Error Augmentation for Upper Limb Rehabilitation in Stroke Survivors: a Proof-of-concept Study
Phase: NA
Conditions: Stroke
Sponsor: McGill University

Document: Protocol 
Official study title: Error augmentation for upper limb rehabilitation in stroke survivors 
NCT identification number: pending 
Document date: February 5, 2021 
 
 

Error augmentation for upper limb rehabilitation in stroke survivors 
Caroline Rajda,1 Mindy F. Levin,1 Sigal Berman,2 Shelly Levy-Tzedek3 
1School of Physical and Occupational Therapy, Faculty of Medicine, McGill 
University, Canada, 2Department of IEM, Ben-Gurion University of the Negev  
 3Department of Physical Therapy, Ben-Gurion University of the Negev  
 
Research Innovation – contribution beyond state of the art 
Stroke is a leading cause of long-term sensorimotor disability with deficits in upper 
limb function persisting into the chronic stage in a large proportion of stroke survivors 
(Langhorne et al. 2009). This is partly due to the limited effectiveness of current upper 
limb rehabilitation interventions (Kwakkel et al. 2003). A stronger focus on training 
aligned with addressing the underlying causes of the motor control deficits rather than only 
on the behavioral output, is essential for significantly improving treatment efficiency. 
Robotic augmented therapy can repetitively deliver patient-specific, high-dosage 
training. To-date, however, results of training approaches integrating robotics have not 
been highly successful (Maciejasz et al. 2014; Huang and Krakauer 2009). Robots have 
been used to administer high-dosage repetitive training, which leads to improvement in the 
trajectories practiced. While learned movement patterns may generalize across different 
movements made within the practiced regions (Conditt et al. 1997), whether such training 
leads to improvement in upper limb functional ability remains controversial. Upper limb 
motion is by nature variable and not pattern-specific due to the kinematic redundancy of 
the system. Therefore, repetitive practice of single motion paths may not be sufficient to 
improve generalized upper limb functional ability.  
One way to increase the control of upper limb movement is through the use of a 
specialized robotic technique based on error augmentation (EA) (see Israely and Carmeli 
2016 for recent review). In EA treatment, subjects are provided with feedback that 
enhances their motor errors and forces them to move into larger joint ranges. This is usually 
done by distorting visual feedback while additional haptic feedback can be provided using 
robotics. EA has resulted in positive effects for lower limb locomotor training, e.g., Kao et 
al. 2013; Tyrell et al. 2015). Recently, several researchers have implemented EA for upper 
limb training, but results have been disappointing in terms of functional improvement (e.g., 
Abdollahi et al. 2014; Molier et al. 2011). It is likely that the reason for the lack of 
functional effects is that the error enhancement is only applied in a generalized way, 
without consideration of the specific upper limb motor deficit of the patient; i.e., the 
training is not personalized to the patient’s particular motor impairment.        
In previous work, we have tested the effect of a patient-specific impairment-based 
training approach based on the threshold control theory (TCT) of motor control (Feldman 
2015) in a tri-national randomized controlled trial (IDRC/CIHR/ISF 2015-20). According 
to TCT, voluntary movement is generated by regulating the spatial thresholds (STs) at 
which muscle activation begins. EMG emerges based on the interaction of the 
biomechanics of the system with the environment. The ability to regulate STs is reduced 
in patients with stroke, due to damage to descending neural pathways (Levin and Feldman 
1994). The reduced capability leads to abnormal muscle activation patterns (i.e., excessive 
co-activation) as well as spasticity and weakness within well-defined spatial (angular) 
zones. The active control zone, i.e., the angular zone in which voluntary movements can 
be made with typical muscle activation patterns, is reduced. Thus, in the previous trial 
  Approuvé par le CÉR des établissements du CRIR le 5 février 2021

(Levin et al. 2018), the upper limb training was optimized to increase the patient’s active 
control zone of the elbow joint by identifying and restricting reaching movements to that 
specific zone. Preliminary results suggest that this approach led to greater gains in upper 
limb function in the group who had individualized training compared to the group that did 
not.  
Studies suggest that errors made during training improve later performance of the task. 
For example, in a study in which elderly subjects practiced rhythmic motion of the upper 
limb with and without visual feedback, greater errors made during treatment led to more 
movement improvement following training (Levy-Tzedek 2017). This result is consistent 
with the TCT as muscle activation is regulated by the difference between the actual limb 
position and referent limb position specified by the brain. The referent position also plays 
a role in the perception of the actual limb position, while the afferent component identifies 
the deflection of the limb from the referent limb position. Based on this combination, the 
system identifies the movement error, i.e., deviation of the limb from the target position. 
Movement correction is accomplished by properly adjusting the referent limb position.  
The sensitivity of referent control may be enhanced by augmentation of the movement 
error, i.e., Joint Error Augmentation (JEA). Furthermore an improvement in referent limb 
control is likely to be associated with improvement in threshold (referent control) at the 
level of individual joints. Development of rehabilitation treatment protocols based on 
motor control theory, such as that underlying JEA, is a promising direction for robotic 
rehabilitation therapy.  
 
