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A Wearable Bracelet Device for Promoting Arm Use in Stroke Patients


Abstract and Figures

After stroke many patients experience hemiparesis or weakness on one side of the body. In order to compensate for this lack of motor function they tend to overuse their non-affected limb. This so called learned non-use may be one of the most relevant contributors to functional loss after post-stroke hospital discharge. We hypothesize that frequent exposure to movement related feedback through an ubiquitous wearable bracelet device may 1) increase the patient's intrinsic motivation for using the paretic limb, and 2) counteract learned non-use, therefore inducing motor recovery. First, to validate the accelerometers-based measurement of arm use, we recruited 10 right-handed volunteers without neurological impairments. Second, we explored the acceptability and clinical impact of a low-cost wearable system on 4 chronic stroke patients with hemiparesis. Our results suggest that frequent exposure to direct feedback about arm use promotes the integration of the paretic limb in the performance of ADLs. In addition, results from questionnaires revealed that the use of wearable devices may influence positively the patient's intrinsic motivation for using the affected arm. To the best of our knowledge, this is the first study providing evidence of the benefits of wearable-based feedback as an intervention tool for counteracting learned non-use.
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A Wearable Bracelet Device for Promoting Arm Use in Stroke Patients
en Rubio Ballester1, Alica Lathe1, Esther Duarte2, Armin Duff1and Paul F. M. J. Verschure1,3
1Laboratory of Synthetic Perceptive, Emotive, and Cognitive Systems (SPECS), Departament de Tecnologies
de la Informaci´
oi la Comunicaci´
o(DTIC), Universitat Pompeu Fabra, Roc Boronat, 138 08018 Barcelona, Spain
2Servei de Medicina F´
ısica I Rehabilitaci´
o, Hospitals del Mar I l’Esperanc¸a,
Institut Hospital del Mar d’Investigacions M`
ediques, Barcelona, Spain
o Catalana de Recerca i Estudis Avanc¸ats (ICREA), Barcelona, Spain
Keywords: Stroke, Learned Non-use, Wearables, Motor Rehabilitation, Hemiparesis.
Abstract: After stroke, many patients experience hemiparesis or weakness on one side of the body. In order to com-
pensate for this lack of motor function, they tend to overuse their non-affected limb. This so called learned
non-use may be one of the most relevant contributors to functional loss after post-stroke hospital discharge.
We hypothesize that frequent exposure to movement related feedback through a wearable bracelet device may
1) increase the patient’s intrinsic motivation for using the paretic limb, and 2) counteract learned non-use,
therefore inducing motor recovery. First, to validate the accelerometers-based measurement of arm use, we
recruited 10 right-handed volunteers without neurological impairments. Second, we explored the acceptability
and clinical impact of a low-cost wearable system on 4 chronic stroke patients with hemiparesis. Our results
suggest that frequent exposure to direct feedback about arm use promotes the integration of the paretic limb
in the performance of instrumental activities of daily living (iADLs). In addition, results from questionnaires
revealed that the use of wearable devices may influence positively the patient’s intrinsic motivation for using
the affected arm. To the best of our knowledge, this is the first study suggesting the benefits of wearable-based
feedback as an intervention tool for counteracting learned non-use.
After hospital discharge, up to 55% to 75% of stroke
patients experience persistent motor impairments (Lai
et al., 2002) and may even suffer substantial declines
in function in the following 6 months. A number of
studies suggest that this loss may be due to the lack
of use of the paretic limb (Lai et al., 2002), a phe-
nomenon that has been called learned non-use. Re-
cent work on studying the dynamics of motor recov-
ery after stroke have shown that learned non-use may
emerge as a consequence of decision making for mo-
tor optimization, therefore being dependent on two
main factors: the expected success and the expected
cost of using either effector (Hidaka et al., 2012; Han
et al., 2008; Ballester et al., 2015a). On these basis,
there may exist different strategies for counteracting
learned non-use. For instance, Constrained-Induced
Movement Therapy (CIMT) proposes to reduce the
probability of success and increases the cost of us-
ing the non-affected limb by restricting its movement
and tactile feedback using a mitt (Taub and Uswatte,
2003). Recently, we have shown that Reinforcement-
Induced Movement Therapies (RIMT) may be com-
plementary to CIMT (Ballester et al., 2015a; Ballester
et al., 2015b). In RIMT, visual manipulations during
training increase the probability of success and reduce
the cost of using the paretic limb. However, these re-
habilitation protocols are usually limited to short ses-
sions of intervention and may not be suitable for unsu-
pervised domiciliary environments. In light of these
limitations, the use of wearable devices could be spe-
cially suitable for the persistent monitoring and treat-
ment of leaned non-use.
