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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 13, NO. 3, SEPTEMBER 2005 325

Customized Interactive Robotic Treatment for Stroke:EMG-Triggered Therapy

Laura Dipietro, Mark Ferraro, Jerome Joseph Palazzolo, Hermano Igo Krebs, Member, IEEE, Bruce T. Volpe,and Neville Hogan

Abstract—A system for electromyographic (EMG) triggering ofrobot-assisted therapy (dubbed the EMG game) for stroke patientsis presented. The onset of a patient’s attempt to move is detected bymonitoring EMG in selected muscles, whereupon the robot assistsher or him to perform point-to-point movements in a horizontalplane. Besides delivering customized robot-assisted therapy, thesystem can record signals that may be useful to better understandthe process of recovery from stroke. Preliminary experimentsaimed at testing the proposed system and gaining insight into thepotential of EMG-triggered, robot-assisted therapy are reported.

Index Terms—Myoelectric control, robot-assisted rehabilitation,stroke.

I. INTRODUCTION

STROKE is a leading cause of disability worldwide. Everyyear, over 600 000 people suffer from stroke in the U.S.

alone, and about 80% of acute stroke survivors lose motor skillsof the arm and the hand [1], [2]. A recent innovation in reha-bilitation of stroke patients is robot-assisted therapy. Several re-search studies have shown that intensive goal-directed repeti-tion of movement promotes recovery following a stroke [3]–[5].Robots can perform repetitive tasks consistently and control-lably. They are also equipped with sensors and can record po-sition, velocity, and force exerted by the patient, which may beused to measure patient’s motor performance quantitatively andobjectively.

Several rehabilitation robots have been proposed so far.Examples include MIT-MANUS [6], ARM Guide [7], andMIME [8]. Clinical effectiveness was reported in several

Manuscript received January 13, 2004; revised November 19, 2004; acceptedJanuary 17, 2005. This work was supported by in part by the National Institute ofChild Health and Human Development under Grant R01-HD-37397 and GrantR01-HD-045343 and in part by the Burke Medical Research Institute.

L. Dipietro and J. J. Palazzolo are with the Newman Laboratory for Biome-chanics and Human Rehabilitation, Department of Mechanical Engineering,Massachusetts Institute of Technology, Cambridge, MA 02139 USA (e-mail:[email protected]).

M. Ferraro is with the Department of Neurology and Neuroscience, BurkeMedical Research Institute, Weill Medical College, Cornell University, WhitePlains, NY 10605 USA.

H.I. Krebs and B.T. Volpe are with the Newman Laboratory for Biomechanicsand Human Rehabilitation, Department of Mechanical Engineering, Massachu-setts Institute of Technology, Cambridge, MA 02139 USA, and also with theDepartment of Neurology and Neuroscience, Burke Medical Research Institute,Weill Medical College, Cornell University, White Plains, NY 10605 USA.

N. Hogan is with the Newman Laboratory for Biomechanics and Human Re-habilitation, the Departments of Mechanical Engineering and Brain and Cog-nitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139USA.

Digital Object Identifier 10.1109/TNSRE.2005.850423

Fig. 1. (A) Patient during robotic therapy. (B) Display of the typical game inrobotic therapy. Eight targets in directions north (N), north-east (NE), east (E),south-east (SE), south (S), south-west (SW), west (W), and north-west (NW)are shown on a videoscreen. Patient is required to move the end-effector of therobot in the horizontal plane from the central target to the eight outer targets.Position of the end-effector in the plane is displayed on the screen by a smallyellow cursor. Distance between the central target and each outer target positionis 0.14 m.

studies; patients who received robot-assisted therapy showedgreater recovery than those who received sham exposure torobot therapy or a matched amount of traditional occupationaltherapy [9]–[14]. The potential advantages have stimulated anexpanding array of research effort using customized robot-as-sisted therapy. Adaptive robot therapy was recently proposedand implemented on MIT-MANUS [15], [16]. The patient isasked to move the end-effector of the robot while playing asimple video-game (see Fig. 1). The robot provides assistanceto complete the movements and the degree of assistance varies

1534-4320/$20.00 © 2005 IEEE

326 IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 13, NO. 3, SEPTEMBER 2005

with the patient’s motor abilities. The patient’s efforts are de-tected by monitoring when the speed of the robot end-effectorexceeds a certain threshold1.