Research Objectives:  
Our goals are to investigate the effect of JEA on motor learning in healthy subjects and 
on upper limb rehabilitation in subjects with stroke.  
 
Hypothesis 1: In both groups, JEA applied to the elbow joint range will lead to better 
learning of movement speed and precision of a reaching task compared to no JEA.  
Hypothesis 2: In patients with stroke, JEA applied to the elbow joint range will lead to a 
greater increase in the patient’s active control zone at the elbow compared to no JEA and 
thus in his/her ability to perform isolated voluntary movement of the elbow.  
 
Research plan  
In our preliminary proof of principle study, we developed and tested a passive robotic 
system in 21 healthy subjects (aged 26.2, SD 1.2 yrs). A passive robotic arm was developed 
for supporting and monitoring arm motion and combined with a simple virtual reality pick-
and-place game (Sror et al. 2018). Software was developed for integrating input from the 
robotic arm and a Kinect camera. The software includes a pick-and-place VR game, game 
success feedback, and motion feedback with and without JEA. Individuals were assigned 
to a training group with or without EA and performed one 30 min training period. There 
was no difference between groups in terms of the success rate of hitting the targets. The 
group training with EA tended to work longer and be more motivated. The application was 
found to be acceptable to healthy individuals, to enhance motivation to exercise and not to 
cause fatigue or any adverse effects (Sror et al., 2018; 2019a; 2019b).  

The next phases of the study are to investigate the effects on learning with JEA 
feedback in healthy subjects, and then to test the JEA feedback effect in patients with 
stroke.  
In two separate studies, we will test 24 healthy subjects and 24 patients who have 
moderate-to-severe stroke (spasticity + some arm movement). Testing of the healthy 
subjects will be done in Canada and testing of patients will be done in two countries (Israel-
Soroker Hospital, Beer-Sheva; Canada-Jewish Rehabilitation Hospital, Laval/Montreal). 
The motivation for testing the application in patients at two sites is to assess the feasibility 
of conducting a multi-site study using this technology.  
 
In both studies: Subjects will be randomly allocated to two equal groups. One group 
(control) will receive regular feedback and the other group will receive JEA feedback 
(randomized with catch trials).  
Subjects will perform ~30 min of target reaching with their non-dominant/affected arm, 
3 times in one week, with a minimum of 1 day between sessions. Rest periods will be 
provided when needed to avoid subject fatigue. In the experimental group, target locations 
will be tailored to coincide with active control range limits determined at the beginning of 
each session. In the stroke group, workspace extensions will be added as active range 
increases (i.e., exercise progression) for patients receiving JEA feedback.  
Session 1 (1 hour and 15 min) will include a Reaching Pre-Test of 10 trials to a fixed 
target (see below), reaching training and evaluation of perceived exertion. Session 2 (1 
hour) will include the evaluation of the TSRT in the stroke group, reaching training and 
perceived exertion rating. Session 3 (2 hours) will be the same as Session 2 but the last 10 
trials of Session 3 will be done to a fixed target (Post-Test). Following Session 3, there will 
be a pause of 1 hour after the training and then a Reaching Follow-up Test of 10 trials to a 
fixed target will be done. In addition, the stroke group will undergo a clinical evaluation 
including determination of the elbow flexor TSRT angle prior to Session 1 and another 
clinical evaluation following Session 3.  
 
For Study 1, the primary outcome measures are change in the index of performance (IP), 
which is a measure of reaching efficiency (Takeda et al., 2019).  
 
For Study 2, the primary outcome measures are the same as those for the healthy group 
as well as the area of the active control workspace based on the TSRT and spasticity. The 
secondary outcome measures are the change in the FMA-UL and patient effort according 
to the Borg scale.  
 
Evaluations for Study 2: 
 
Clinical evaluation sessions: Clinical assessments will be conducted by a blinded assessor 
before (Pre-test) and after (Post-test) the last training session. In the Pre and Post-test 
sessions, all of the following assessments will be done.   
 