The current state of research and technological de-
velopment shows a tendency towards the gamifica-
tion of rehabilitation tools, combining various types
of sensors to capture motion and posture. Several
studies have tested the reliability and validity of us-
ing accelerometers for measuring arm use in activities
of daily living (Noork˜
oiv et al., 2014; Uswatte et al.,
2005). In this vein, significant effort has been made in
evaluating the acceptability of wearable devices that
incorporate accelerometers for the quantification of
motor performance and recovery (Wang et al., 2014).
The application of wearable devices to the rehabilita-
Ballester, B., Lathe, A., Duarte, E., Duff, A. and Verschure, P..
A Wearable Bracelet Device for Promoting Arm Use in Stroke Patients.
In Proceedings of the 3rd International Congress on Neurotechnology, Electronics and Informatics (NEUROTECHNIX 2015), pages 24-31
ISBN: 978-989-758-161-8
Copyright c
2015 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
tion field offers a number of advantages, such as im-
proved objectivity, sensitivity, and ease of measure-
ment of therapy outcomes. However, because of their
ubiquity, wearable devices may be also useful as inter-
vention tools. Their design and interface enables the
frequent delivery of multimodal feedback during the
performance of iADLs, which may facilitate the re-
alignment of attention towards the affected limb, thus
encouraging the selection of the weaker arm.
Recently, Markopoulos et. al (Markopoulos et al.,
2011) developed a credibility and usability study on
an experimental wearable device that monitors the
patient’s behavior and displays feedback about the
use of the affected versus unaffected arm. So far,
previous work on wearable devices for rehabilitation
describe prototypes and techniques for the integra-
tion of monitoring hardware in wearable garments
as well as communications systems (Uswatte et al.,
2005). Uswatte et al. conducted a clinical experi-
ment in which 20 stroke patients wore an accelerom-
eter on each arm, the chest, and the more affected
leg. Recordings from each sensor were used to es-
timate the duration of movement as a percentage of
the total recording period. Results revealed a strong
correlation between the accelerometers-derived mea-
surements and the Motor Activity Log (Uswatte et al.,
2006). More recent studies have validated and ex-
tended these findings showing strong correlations be-
tween triaxial accelerometry-derived measurements
and the Quality of Movement scores (van der Pas
et al., 2011) or the National Institutes of Health Stroke
Scale (NIHSS) (Gubbi et al., 2013). However, there
is no evidence yet about the clinical impact of these
types of devices.
The aim of this study is to evaluate the potential
of a wearable system for measuring the amount of
use of the paretic arm in iADLs in chronic stroke pa-
tients with upper extremity hemiparesis. Specifically,
we hypothesize that frequent exposure to movement
related feedback through a wearable device may 1)
counteract learned non-use, and 2) increase the pa-
tient’s intrinsic motivation for using the paretic limb.
This work presents results from a pilot study explor-
ing stroke patients’ acceptance of a wearable device
for independent usage in their home setting and its ef-
fectiveness as an intervention tool for promoting the
use of the paretic limb.
2.1 Equipment
The RGS-Wear is a wearable system for the con-
Figure 1: Prototype wearable bracelets integrating the
MetaWear board and Velcro straps. the MetaWear board
includes a low-power, 3-axial capacitive micromachined ac-
celerometer, RGB LEDs, and a coin vibrating motor.
tinuous monitoring of arm use in hemiparetic stroke
patients. It is composed by a pair of bracelets
and a smartphone (Sony Xperia Z3 Compact).
The bracelets include a coin-sized Bluetooth-Board
(MetaWear, MbientLab, San Francisco, CA.) with in-
tegrated accelerometer, a vibrating motor, an ultra
bright RGB LED, a battery, and a wristband (Fig.