We investigated whether it would be feasible to use elec-tromyographic (EMG) signals, i.e., muscular electrical activity,to trigger the assistance provided by the robot. Using the EMGto trigger the robot action may offer the following advantages.

1) It would allow robotic therapy to be customized, e.g.,through selection of specific muscle(s) to trigger therobot. This could be used to train specific muscles ofpatients according to their needs.

2) It would provide a means to verify that patients areactually attempting to generate movements duringrobotic therapy (e.g., rather than engaging their trunkto generate movements to exceed the speed threshold)2.

3) It may trigger the robot earlier than triggering based onkinematic signals3.

4) It may allow highly-impaired subjects to activate robotassistance; such patients might be able to generateEMG signals even though they were unable to producesufficient movement to trigger the robot.

5) It may provide data critical to understanding theprocess of recovery from stroke, and additional mea-sures of patient recovery, i.e., parameters derivedfrom EMG signals may be used to quantitatively andobjectively assess patient’s motor abilities.

While the use of EMG to control machines has been investigatedextensively, the use of EMG in robot-assisted therapy has so farbeen limited to monitoring pre-treatment versus post-treatmentchanges in muscle activation [22]–[24] and to confirm muscleactivity or inactivity in patients undergoing treatment [7], [25].To the best of our knowledge, the use of EMG for interactive andcustomized robot-assisted therapy has not yet been explored.

II. BACKGROUND: MYOELECTRIC CONTROL

In myoelectric control, EMG is processed to generate a signalwhich is fed into a control system. Two classes of control sig-nals are commonly derived from EMG: 1) continuous signals:typically a measure of EMG amplitude, presumably related tomuscle exertion, is used to control continuous variables, such asthe speed of prosthesis motion [26]–[33] and 2) discrete signals:typically the output of a pattern recognition processor that ana-lyzes multichannel inputs is used to distinguish different classesof limb motion, such as different gestures [34]. Historically, re-habilitation engineering has been the earliest field of applicationfor myoelectric control. The main application, explored sinceWiener’s Cybernetics in the late 1940s [35], is control of arm

1If the patient is not able to move the end-effector within a given time (typi-cally 2 s), the robot will start providing assistance.

2Voluntary physical exercise is considered to be a key factor in promotingrecovery after traumatic injury of the central nervous system (CNS) on rats [17]and monkeys [4]. It has been shown that it induces neurogenesis in the adult CNS[18], increases trophic factor production in select regions of the brain [19], andcan modulate crucial aspects of plasticity [20].

3Generating faster triggering signals may improve the correlation between apatient’s attempts to move and the robot’s assistance. This may make robotictherapy more effective since the timing of stimulus seems to be important forinduction of plasticity [21].

amputation prostheses [36]–[40]. More recently, tele-operationand virtual reality have used EMG for predicting user’s inten-tions [29], [41]–[45].

In rehabilitation of stroke patients, a recent application isEMG-triggered neuromuscular stimulation, which seeks to pro-mote motor relearning via retraining of voluntary control ofmovement [46]. EMG is recorded from a given muscle, usu-ally the wrist or finger flexor. As soon as a patient voluntarilyattempts to move and EMG exceeds a threshold, a neuromus-cular stimulator assists the movement so that full extension ofthe limb is experienced. A theoretical neurophysiological basisfor clinical effectiveness of this technique [47]–[53] is that al-ternative motor pathways can be recruited and activated to as-sist the stroke-damaged efferent pathways. This explanation isbased on sensorimotor integration theory, which states that sen-sory input from movement of the affected limb directly influ-ences subsequent motor output [47].

In many of the above applications, detection of the onset ofmuscular contractions is the first step for identifying the user’sintentions. Single-threshold detectors [54] are the main class ofalgorithms used for this purpose, although more sophisticatedapproaches have been proposed [55], [56]. In single-thresholddetectors, the onset is the time when the processed EMG signal(typically the EMG envelope) exceeds a threshold, which is se-lected to discriminate the background noise from the componentgenerated by active muscles. While parameters such as band-width of the filter for EMG processing and threshold value caninfluence the sensitivity of the algorithm and processing delay[54], single-threshold detectors are easy to implement; for thisreason, they were chosen for the design of the EMG game.

III. EMG GAME

A. Hardware

The system was implemented using (InteractiveMotion Technologies, Inc., Cambridge, MA), a back-drivabletherapy robot and Bagnoli 4 (Delsys, Inc., Boston, MA) an EMGacquisition system with four differential electrodes.