Arm motor impairment: i) Voluntary arm motor ability and coordination will be evaluated 
using the valid (Berglund and Fugl-Meyer 1986) and reliable Fugl-Meyer Arm Assessment 
(FMA; Fugl-Meyer et al. 1975) that measures reflexes, volitional movements and UL 

coordination on a 66 point scale. FMA also evaluates sensation (light touch), kinaesthesia 
and passive range of motion on ordinal scales; ii) spasticity will be measured by the 
Modified Ashworth Scale (Bohannon and Smith 1987) on a scale of 0 to 4 : iii) TSRT The 
threshold of the active control zone of the 
elbow will be identified by determining the 
tonic stretch reflex threshold (TSRT) angle in 
elbow flexors using the Montreal Spasticity 
Measure (Calota et al. 2008).  This 
methodology 
has 
been 
successfully 
implemented in our previous RCT (Levin et al. 
2018). 
It 
consists 
of 
recording 
electromyographic signals from the elbow 
flexors and extensors with surface electrodes 
as well as the elbow angle using a goniometer, 
while the elbow is stretched from flexion to 
extension 20 times. Then, determination of the 
active control workspace will be done by 
passively moving the arm attached to the robot 
arm at a moderate speed throughout the 
horizontal workspace while monitoring elbow 
extensor and flexor EMG on an oscilloscope and respecting the identified elbow joint range 
limitation. Since, no EMG activity should be present at rest, the appearance of elbow flexor 
or extensor activation during passive manipulation of the arm through the horizontal 
shoulder/elbow workspace will indicate that the elbow has been moved beyond the TSRT 
angle. In this way, the TSRT angle will define the limits of the whole arm active control 
workspace. The area of the active control workspace will be computed from the x, y 
workspace recorded by the Kinect II camera using enhanced tracking and expressed as a 
percentage of the full biomechanical workspace defined by the limits of the passive 
shoulder horizontal/elbow area.  
 
For both studies: Training protocol (Fig 1): As described above, the arm active control 
workspace will be measured by the Kinect II camera and reproduced in the VR workspace 
for each session. Objects to be reached will be displayed within this defined VR workspace 
for both groups. In this way, the training will be personalized to the range of active motion 
in each subject. Three 30 min training sessions will be done. At the end of each training 
session, the patient’s perceived sense of effort will be evaluated with the valid and reliable 
20 pt Borg Rating of Perceived Exertion Scale (Borg 1982).   
 
The robotic system consists of an ergonomic double-joint horizontal manipulandum 
mounted on a rigid, movable support (Fig. 1). The subject sits in an armless chair with a 
high back support and trunk movement is restricted. The forearm is attached to the 
manipulandum with Velcro and the wrist and hand are free to move. Movements of the 
arm and manipulandum are used as input signals to move the arm avatar in the VR game. 
For the stroke group, the game (Fig. 2) is adapted to patient capabilities, based on her/his 
active control range for isolated elbow flexion and extension determined before each 
training session. The initial hand position is 10 cm sagittal to the patient’s midline. Prior to 
Fig. 1. Training position and set-up.   

Session 1, a Pre-Test will be conducted in which subjects will reach 10 times to a fixed 
target placed at arm’s length in the subject midline. The same protocol will be added to the 
end of Session 3 as a Post-Test and repeated 1 hour after Session 3 as the Follow-up Test.  
Training Protocol: Objects to be reached are randomly presented within the virtual 
workspace every 2-10s. The subject is instructed to reach each object from the specified 
starting position as quickly and as accurately as possible in a session 
of at least 30 minutes (approximately 138 
reaches, considering 3s/reach). To control 
training intensity, practice will be extended to 
the time needed to complete 138 reaches per 
session. During reaching, the motion of the 
whole arm is shown by an avatar, while vision 
of the subject’s actual arm is blocked by a 
screen, to minimize visual-proprioceptive 
mismatch (Dietz 1992). For JEA trials, the 
avatar depiction includes a 10 elbow flexion 
error 
based 
on 
forward 
kinematic 
calculation. For implicit learning to occur, 
we will use a small (i.e., 10) instead of a large error, to decrease direct perception of elbow 
position and conscious correction. Feedback in the form of task success of endpoint (hand) 
movement is presented at the end of each reaching trial to increase motivation and 
engagement (Guadagnoli and Lee 2004). The introduced error causes the subject to 
perceive their hand position as undershooting the target. The subject’s task is to solve the 
problem of moving the arm further by learning to incorporate more elbow extension into 
the whole arm reaching task. Multiple solutions are needed in order to solve problems for 
targets located in different parts of the arm workspace. Game difficulty will be progressed 
according to the Challenge Point theory of motor learning (Guadagnoli and Lee 2004), 
which suggests that learning is enhanced by optimally challenging the individual through 
manipulation of task difficulty according to their motor skill level and cognitive capacity. 
Difficulty level will be increased by altering target locations (i.e., based on changes in 
active ranges for the JEA stroke group and by increments of 2 cm for the non-JEA group) 
and by decreasing the allowable movement time for each reach (increasing movement 
speed). Difficulty will be progressed when the participant reaches a 90% success rate. Low-
cost motion tracking is used for clinical accessibility. Motion tracking is based on the 
Kinect II skeleton and an extended Kalman filter (Simon 2006) to improve accuracy (Fig. 
3). A Kalman filter has two steps in each iteration. First, a prediction is made based on the 
current location and the motion model. The prediction is of both the state variables and 
their uncertainty. Then, the predictions are updated based on the current measurement 
using a weighted average, which incorporates the uncertainties of the measurements and 
the predicted states. The model assumes no shoulder motion, and motion along the same 
direction as in the previous step in the elbow and wrist. At each step, model predictions are 
combined with the measured locations, where weights are based on the Kalman gain 
calculated on the estimated state error. Due to the incorporation of rigid body relations, 
Fig. 2. Left: View of the subject’s arm avatar 
and arm workspace for the VR training. Right: 
Therapist’s control screen showing patient 
information and training parameters.  