1). The accelerometer is a Freescale MMA8452Q:
a smart low-power, three-axis, capacitive micro-
machined accelerometer with a resolution of 12 bits.
Data recordings from each accelerometer are con-
tinuously monitored and sent through Bluetooth to
a paired smartphone, which demands the patient to
carry the smartphone with him or her throughout the
day. For this purpose the participants were equipped
with a holding bag for the phone to be placed at the
2.2 Quantification of Arm Use
For monitoring purposes, the acceleration data from
each device (left bracelet, right bracelet, and smart-
phone) is sampled at 50Hz for each directional di-
mension. In order to derive from these data some
meaningful quantification of arm use, we followed a
number of steps. First, over a one minute epoch, we
computed the mean squared sum of the acceleration:
This measurement represents a rough index of the
amount of movement of the object to which the ac-
celerometer is attached. This method has been shown
to be an adequate approximation of Energy Expen-
diture (EE) in comparison to measurements derived
from the heart’s electrical activity, muscle activation,
and oxygen consumption (van der Pas et al., 2011;
A Wearable Bracelet Device for Promoting Arm Use in Stroke Patients
Tsurumi et al., 2002). Next, in order to provide feed-
back to the user, we defined the change in the EE of
the paretic arm as:
·100 (2)
where γprefers to the mean activity of the paretic
arm per hour, γ0corresponds to the mean activity
of the paretic arm at baseline, and arm activity is
given by the difference between the activity of the
corresponding arm βaand the activity of the body βb:
where [γ]+=γ,if γ>0
0,if γ<0
In addition, in order to monitor arm balance, we
compute the ratio between the daily mean activity of
the paretic γp, and non-paretic arm γnp . Thus arm bal-
ance is given by:
100 (4)
Figure 2: Graphical interface for feedback delivery. A.
Types of feedback according to the change in EE by hour
(Hourly Feedback). B. Example of Review Feedback show-
ing the change in Energy Expenditure (EE) achieved dur-
ing the morning (from 10 to 13 hours). C. Example of Re-
view Feedback delivered at the end of the day, showing the
change in EE by hour along the daily session (from 10 to 19
2.3 Feedback Design
The main objective of this study was to evaluate the
influence of feedback of performance on arm use.
Therefore, we first explored how to deliver this type
of information to the patient in an efficient and mean-
ingful manner. The design principles shaping RGS-
Wear were derived from the Self-Determination The-
ory proposed by Ryan et al. (Ryan et al., 2008).
This theory defines three main behavioral mediators,
which determine a patient’s self-engagement within
the process of Health Behavior Change:
1. Autonomy: the patient’s degree of Self-
engagement as the willingness to change due to
the self-referenced value of the targeted behavior.
2. Competence: the individual’s capacity to af-
ford a change.
3. Relatedness: the relation of the patient with
the practitioner, who facilitates the other two me-
Based on these principles, the RGS-Wear was de-
signed to serve as a rehabilitation device for self-
reinforcement. The incoming feedback messages are
delivered through the vibration of both the smart-
phone and bracelets. Additionally, the phone displays
a sound signal, whereas the bracelets’ LEDs blink in
red, green or blue, according to the improvement cat-
egory achieved (Table 1). Every message has a con-
firmation button, which should be pressed to confirm
its reception. The following subsections describe in
detail these feedback messages.
2.3.1 Hourly Feedback
The Hourly Feedback was designed to be simple and
effective as the user derives all information about im-
provement in use with a short glance at his smart-
phone (Fig. 2A). A percentage indicates the mean
change in the EE of the paretic limb in respect to
baseline that was achieved within the previous hour
(mean δ, see Eq.2). This type of messages are accom-
panied by an illustration of the upper-limbs. Beneath,
a comment window shows different motivational say-
ings (e.g. ”Stay active along the day.”). Considering
not to generate pressure, the text appears as a general
request to stay active within the day, regardless of the
numerical result shown.
This feedback was designed to support the pa-
tient’s self-engagement and increase the value of us-
ing the paretic arm in the performance of iADLs. This
type of feedback is thus tightly related to the con-
cept of Autonomy proposed by the Self-Determination
Theory (SDT). In this line, the display of a percentage
NEUROTECHNIX 2015 - International Congress on Neurotechnology, Electronics and Informatics
Table 1: Feedback Categories.