B. The Control System

The control system is shown in Fig. 2. The control programwas implemented on a personal computer in C++ using the QNXoperating system4.

1) EMG Preprocessing: EMG signals were recorded with again of 1000 V/V and a bandpass of toand digitized with sampling frequency kHz and 16-bitquantization (United Electronics Industries, Inc., Canton, MA).Signals were high-pass filtered (second order Butterworth,cut-off 10 Hz), full-wave rectified, and low-pass filtered toconstruct an estimate of the EMG envelope (moving average,length , where indicates a data acquisitionchannel).

2) Generation of the Control Signal: In the following,the th sample of the processed EMG signals recorded fromeach data acquisition channel ( ) is defined as

4Although we are presently moving to C code using Real-Time Linux.

DIPIETRO et al.: CUSTOMIZED INTERACTIVE ROBOTIC TREATMENT FOR STROKE: EMG-TRIGGERED THERAPY 327

Fig. 2. Triggering algorithm of the EMG game. Given four channels of EMG, each signal was processed separately to identify EMG onset, which generated abinary signal (1 if the muscle had become active, 0 otherwise). Triggering signal TR was generated as the logical OR of the binary signals generated by eachchannel. C is the impedance control algorithm for the robot that generated shoulder (� ) and elbow (� ) torques.

. The robot action was triggered when a robot booleancontrol signal was enabled (became 1).

(1)

where is the logical OR operator and is a binary trig-gering signal associated with each EMG channel, defined as fol-lows:

ifotherwise

(2)where

(3)

(4)

(5)

and is the number of samples used to calculate the parame-ters above (see below). Equations (2)–(5) implement a single-

threshold detector: for each channel , the onset was detectedwhen exceeded (calculated as the mean plustimes the standard deviation of ) for at least con-secutive samples. If onset was not detected in any of the EMGchannels, the robot control signal was kept at 0 and no robotaction was triggered. As soon as onset was detected in any of thefour channels, the robot control signal became 1, therebytriggering robot action.

The performance of the single-threshold detector can bevaried by adjusting the values of parameters , , and[54]. These parameters were tuned to provide satisfactory onsetdetection for this application (e.g., to reject electrocardio-graphic artifacts that affected some of the EMG traces).

3) EMG Game Controller Timing: The controller generatedthe targets on a video-screen, similar to previous robot therapy(see Fig. 1) [9], [10]. Fig. 3 shows the timing of the EMG gamecontroller.

4) Impedance Controller: Once the robot action had beentriggered, the robot impedance controller assisted the patient’sarm movements in the same way as in the adaptive game de-scribed elsewhere [15], [16]. The controller allowed patients


Fig. 3. Timing of the EMG game controller for each time slot, i.e., movementfrom the central target to each outer target and back. Patient’s arm waspositioned in the center of the robot workspace, which corresponds to thecentral target on the videoscreen (see Fig. 1(b), part B). At t , the outertarget was shown to the patient; the controller waited t seconds beforetriggering the robot action if the patient did not attempt to move. Game startedas soon as the patient’s attempt to move was detected. Patient had to reach thetarget position within t seconds; if needed, the robot assisted the patient’sarm to move. After the outer target was reached, the controller waited t

seconds and then showed the patient the central target, which had to be reachedwithin t seconds. After t seconds, the controller showed a new targetin a new direction. For each channel the threshold T (d = 1; . . . ; 4) wascalculated in a time window of length N samples (1 s) before the end of thetime slot. For target North, T was calculated before t ; for the sevenremaining outer targets, T was calculated at the end of the second t time.

capable of movement to reach the targets unassisted, but it pro-vided assistance to patients who could not reach the targets. Theamount of assistance was based on the patient’s performance.

Excessive muscular tone in pathological subjects may induceinvoluntary arm movements that can cause the end-effector ofthe robot to move far from the starting position prior to trig-gering robot action. To prevent abrupt motion of the patient’sarm when the robot was triggered, the robot stiffness was ini-tialized at 150 N/m to ensure gentle arm positioning.

5) Selective Triggering: Different ways to generate thesignal can be implemented by modifying (1). As an ex-ample, let channel 1 record the EMG signal taken from anteriordeltoid (AD) and modify (1) in a target-dependent way asfollows:

for targets (6)

For each , robot action would be triggered only whenexceeds . Such an option could be useful to train muscle ADfor given directions (in this example the directions of targets N,NE, W, NW). Targets and muscles could be chosen in a differentway for each patient to implement personalized therapies.

