hypertrophy in the time course of muscle strength gain, Amer. J. Phys. Med., vol. 58, no. 3, pp , [14] H. Akima, H. Takahashi, S. Y. Kuno, K. Masuda, T. Masuda, H. Shimojo, I. Anno, Y. 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Levin, Effect of trunk restraint on the recovery of reaching movements in hemiparetic patients, Stroke, vol. 32, no. 8, pp , [20] M. C. Hammond, G. H. Kraft, and S. S. Fitts, Recruitment and termination of electromyographic activity in the hemiparetic forearm, Arch. Phys. Med. Rehab., vol. 69, no. 2, pp , [21] C. Gowland, H. de Bruin, J. V. Basmajian, N. Plews, and I. Burcea, Agonist and antagonist activity during voluntary upper-limb movement in patients with stroke, Phys. Ther., vol. 72, no. 9, pp , [22] L. Ada, C. Canning, and T. Dwyer, Effect of muscle length on strength and dexterity after stroke, Clin. Rehab., vol. 14, no. 1, pp , [23] N. J. O Dwyer, L. Ada, and P. D. Neilson, Spasticity and muscle contracture following stroke, Brain, pt. 5, vol. 119, pp , [24] D. M. Corcos, G. L. Gottlieb, R. D. Penn, B. Myklebust, and G. C. Agarwal, Movement deficits caused by hyperexcitable stretch reflexes in spastic humans, Brain, pt. 5, vol. 109, pp , [25] B. D. Schmit and W. Z. Rymer, Identication of static and dynamic components of reflex sensitivity in spastic elbow flexors using a muscle activation model, Ann. Biomed. Eng., vol. 29, no. 4, pp , [26] J. M. Gracies, L. Wilson, S. C. Gandevia, and D. Burke, Stretched position of spastic muscles aggravates their cocontraction in hemiparetic patients, Ann. Neurol., vol. 42, pp , [27] E. Knutsson and A. Martensson, Dynamic motor capacity in spastic paresis and its relation to prime mover dysfunction, spastic reflexes and antagonist co-activation, Scand. J. Rehab. Med., vol. 12, no. 3, pp , [28] M. F. Levin, Interjoint coordination during pointing movements is disrupted in spastic hemiparesis, Brain, pt. 1, vol. 119, pp , [29] R. F. Beer, J. P. Dewald, and W. Z. Rymer, Deficits in the coordination of multijoint arm movements in patients with hemiparesis: Evidence for disturbed control of limb dynamics, Exp. Brain. Res., vol. 131, no. 3, pp , [30] B. Bobath, Adult Hemiplegia: Evaluation and Treatment, third ed. Oxford, U.K.: Butterworth Heinemann, [31] R. F. Beer, J. D. Given, and J. P. Dewald, Task-dependent weakness at the elbow in patients with hemiparesis, Arch. Phys. Med. Rehab., vol. 80, no. 7, pp , [32] E. Taub, N. E. Miller, T. A. Novack, E. W. Cook 3rd, W. C. Fleming, C. S. Nepomuceno, J. S. Connell, and J. E. Crago, Technique to improve chronic motor deficit after stroke, Arch. Phys. Med. Rehab., vol. 74, pp , [33] E. Taub, G. Uswatte, and R. Pidikiti, Constraint-induced movement therapy: A new family of techniques with broad application to physical rehabilitation A clinical review, J. Rehab. Res. Dev., vol. 36, pp , [34] J. H. van der Lee, R. C. Wagenaar, G. J. Lankhorst, T. W. Vogelaar, W. L. Deville, and L. M. Bouter, Forced use of the upper extremity in chronic stroke patients: Results from a single-blind randomized clinical trial, Stroke, vol. 30, no. 11, pp , Peter S. Lum (M 03) was born in Washington, DC. He received the B.S. degree in mechanical engineering from George Washington University, Washington, DC, in 1987, the M.S. degree in applied mechanics from the Calornia Institute of Technology, Pasadena, CA, in 1988, and the Ph.D. degree in bioengineering from the University of Calornia at Berkeley and San Francisco, in From 1994 to 2002, he was a Research Biomedical Engineer with the Department of Veterans Affairs Rehabilitation Research and Development Center, Palo Alto, CA. He is currently an Associate Professor of biomedical engineering at Virginia Commonwealth University, Richmond, VA. He is also currently a Research Scientist at the Hunter Holmes McGuire Veterans Affairs Medical Center, Richmond, VA. His research interests include understanding the motor control principles that underlie human movement, and development of novel devices and treatments for rehabilitation of motor function following neurological injury. Charles G. Burgar (S 71 M 74) received the B.S.E.E. degree from the University of Texas at Austin, in 1974 and the M.D. degree from the University of Texas Health Science Center, San Antonio, in After receiving the M.D. degree, he performed his residency training at the University of Texas Affiliated Teaching Hospitals. He served as Medical Director of the VA Rehabilitation Research and Development Center, Palo Alto, CA, from 1993 to He is currently Chief of Physical Medicine and Rehabilitation at the VA Central Texas Health Care System, Temple, TX. His research interests include functional recovery following stroke and robotic applications in rehabilitation. Dr. Burgar is a Fellow of the American Academy of Physical Medicine and Rehabilitation and of the American Association of Electrodiagnostic Medicine. He is also a registered Professional Engineer and a Diplomate of the American Board of Physical Medicine and Rehabilitation and of the American Board of Electrodiagnostic Medicine. Peggy C. Shor received the B.S. degree in occupational therapy from San Jose State University, San Jose, CA, in She is currently a Research Occupational Therapist working on projects focused on exploring robot-assisted neurorehabilitation at the Palo Alto VA Rehabilitation R&D Center, Palo Alto, CA. Her research interests include human services and robotic rehabilitation and the effectiveness of neuro-developmental treatment techniques for persons with adult hemiplegia.