Result Above 10% Between 10% and -10% Below -10%
Category Positive Neutral Negative
Signal in bracelets Green blinking light Blue blinking light Red blinking light
Signal in smartphone Positive green operator Positive blue operator Negative red operator
Figure 3: Averaged activity of the non dominant arm, for
each subject, achieved during the execution of three iADLs
(Wash de dishes, eating, and buttoning up a shirt) under two
different conditions. During the Restricted condition, the
use of the left limb was limited. During the Balanced condi-
tion, the participant was encouraged to use both arms. Red
horizontal lines indicate the inactivity threshold.
improvement in use provides a target oriented self-
2.3.2 Review Feedback
The Review Feedback displays a summary of the
Hourly Feedback in session intervals (Fig. 2B), thus
providing knowledge of progress. In this study, RGS-
Wear was pre-programmed to monitor 9 consecutive
hours a day. Daily recordings were partitioned in a
morning, afternoon, and night sessions, and each of
them had a duration of 3 hours. Review Feedback was
provided at the end of each session and at the end of
the day, displaying the hourly mean activity level of
the paretic arm in a graphical chart. The rational for
this feedback was to meet the patients’ psychologi-
cal need of Competence by presenting an overview of
performance over time.
2.3.3 Instructions Slides
The RGS-Wear daily protocol is initialized at 10 a.m.
by presenting a number of welcoming slides accom-
panied by an alarm sound that signalizes the begin-
ning of the monitoring.
2.4 Experimental Paradigm
In order to assess the reliability of the RGS-Wear
for capturing differences in arm use, we first con-
ducted an experiment on humans with no neurolog-
ical impairments. We instructed participants to per-
form four iADLs (washing the dishes, eating, button-
ing up a shirt, and walking) while wearing the RGS-
wear bracelets. Each activity had a duration of 3 min-
utes. All activities were performed twice: first, partic-
ipants were asked to use their left arm with reduced
intensity as they would normally do (non-dominant
condition), and second, bi-manual execution was en-
couraged (balanced condition).
After validating RGS-Wear as a monitoring tool,
we designed an experimental paradigm to explore the
potential of RGS-Wear for promoting the use of the
paretic limb in stroke patients. Participants were in-
structed to use the RGS-Wear system at home for ve
consecutive weekdays, from 10 to 19 o’clock, except
when bathing. The experiment was divided in three
phases: pre-test, intervention, and post-test. Due to
the ubiquitous presence of the device, the sensation
of being under observation could be cued and may
A Wearable Bracelet Device for Promoting Arm Use in Stroke Patients
Figure 4: Quantifying behavioral changes. A. Difference between arm activity during baseline (day 1). B. Arm Use Balance
between the paretic (red) and non-paretic arm (green) per day exhibited by each patient. C. Mean change in the activity of the
paretic limb with respect to baseline, across the three days of intervention (day 2-4), and post-test (day 5). The y-axis refers
to the change from baseline averaged across subjects.
lead to an over-encouraged behavior. In order to con-
trol for this effect, patients were instructed to wear
the system everyday but did not receive any type of
feedback at day 1 (i.e. pre-test or baseline) and day
5 (i.e. post-test) of the experimental protocol. From
day 2 to day 4 (i.e. intervention phase), the RGS-
wear system provided Hourly Feedback and Review
Feedback to the patient. Before (day 1) and after the
experiment (day 5), participants fulfilled an Intrinsic
Motivation Questionnaire (IMQ). The IMQ consisted
in 7 statements designed to capture changes in the pa-
tient’s perceived competence and effort when using
the paretic limb (see APPENDIX, Questionnaire on
Intrinsic Motivation). Answers were reported using a
7-point Likert Scale, ranging from Strongly Disagree
to Strongly Agree. In addition, a Usability Question-
naire (UQ) was administered at the end of the experi-
mental protocol (day 5) to assess the system’s accept-
ability in terms of its hardware design, graphical user
interface (GUI), interaction design, and perceived ef-
ficacy (see APPENDIX, Questionnaire on Usability).