IV. EXPERIMENTS

Three different experiments were conducted. Experiment Iwas aimed at exploring the characteristics of EMG signals ofstroke patients to better understand the potential of EMG-trig-gered, robotic therapy (robot assistance off). Experiments II andIII were aimed at testing the EMG game under unassisted andassisted conditions (robot assistance off and on, respectively).

Similar to ongoing robot therapy [15], subjects were seated ina comfortable chair in front of the robot, restrainedby a seat-belt with shoulder straps, and had their elbow sup-ported with a padded wooden support attached to the elbow withVelcro straps. They were asked to move the end-effector of therobot in the horizontal plane to targets shown on a video-screenlocated in front of them. Targets were presented sequentially ina clockwise direction, starting from target North (see Fig. 1).The sequence was repeated five times. In the unassisted experi-ments, if a patient was unable to move back to the central target,the therapist gently guided the patient’s arm back to the centerposition.

In all the experiments, electrodes for EMG recording wereplaced by an occupational therapist according to establishedrecommendations [57]. Subjects who gave informed consent toparticipate were tested at Burke Rehabilitation Hospital, WhitePlains, NY. The experiments and informed consent procedureswere approved by the Massachusetts Institute of Technology(MIT) Committee on the Use of Humans as Experimental Sub-jects and the Institutional Review Board of Burke RehabilitationHospital.

A. Experiment I: EMG Exploration

Two unimpaired subjects (unimpaired subjects 1 and 2) andfive stroke patients participated in this experiment (patients1–5). Patients 1 and 2 were out-patients with moderate and mildhemiparesis; patients 3, 4, and 5 were in-patients with severehemiparesis (upper extremity component of the Fugl-Meyer(FM) at admission 18/66, 30/66, 8/66, 0/66, and 4/66, respec-tively). For unimpaired subjects and out-patients, EMG wasrecorded from the following muscles: pectoralis major clav-icularis part (PEC), latissimus dorsi (LAT), triceps long-head(TRIC), middle deltoid (MD), brachioradialis (BRA), teresmajor (TM), infraspinatus/teres minor (INF), anterior deltoid(AD), biceps (BIC), posterior deltoid (PD), upper trapezius(UT), lower trapezius (LT), and middle trapezius (MT). Thesemuscles were chosen because they were expected to becomeactive according to previous studies on unimpaired subjects[58], [59]. Since the Bagnoli 4 records from four channels theprotocol had to be repeated four times in order to gather datafrom all the muscles selected for recording. For in-patients, dueto the tight treatment schedule required by their rehabilitationprograms, EMG was only recorded from a subset of the abovemuscles: PEC, BIC, TRIC, and UT. These muscles were chosenbecause they were expected to be among the muscles displayingthe highest modulation.

B. Experiments II and III: EMG Triggering With Unassistedand Assisted Games

One unimpaired subject (unimpaired subject 3) and twostroke out-patients with severe hemiparesis (patient 6 and 7;upper extremity component of the FM at admission 11/66 andunavailable in medical records) participated in these experi-ments. EMG was recorded from PEC, AD, BIC, and PD. Thesemuscles were chosen because during experiment I, they were


Fig. 4. (A) Recording from patient 5 during a part of trial 4 in experiment I. EMG modulation is evident even when speed is measurably zero (see for exampletime windows [7– 9 s] and [12–18 s]). Speed is composed of several low-amplitude peaks, i.e., the subject generates isolated small movements. Also, increases inspeed do not always correspond to modulation in EMG signals (e.g., see time window [18–20 s]); movements might have been generated by muscles not monitored,or by trunk muscles engaged by the patient in the attempt to move. (B) Plan view. (C) Plan view magnified. Note that the squares labeled 1, 14, correspond to theintervals 1, 14 indicated in the tangential speed plot in (A).

among the muscles that displayed the highest modulation5 andthey were easier to record than other muscles in the 13-musclelist (i.e., easier than UT used in experiment 1 with in-patients).

5During experiment 1, for all directions, among the 13 muscles recorded forout-patients and unimpaired subjects the subset PEC, BIC, UT, MD, BRA, PD,and AD displayed the highest modulation.