calculations of Kalman gains for elbow and wrist also account for shoulder and elbow error 
covariance respectively. Segment lengths are updated after each measurement step. Objects 
to be reached in VR are determined before training based on the subject’s arm range of 
motion. The RANSAC algorithm (Fischler and Bolles 1981) is used to fit the recorded arm 
positions into the plane to reduce measurement errors. 
 
Subjects and subject recruitment. Participants will be allocated by random numbers to 
either an experimental group that will receive JEA feedback (randomized with catch trials) 
or a control group that will receive no JEA feedback. Blinding: The sequence of allocation 
will be concealed from those assigning participants to groups until the moment of 
assignment, to prevent them from consciously or unconsciously influencing which 
participants are assigned to which groups. Participants will also be blinded to group 
allocation. Finally, evaluators will be blinded by concealing participant group allocation.  
 
Patients will be recruited through well-established procedures at the JRH. Potential 
participants will be identified from 
chart or database review and sent a 
letter of information describing the 
study. If they are interested, they will 
be directed to contact the research 
assistant by phone or email who will 
schedule them for a screening 
interview. Healthy subjects will be 
recruited from the community with 
the help of information flyers posted 
on bulletin boards. Subjects will participate after signing informed consent forms approved 
by the Center for Interdisciplinary Research in Rehabilitation (CRIR) Ethics Committee. 
Handedness will be measured for both studies with the Edinburgh Handedness Inventory 
(Oldfield 1971) but for Study 2, both right- and left-handed subjects will be included since 
hemispheric dominance is not expected to influence results. For Study 1, healthy women 
and men will be included who 1) are between the ages of 18-75 yrs; 2) have normal or 
corrected-to-normal vision; and 3) are able to provide informed consent. For Study 2, 
women and men will be included who 1) sustained a first cortical/sub-cortical 
ischemic/hemorrhagic stroke <1 yr previously and are medically stable; 2) are aged 18-75 
yrs to minimize confounding effects of age-related changes in muscular responses; 3) are 
no longer receiving treatment; 4) have arm paresis (Chedoke-McMaster Arm Scale of 2-6 
/7; Gowland et al. 1993) but are able to perform voluntary elbow movement of at least 30° 
per direction; 5) have mild-moderate elbow flexor spasticity; and 6) are able to provide 
informed consent. Subjects will be excluded if they have/are: 1) other major neurological 
neuromuscular/orthopaedic/pain problems that may interfere with task performance; 2) 
major cognitive deficits (<26 on Montreal Cognitive Assessment; Nasreddine et al. 2005) 
or 3) depression (> 14 on Beck Depression Inventory II; Beck et al. 1988; 1996).  
 
Data analysis 
For Study 2, data from Canada will be combined with those from Israel for a total of 12 
subjects per group. 
Fig. 3. UL model based on the Kinect II skeleton (left) 
and explanation of the extended Kalman filter (right) 
to improve motion tracking accuracy.   