In this questionnaire, answers were reported using a
5-point Likert Scale. The ethics committee of clinical
research of the Parc de Salut Mar approved experi-
mental guidelines.
2.5 Participants
For the validation of the accelerometers-based mea-
surement of arm use, we recruited 10 right-handed
volunteers without neurological impairments (5 fe-
males, mean age = 26.6±2.59 years old). Sec-
ondly, in order to explore the clinical impact of the
RGS-Wear, five chronic stroke patients were first ap-
proached by a doctor from the rehabilitation depart-
ment of Hospital Esperanc¸a in Barcelona to determine
their interest in participating in this research project.
Selected patients met the following inclusion criteria:
1) Ischaemic strokes (Middle cerebral artery territory)
and hemorrhagic strokes (intra-cerebral). 2) Mild-to-
moderate upper-limbs hemiparesis. 3) Age between
45 and 85 years old. 4) Absence of any major cogni-
tive impairments. 5) Frequent smartphone user. One
patient refused to participate. The remaining four pa-
tients (4 males, 70.5±6.76 years old) were included
in the study. Prior to the experiment, all participants
signed informed consent.
3.1 Accelerometer-based Measurement
of Arm Use
In order to evaluate the reliability and validity
of accelerometry for measuring arm use in non-
impaired subjects, we examined the subjects’ non-
dominant arm activity under 2 different conditions
(non-dominant and balanced), in four different iADLs
(washing the dishes, eating, buttoning up a shirt, and
walking). As we expected, in the non-dominant con-
dition, performance of iADLs was characterized by
the decreased activity of the left hand (Fig. 3). In
the walking task, activity measures fell below the in-
activity level in both conditions, indicating that the
mean acceleration of each hand was not superior to
the mean body acceleration. These results validate
the reliability of the RGS-Wear system for capturing
the amount of use of the upper-limbs in iADLs.
3.2 Effects on Amount of Use
After exploring the use of wearable devices for arm
use monitoring, we proceeded to investigate its appli-
cability as an intervention tool. Since amount of use
and recovery are tightly coupled, using wearable de-
vices to induce an increase in arm use could have a
positive impact in motor recovery. One approach to
pursue this idea is to use wearable devices to expose
the patient to arm movement related feedback, thus
increasing the intrinsic motivation for integrating the
paretic limb in the performance of iADLs.
NEUROTECHNIX 2015 - International Congress on Neurotechnology, Electronics and Informatics
Figure 5: Responses from questionnaires. A. Average scores quantifying the patient’s intrinsic motivation for using the paretic
limb in the performance of iADLs. B. Mean scores for each category assessed by the usability test.
To address this question, we first compared the
mean levels of Energy Expenditure (EE) estimated at
baseline (day 1) for each arm and subject (βin Eq.
1). As expected, we observed that all patients reached
higher EE values when using the less affected limb
(Figure 4A). Patient 3, who presented with mild hemi-
paresis, showed a highly balanced arm use, reaching a
mean EE value of 7.53 for the paretic limb, and 8.85
for the non-paretic limb. These preliminary results
support the use of accelerometry for quantifying arm
use in hemiparetic stroke patients. Next, we analyzed
the change in arm use balance respect to baseline (day
1). Although we observed differences between pa-
tients, the estimation of arm balance values remained
stable within subjects (Fig. 4B). Overall we found
a general increase in the Arm Use Balance, suggest-
ing an increased integration of the affected limb in
the performance of iADLs. However, since Arm Use
Balance is a relative measurement (see Eq. 4), it does
not express the amount of movement. A patient could
therefore achieve positive improvements in Arm Use
Balance by only limiting the movement of the non-
paretic limb. In order to take into account the patient’s
amount of arm movement, we analyzed the change in
the activity of the paretic limb with respect to baseline
(day 1). Interestingly, results revealed an increase in
activity which accumulated along the three days of in-
tervention (Fig. 4C). Even though we observed a drop
in activity at day 5 (post-test), when no feedback was
delivered any more, arm use improvements were still
partially retained.