V. RESULTS

A. Experiment I

1) Need for Personalized Therapy: All subjects apart frompatient 5 (see below) were able to move toward the outer targetsand back. With each subject, given a movement toward a specific


TABLE IMEAN AND STANDARD DEVIATION OF d1 AND d2 VALUES FOR AD ACROSS THE 5 TRIALS OF EXPERIMENT I. t WAS AUTOMATICALLY CALCULATED WITH

THE SINGLE-THRESHOLD ALGORITHM DESCRIBED IN SECTION III WITH PARAMETERS M = 100, AND P = 120. NOTE THE DIFFERENT BEHAVIOR OF THE

MUSCLE ACROSS DIFFERENT SUBJECTS. NEGATIVE VALUES INDICATE THAT THE EMG ONSET OCCURS BEFORE THE INSTANT THAT v EXCEEDS THE SPEED

THRESHOLD. POSITIVE VALUES, ESPECIALLY IF HIGH SUGGEST THAT MUSCLE ACTION WAS NOT RELATED TO MOVEMENT; OTHER MUSCLES, EITHER EXTERNAL

(E.G., TRUNK MUSCLES) OR INTERNAL TO THE SUBSET RECORDED FOR THE SUBJECT, COULD HAVE GENERATED THE MOVEMENT (SEE TEXT)

target, several muscles were found to be active (EMG displayedmodulation). In unimpaired subjects, muscular activation pat-terns were similar across subjects and consistent with the resultsof previous studies [58]–[60]. In stroke patients, patterns variedhighly across subjects, i.e., patients accomplished similar move-ments by activating different muscles with different timings6.For example, the EMG taken from TRIC in patient 2 showedalmost no modulation in any direction, but showed modulationin patients 1, 3, and 4. In the movement from center to targetNorth, with patient 1, BIC was found to be highly active, whilewith unimpaired subjects, BIC was found to be active mainlyduring movements from target North back to the central target.

These findings suggest that different patients may need tore-learn how to activate different muscles. EMG game mightallow selective training of specific muscles.

2) EMG Triggering in Highly-Impaired Subjects: Fig. 4shows data taken from patient 5. This patient was not capable ofmoving in any direction. However, her EMG data showed somemodulation, which in several cases occurred without movement.In the remaining patients, several muscles presented weakeror slower activation profiles than the corresponding musclesin unimpaired subjects. For example, in movements towardtarget North with patient 1, the AD envelope profile slowlyincreased as movement was performed (while in unimpairedsubjects it rapidly increased before or as soon as movement wasinitiated). However, it was still able to trigger the robot action(see Table I). This shows that highly-impaired patients, whomay be able to activate muscles only weakly or even unable toproduce movement, may still produce EMG signals that cantrigger robot action. This suggests that EMG triggering mightbe particularly useful for highly-impaired subjects.

3) Comparison and Combination of EMG, Velocity, andForce Triggering: Besides EMG, velocity ( ) or force signals

6Stroke affects motor control mechanisms and stroke patients might accom-plish movements by using compensatory muscular synergies [61].

could be used to detect a patient’s attempt to move. We exploredwhether a control signal for triggering the robot action couldbe generated earlier by using EMG signals rather than velocityor force signals.

In order to compare EMG and triggering, we investigatedmovements from center to target North. For each subject’s trialand EMG trace, we calculated the difference ,with the onset of muscular contraction and the in-stant when exceeded a given threshold . As for , weconsidered two values: and

. Accordingly, we calculated the parametersand . Threshold values

and were chosen because they defined the range forthe speed threshold used in the adaptive game [15], [16],which was calculated as follows:

(7)

where is the time allotted by the adaptive game controllerfor the movement of the patient’s arm from the central target toeach of the eight outer targets. This value represents 10% of thepeak velocity of a reaching movement assuming a minimum jerktrajectory [62]. . Also,

corresponds to the resolution for measurements, sinceits value is just above the noise level present in the filtered signal.As for , we considered two signals: and its componentalong the direction pointing to the outer target. was calculatedas follows: , with andthe sampled positions of the end-effector of the robot at time ,

andwith sampling frequency, and obtained by fil-tering and with a low pass-filter (first order, cut-offfrequency 10 Hz). The following values were then calculated:

, ,, and .