To address Hypothesis 1, we will compare the change in the IP between the Pre-Test and 
the Post-Test as an indicator of motor improvement and the change from Post-Test to 
Follow-Up as an indicator of motor learning using paired t-tests after verifying for data 
normality.  
To address Hypothesis 2, changes in the area of the active control workspace and clinica 
scores will be compared between groups using paired t-tests after verifying for data 
normality.  
 
Knowledge translation 
The preliminary results will primarily be used to inform the design of a full randomized 
controlled trial. However, preliminary results will be shared with clinicians at both sites 
and by oral or poster presentations at local, national or international meetings.  
 
Participating researchers  
Caroline Rajda is a 1st year Master’s student in the Integrated Program of 
Neuroscience at McGill University. Mindy Levin is Professor in the School of Physical 
and Occupational Therapy in the Faculty of Medicine at McGill University. She has 
pioneered many innovations in the area of upper limb rehabilitation, including 
identification of underlying motor control deficits at the joint and interjoint level, 
measurement of spasticity, incorporation of VR training interventions and the distinction 
between compensation and recovery. Sigal Berman is an Associate Professor in the 
Department of Industrial Engineering and Management at Ben-Gurion University of the 
Negev. She leads the Telerobotics laboratory where robotic motion control, learning, and 
interaction methodologies are developed and assessed. Prof. Berman has applied advanced 
mathematical methods for modeling and analysis of arm motion in healthy subjects and in 
patients with stroke. Shelly Levy-Tzedek is a Senior Lecturer in the Department of 
Physiotherapy who has been studying human movement in health and in disease for over 
a decade, publishing in journals that are either specific to neurological rehabilitation, or 
appropriate for inter-disciplinary research (e.g., in Scientific Reports). Profs. Berman and 
Levin have been collaborating on stroke rehabilitation for close to a decade. They have 
jointly published papers in leading journals and conferences [e.g., 15-17] and have worked 
together in two funded research projects. Prof. Berman and Dr. Levy-Tzedek have 
collaborated on robotic rehabilitation, and have jointly supervised a student working on 
robotic mirror-game treatment [18]. 
 
Budget and matching funds: details and justification 
The project is funded by an ABC (Agricultural, Biological and Cognitive Robotics)  
grant to Sigal Berman from Ben-Gurion University for the portion of the research done in 
Israel. Funds are allocated for supporting a scholarship or a graduate student. During the 
first year, most of the funds were allocated to the construction of the passive manipulator 
and purchase of equipment (i.e., computer, Montreal Spasticity Measure). Funds are 
allocated for subject compensation, publications and student travel to conferences.   
 
Table 1. ABC budget Israeli team 
Funding plan per fiscal year k US$ 
Y - 1 Y - 2 Y - 3 Total  
Scholarship for graduate student 
2 
2 
2 
 

Building manipulandum + computer for the system 
11 
 
 
 
Spasticity measurement equipment  
 
9.5 
 
 
Supplies, peripherals, software license, Kinect, etc. 
1 
0.5 
2 
 
Human subject reimbursement  
 
2 
3 
 
Travel: To Canada (year 2) Hosting in IL (year 1+3) 
1 
1 
2 
 
Student travel to conferences 
 
 
3 
 
Publications 
 
 
3 
 
Total k US$ 
15 
15 
15 
45 
 
 
Justification: Most of the budget in the first year is allocated to adapting the current 
robot/manipulandum and writing/implementing appropriate software for EA. Personnel 
costs are related to this activity in year 1 and to conducting the study in years 2 and 3. 
Human subject reimbursement ($25 x 14 sessions (3 test + 10 training) x 12 subjects = 
4200). Funds are allocated to defray travel costs to Israel in year 1 and 3 and hosting costs 
in Canada in year 2 for PI and students, as well as to defray costs of publication and 
presentation in years 2 and 3.  
 
Table 2. Matching budget Canadian team 
Matching funding is provided by Mindy Levin from the Canada Research Chairs fund 
(until March 2019) and then the Distinguished James McGill Professor fund. 
Funding plan per fiscal year k US$ 
Y - 1 Y - 2 Y - 3 
Total  
Personnel (Research assistant + engineer)  
10 
 
 
10 
Personnel (Research assistant + student) 
 
10 
10 
20 
Adapting manipulandum, software + computer for the 
system 
3 
 
 
3 
Supplies, peripherals, software license, Kinect, etc. 
 
 
 
 
Human subject reimbursement 
 
2 
2 
4 
Travel: To IL (year 1+3) Hosting in Canada (year 2)  
2 
1 
2 
5 
Conference presentation and publications 
 
2 
1 
3 
Total k US$  
15 
15 
15 
45 
 
 
 
 
 
 
 

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