3.3 Effects on the Patients’ Intrinsic
We analyzed the influence of the RGS-Wear paradigm
on intrinsic motivation by comparing the scores re-
ported by the four patients before (day 1) and after
the treatment (day 5). We observed that, after treat-
ment, 3 out of 4 patients exhibited higher intrinsic
motivation to use the paretic limb (Fig. 5A). Accord-
ing to the Self-Determination Theory, this subjective
improvement may emerge from the repetitive expo-
sure to knowledge of progress, a factor tightly linked
to the behavioral mediator Autonomy.
3.4 Usability
We studied the usability aspects of the RGS-Wear
through a questionnaire that was divided into 4 cat-
egories (5 questions each): hardware, graphical inter-
face, feedback, and perceived efficacy. Overall, the
patients’ ratings were above 3 (neutral), suggesting
that the system’s design was generally accepted. In-
terestingly, we noticed that the rating of hardware fea-
tures was notably lower in comparison to the other
categories. When we explored the patient’s answers
in detail, we found that those statements referring to
the comfort of putting the bracelets on received lower
scores from most of the patients.
We have presented results from a pilot study support-
ing the benefits of wearable-based feedback on arm
use. Our results suggest that frequent exposure to
direct feedback about arm use promotes the integra-
tion of the paretic limb in the performance of iADLs.
In addition, results from questionnaires revealed that
the use of wearable devices may influence positively
the patient’s intrinsic motivation for using the affected
The work we presented in this article is the con-
tinuation of our previous work on the use of new
technologies for counteracting learned non-use. In
a recent study, we used a neurologically grounded
A Wearable Bracelet Device for Promoting Arm Use in Stroke Patients
computational model of motor recovery (Han et al.,
2008) that can predict the positive influence of
reinforcement-based training on arm use (Ballester
et al., 2015a). In this work, we proposed that hand
selection is modulated by two main parameters: ex-
pected success and effort. We conducted two clini-
cal experiments that suggested that by increasing the
value of using the paretic limb (expected success) and
decreasing its cost (effort) we can promote its spon-
taneous use (Ballester et al., 2015b) and boost recov-
ery (Ballester et al., 2015a). Based on these findings,
we now explore how wearable devices could allow
the ubiquitous delivery of a variant of Reinforcement-
Induced Movement Therapy (RIMT).
Our results suggest that monitoring the amount of
arm use and providing knowledge of progress could
provide multiple benefits: 1) it may allow the pa-
tient to set-up implicit goals, and 2) it may increase
the value of using the paretic limb, therefore bias-
ing effector selection patterns. Thus, the repetitive
exposure to reinforcement-based feedback after per-
formance may modify both the individual’s goals and
self-representation. While the first may provide the
necessary context for the introduction of behavioral
changes, the second may consolidate them. Inter-
estingly, a recent controlled clinical trial including
156 acute stroke patients evaluated the clinical impact
of using wearable triaxial accelerometers at both an-
kles and recording continuously for 8 hours per day
(Dorsch et al., 2015). Once a week, participants in
the experimental group also reviewed the results of
their summary activity graphs with the therapists. Re-
sults indicated that the group receiving the augmented
feedback did not spend a greater amount of time walk-
ing. This findings seem to be contrary to our re-
sults. This difference can be explained by three fac-
tors: 1) the RGS-Wear provided frequent daily feed-
back about performance and progress, 2) the patients
using RGS-Wear reviewed their activity feedback au-
tonomously and 3) the RGS-Wear was applied on the
upper-extremities while Dorsch, et al. focused in gait
and lower-extremities.
Future work aims at validating the impact of RGS-
Wear in arm use by conducting a controlled longitu-
dinal clinical study on acute stroke patients. In this
study we plan to measure the retention of improve-
ments in arm use induced by the RGS-Wear, and its
consequent influence on motor recovery.
We would like to acknowledge all patients who par-
ticipated in this study. Special thanks to Dr. Boza
omez for her assistance in recruiting stroke patients.
This project was supported through ERC project
cDAC (FP7-IDEAS-ERC 341196), EC H2020 project
socSMCs (H2020-EU.1.2.2. 641321) and MINECO
project SANAR (Gobierno de Espaa).
Ballester, B. R., Maier, M., Duff, A., San Segundo,
R., Casta˜
neda, V., and Verschure, P. (2015a).