TABLE IIRESULTS OF EXPERIMENT II. FOR EACH SUBJECT, COLUMNS REPORT THE FOLLOWING VALUES. COLUMN 2: NUMBER OF TR SIGNALS GENERATED BY THE EMG

GAME ((1)); COLUMN 3-4: NUMBER OF TIMES v EXCEEDED v -v ; COLUMN 5-6: NUMBER OF TIMES v EXCEEDED v -v ; COLUMN 7-10:NUMBER OF TIMES d1 , d2 , d1 , AND d2 WAS NEGATIVE AND AVERAGE VALUE (IN SECONDS).THIS DATA WAS OBTAINED WITH M = M = 100,P = P = 120, � = � = 3 (d = 1; . . . ; 4). THE VALUES WERE CALCULATED ON A TOTAL OF 40 TRIALS AND IN THE INTERVALS [t � t ] OF EACH

TIME SLOT (SEE FIG. 3). TRIGGERING SIGNALS BASED ON v ARE GENERATED MORE EASILY (E.G., COMPARE COLUMN 5 WITH COLUMNS 2 AND 3) AND QUICKLY

(E.G., COMPARE COLUMN 9 WITH COLUMN 7) THAN EMG AND v TRIGGERING SIGNALS

TABLE IIIRESULTS OF EXPERIMENT III. SEE TABLE II FOR DESCRIPTIONS OF COLUMNS. NOTE THAT d1 AND d2 VALUES ARE NOT REPORTED: WHEN EMG ENABLED THE

ROBOT ACTION BEFORE v SIGNALS, v THRESHOLDS WERE LIKELY TO BE EXCEEDED DUE TO ROBOT ASSISTANCE AND NOT TO THE PATIENT MOVEMENT. THIS

DATA WAS OBTAINED WITH M = M = 100, P = P = 180, � = � = 3 (d = 1; . . . ; 4)

For both and , since .since . With each subject

and , values were negative in several different mus-cles and trials. For example, they were negative in more than 1trial for MD, INF, PD, and UT with subject 1; for AD and BICwith subject 2; for INF and AD with patient 1; for AD, BRACH,MD, and INF with patient 2; for TRIC, BIC, and UT with patient3; for TRIC, PEC, BIC, and UT with patient 4. In stroke patients,

negative values of the order of several hundreds of millisec-onds were not uncommon. For example, with patient 3, wasnegative in four cases in TRIC (average value ) and inUT (average value ); similar data was found with patient4 in UT, as well as in several muscles of patient 2. Then, as fortiming, triggering with EMG versus velocity based signals (andvice-versa) might make a difference. As was increased, e.g.,from 1 to 3, the number of times EMG onset was detected beforespeed decreased. The increase of seemed to have a greater ef-fect in patients than in unimpaired subjects. Table I reports and

values for movements performed from center to target Northfor AD, a muscle primarily involved in such movements in unim-pairedsubjects.With and , valueswerenegativein 10 and 5 trials out of 20 (5 trials for each subject), respectively,all differences localized in patients data.

With patient 1, we found negative values for for very fewtrials and muscles. Although strapped to a chair, patients couldhave engaged their trunk to attempt to generate enough move-ment to trigger the robot. While trunk data were not recordedduring our experiments, making it difficult to draw a definiteconclusion, this scenario enlightens a potential advantage ofEMG selective triggering. Triggering with EMG taken fromupper limb muscles might “force” the patient to use the upperlimb to generate movement to trigger the robot.

To increase the chance of generating EMG triggering sig-nals more quickly than speed triggering signals the number ofchannels for (simultaneous) EMG recording could be increased;lower values could also be used to allow weaker activationsto trigger the robot. To increase the chance of triggering therobot as quickly as possible, a hybrid EMG and speed triggeringstrategy could be used. Thus, to allow the fastest signal (eitherEMG or speed) trigger the robot (1) could be modified as:

(8)Force signals were recorded using a 6-DOF ATI US-30-100force transducer (ATI Industrial Automation, Apex, NC). Thetransducer was placed at the end-effector. Force signals becamedistinguishable from the baseline before signals, but afterEMG signals.

B. Experiments II and III

The parameters of the timing of the EMG game controllerwere chosen as follows: s (see Fig. 3). Forexperiment II, was set to 4 s for the patients, so that theyhad enough time to move from the central target to the outertargets or vice-versa, and to 3 s for the unimpaired subject. Forexperiment III, was reduced to 2 s; the impedance con-troller parameters, such as stiffness and damping, were set tothe typical values used in the adaptive game [15], [16].

1) Generation of the Robot Triggering Signal: All subjectswere able to generate signals via their EMG. Results aresummarized in Tables II and III for experiment II and III, re-spectively.

In experiment II, TR signals were generated as follows. Forthe southern targets, they were mainly generated by PD with


unimpaired subject 3, by PEC and AD with patient 6, and byPEC with patient 7; for the northern targets by PEC, AD, and PDwith unimpaired subject 3, by BIC with patient 6 and by PECand BIC with patient 7. For the northern targets, note that withpatients, AD and PEC usually became active, although weaklyand later than BIC, which displayed a strong modulation. Forexperiment III, the number of TR signals generated and the mus-cles that produced them were consistent with the results of ex-periment II.

2) Comparison of EMG and Velocity Triggering: Table IIreports the results of the comparison between and EMG trig-gering for experiment II. Note that with (

) for patient 6 the number of TR signals increased to40. Also, for unimpaired subject 3, patients 6, and 7 the numberof cases where was negative increased to 24, 19, and 23(average values s, s, and s), andwhere was negative to 24, 7, and 12 (average values

, , and ), respectively. Averagepositive values were 0.022 s, 0.276 s, and 0.422 s, and averagepositive values were 0.023 s, 0.320 s, and 0.397 s.

Although the patients generally generated triggeringsignals more easily and quickly than and EMG triggeringsignals, analysis of position data showed that they were onlyable to reach some targets. Specifically they could not reachthe northern targets and, in some cases, targets East and West;when shown these targets, they moved toward southern direc-tions. However, even when patients moved toward the wrongdirections, triggering signals based on were still generated.While triggering signals based on enable robot assistance assoon as any movement is generated, triggering signals based on

ensure that the robot action is enabled only when the patientmoves toward the direction of the target shown (and of robotassistance). With the EMG game, triggering signals based onEMG are generated only when the patient voluntarily activatesthe muscles of the upper-limb, and selective EMG triggeringsignals are generated only when the patient voluntarily activatespreselected muscles when moving toward preselected targets.To correlate assistance to a movement with activation of mus-cles primarily responsible for generating the same movementmight enhance motor recovery from stroke.

3) Effects of Robot Assistance: A comparison of EMG sig-nals of stroke patients recorded during experiment II and IIIshowed that with robot assistance on, the EMG level during therest periods decreased (significant in 6/8 muscles, ).The mean EMG level of stroke patients during moves tended todecrease (significant in 5/8 muscles, ), suggesting thatpatients completed the task with less effort.

VI. CONCLUSION

A system for EMG-triggered, robot-assisted therapy (dubbedthe EMG game) was introduced. The EMG game enables robotaction only when the patient voluntarily initiates a movement;it then assists the patient in completing the movements, therebyproviding sensorimotor feedback. The EMG game can imple-ment customized treatments; its parameters can be adapted totrain specific patient muscles and to deliver robotic treatmenteven when the patient is only able to generate weak bursts of

muscular contractions. While it remains unclear whether EMGallows faster triggering than speed based triggering signals, ex-periment II showed that with speed triggering the direction ofrobot action might be uncorrelated with the direction of themovement generated by the patient. Similarly to EMG-triggeredneuromuscular stimulation, the EMG game can be programmedto trigger the robot action as soon as specific muscles are ac-tivated. The EMG game also records information about a pa-tient’s motor performance, such as which muscles triggered therobot action and at what time instants. This data, together withkinematic signals recorded during the therapy, may be useful tomonitor, quantify, and better understand patient recovery fromstroke. Our future research will be aimed at evaluating its clin-ical effectiveness.

ACKNOWLEDGMENT

Dr. H.I. Krebs and Dr. N. Hogan are co-inventors in the MIT-held patent for the robotic device used in this work. They holdequity positions in Interactive Motion Technologies, Inc., thecompany that manufactures this type of technology under li-cense to MIT.

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Laura Dipietro received the Laurea Degree (summacum laude) in electrical engineering (biomedical cur-riculum) from the University of Florence, Florence,Italy, in 1998 and the Ph.D. degree in biomedicalrobotics from Scuola Superiore S. Anna, Pisa, Italy,in 2003.

She is currently a Postdoctoral Associate atthe Newman Laboratory, Massachusetts Instituteof Technology, Cambridge. Her current researchinterests include robot-assisted neuro-rehabilitation,motor control and learning, and biomedical signal

processing.

Mark Ferraro received the B.S. degree from Quin-nipiac University, Hamdane CT, and did advancedcourse work at the International Academy of Ortho-pedic Medicine, Tucson, AZ, which culminated in theM.S. degree from Texas Tech University, Lubbock,TX.