Reinforcement-Induced Movement Therapy: A novel
approach for overcoming learned non-use in chronic
stroke patients. In Proceedings of the International
Conference on Virtual Rehabilitation. In press., pages
Ballester, B. R., Nirme, J., Duarte, E., Cuxart, A., Ro-
driguez, S., Verschure, P., and Duff, A. (2015b).
The visual amplification of goal-oriented movements
counteracts acquired non-use in hemiparetic stroke
patients. Journal of neuroengineering and rehabili-
tation, 12(1):50.
Dorsch, a. K., Thomas, S., Xu, X., Kaiser, W., Dobkin,
B. H., Emara, T., Edwards, D., Fonzetti, P., Maasch,
J., Lee, S.-G., Owolabi, M. O., Hamzat, T. K.,
LeBlanc, C. J., Morse, R., Swaminathan, N., Karatas,
G. K., Boza, R., Brown, a. W., Miyai, I., Kawano, T.,
Chen, S.-Y., Hanger, H. C., Zucconi, C., Mammi, S.,
Ghislanzoni, C., Juan, F., and Lang, C. E. (2015). SIR-
RACT: An International Randomized Clinical Trial of
Activity Feedback During Inpatient Stroke Rehabili-
tation Enabled by Wireless Sensing. Neurorehabilita-
tion and Neural Repair, 29(5):407–415.
Gubbi, J., Rao, A. S., Fang, K., Yan, B., and Palaniswami,
M. (2013). Motor recovery monitoring using accel-
eration measurements in post acute stroke patients.
Biomedical engineering online, 12:33.
Han, C. E., Arbib, M. A., and Schweighofer, N. (2008).
Stroke rehabilitation reaches a threshold. PLoS com-
putational biology, 4(8):e1000133.
Hidaka, Y., Han, C. E., Wolf, S. L., Winstein, C. J., and
Schweighofer, N. (2012). Use it and improve it or
lose it: interactions between arm function and use
in humans post-stroke. PLoS computational biology,
Lai, S.-M., Studenski, S., Duncan, P. W., and Perera, S.
(2002). Persisting consequences of stroke measured
by the Stroke Impact Scale. Stroke, 33(7):1840–1844.
Markopoulos, P., Timmermans, A. A. A., Beursgens, L.,
Van Donselaar, R., and Seelen, H. A. M. (2011). Us’
em: The user-centered design of a device for motivat-
ing stroke patients to use their impaired arm-hand in
daily life activities. In Engineering in Medicine and
Biology Society, EMBC, 2011 Annual International
Conference of the IEEE, pages 5182–5187. IEEE.
oiv, M., Rodgers, H., and Price, C. I. (2014). Ac-
celerometer measurement of upper extremity move-
ment after stroke: a systematic review of clinical stud-
ies. Journal of neuroengineering and rehabilitation,
NEUROTECHNIX 2015 - International Congress on Neurotechnology, Electronics and Informatics
Ryan, R. M., Patrick, H., Deci, E. L., and Williams,
G. C. (2008). Facilitating health behaviour change
and its maintenance: Interventions based on self-
determination theory. European Health Psychologist,
Taub, E. and Uswatte, G. (2003). Constraint-induced move-
ment therapy: bridging from the primate laboratory to
the stroke rehabilitation laboratory. Journal of Reha-
bilitation Medicine, 35:34–40.
Tsurumi, K., Itani, T., Tachi, N., Takanishi, T., Suzumura,
H., and Takeyama, H. (2002). Estimation of En-
ergy Expenditure during Sedentary Work with Up-
per Limb Movement. Journal of occupational health,
Uswatte, G., Foo, W. L., Olmstead, H., Lopez, K., Holand,
A., and Simms, L. B. (2005). Ambulatory monitoring
of arm movement using accelerometry: an objective
measure of upper-extremity rehabilitation in persons
with chronic stroke. Archives of physical medicine
and rehabilitation, 86(7):1498–1501.