He was a Senior Occupational Therapist from 1998to 2000, and ran the Robotics Stroke Recovery Unit atthe Burke Medical Research Institute, White Plains,NY, until 2004. He is currently with Aventis.

Jerome Joseph Palazzolo received the B.S. andM.S. degrees in mechanical engineering fromMichigan State University, East Lansing, in 1992and 1994, respectively, and the Ph.D. degree inmechanical engineering from the MassachusettsInstitute of Technology (MIT), Cambridge, in 2005.

He is currently a Postdoctoral Associate at theNewman Laboratory for Biomechanics and HumanRehabilitation, MIT. His research interests are inthe areas of dynamic systems and control, adaptiverobotic therapy/training, and human–machine inter-

actions.

Hermano Igo Krebs (M’91) received the electriciandegree from Escola Tecnica Federal de Sao Paulo,Sao Paulo, Brazil, in 1976, the B.S. and M.S.degrees in naval engineering (option electrical) fromUniversity of Sao Paulo, Sao Paulo, Brazil, in 1980and 1987, respectively, the M.S. degree in oceanengineering from Yokohama National University,Yokohama, Japan, in 1989, and the Ph.D. degree inocean engineering from the Massachusetts Instituteof Technology (MIT), Cambridge, in 1997, with thedissertation entitled “Robot-Aided Neuro-Rehabili-

tation and Functional Imaging.”From 1977 to 1978, he taught electrical design at Escola Tecnica Federal de

Sao Paulo. From 1978 to 1979, he worked at the University of Sao Paulo in aproject aiming at the identification of hydrodynamic coefficients during ship ma-neuvers. From 1980 to 1986, he was a surveyor of ships, offshore platforms, andcontainercranes at the American Bureau of Shipping, Sao Paulo office. In 1989,he was a Visiting Researcher at Sumitomo Heavy Industries, Hiratsuka Labora-tories, Japan. From 1993 to 1996, he worked at Casper, Phillips & Associatesin containercranes and control systems. He joined the Newman Laboratory forBiomechanics and Human Rehabilitation, Mechanical Engineering Department,MIT, in 1997, where he is a Principal Research Scientist and Lecturer. He alsoholds an affiliate position as an Adjunct Research Professor of Neuroscience aWeill Medical College, Cornell University, White Plains, NY. He is one of thefounders of Interactive Motion Technologies, a Cambridge, MA, based startupcompany commercializing robot technology for rehabilitation. His research in-terests are in the areas of neuro-rehabilitation, functional imaging, human-ma-chine interactions, robotics, and dynamic systems modeling and control.

Bruce T. Volpe received the B.S. and M.D. degrees from Yale University, NewHaven, CT.

He is currently a Professor in the Department of Neurology and Neuroscience,Weill Medical College, Cornell University, White Plains, NY. He is interestedin a model of antibody mediated disease.

Neville Hogan was born in Dublin, Ireland. He re-ceived the Dip. Eng. (with distinction) from DublinCollege of Technology, Dublin, Ireland, and the M.S.,M.E., and Ph.D. degrees from Massachusetts Insti-tute of Technology (MIT), Cambridge.

Following industrial experience in engineeringdesign, he joined the Faculty of the School ofEngineering, MIT, in 1979 and has served as Headand Associate Head of the System Dynamics andControl Division, Department of Mechanical Engi-neering. He is Professor of Mechanical Engineering,

Professor of Brain and Cognitive Sciences, and Director of the Newman Labo-ratory for Biomechanics and Human Rehabilitation, MIT. He is a co-founder ofInteractive Motion Technologies, Inc., Cambridge, MA, and a Board Memberof Advanced Mechanical Technologies, Inc., Watertown, MA. His research isbroad and multidisciplinary, extending from biology to engineering; it has madesignificant contributions to motor neuroscience, rehabilitation engineering androbotics, but its focus converges on an emerging class of machines designedto cooperate physically with humans. Recent work pioneered the creation ofrobots sufficiently gentle to provide physiotherapy to frail and elderly patientsrecovering from neurological injury such as stroke, a novel therapy that hasalready proven its clinical significance.

Prof. Hogan had received several awards including an Honorary Doctoratefrom the Delft University of Technology, the Silver Medal of the RoyalAcademy of Medicine in Ireland, and an Honorary Doctorate from the DublinInstitute of Technology.

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