Uswatte, G., Taub, E., Morris, D., Light, K., and Thompson,
P. A. (2006). The Motor Activity Log-28 assessing
daily use of the hemiparetic arm after stroke. Neurol-
ogy, 67(7):1189–1194.
van der Pas, S. C., Verbunt, J. A., Breukelaar, D. E., van
Woerden, R., and Seelen, H. A. (2011). Assessment
of arm activity using triaxial accelerometry in patients
with a stroke. Archives of physical medicine and re-
habilitation, 92(9):1437–1442.
Wang, Q., Chen, W., and Markopoulos, P. (2014). Literature
review on wearable systems in upper extremity reha-
bilitation. In IEEE-EMBS International Conference
on Biomedical and Health Informatics (BHI), pages
551–555. IEEE.
Questionnaire on Intrinsic Motivation: 1- Inte-
grating the affected in the performance of activities
of daily living allows me to be more independent.
2- I’m quite competent when I use my affected arm.
3- It’s really tiring to use the affected arm in my
activities of daily living. 4- I feel secure when I use
the affected arm for eating. 5- I feel secure when I
use the affected arm for washing the dishes. 6- I feel
secure when I use the affected arm for dressing up.
7- How much do you use the affected arm?
Questionnaire on Usability: 1- It was easy to put on
the bracelets without help. 2- It was easy to put on
the smartphone without help. 3- It was comfortable
to wear the bracelets. 4- It was comfortable to wear
the smartphone. 5- It was easy to move the affected
arm while wearing the bracelets. 6- It was easy to
hear the messages alarm on the smartphone. 7- It was
easy to notice the bracelets vibrations and lights. 8- It
was easy to understand the messages on the screen of
the smartphone. 9- It was ease to press the buttons on
the screen of the smartphone. 10- It was easy to read
the texts appearing on the screen of the smartphone.
11- I think I missed more than 3 messages a day. 12-
Sometimes the messages were annoying. 13- Some-
times the messages scared me. 14- The hourly feed-
back about the amount of movement was correct. 15-
Feedback about the amount of arm movement across
days was correct. 16- I think the messages were ac-
curate in reporting my activity. 17- I think the levels
of activity reported by the messages were lower than
my real activity level. 18- I think the levels of activity
reported by the messages were higher than my real ac-
tivity level. 19- There were too many messages along
the day. 20- I would like to receive more messages.
A Wearable Bracelet Device for Promoting Arm Use in Stroke Patients
... Participants [21,23,25,26,[28][29][30][32][33][34]38,40,[44][45][46][47]51,52,54,56,58,60,62,65,67,69,71,73,74,76,77,79,80,82,85,86,90] Stroke survivors [20,31,39,42,43,55,59,61,78] Healthy participants [22,24,27,[35][36][37]41,[48][49][50]53,57,63,64,66,68,70,72,75,81,83,84,[87][88][89] Stroke survivors and healthy participants Stage of recovery [35,73,82,89] Subacute [21,23,[26][27][28][29][30][32][33][34]38,40,41,44,46,47,51,52,56,58,60,62,65,68,69,71,74,75,79,80,[83][84][85]87,88,90] Chronic [22,24,25,36,37,45,50,53,54,57,63,64,66,67,72,77,86,91] Subacute and chronic ...
... Participants [21,23,25,26,[28][29][30][32][33][34]38,40,[44][45][46][47]51,52,54,56,58,60,62,65,67,69,71,73,74,76,77,79,80,82,85,86,90] Stroke survivors [20,31,39,42,43,55,59,61,78] Healthy participants [22,24,27,[35][36][37]41,[48][49][50]53,57,63,64,66,68,70,72,75,81,83,84,[87][88][89] Stroke survivors and healthy participants Stage of recovery [35,73,82,89] Subacute [21,23,[26][27][28][29][30][32][33][34]38,40,41,44,46,47,51,52,56,58,60,62,65,68,69,71,74,75,79,80,[83][84][85]87,88,90] Chronic [22,24,25,36,37,45,50,53,54,57,63,64,66,67,72,77,86,91] Subacute and chronic ...
... Arm use [82] The ratio between the duration of movement in the least and less affected arm [83,85] Mean squared sum of the acceleration over a 1-minute epoch of the arm [84] Torques due to the measured tangential forces on the split-steering wheel Arm nonuse [86] The difference of the Euclidean distance between the trunk and hand to the target [87] Movement time, peak velocity, total displacement, and movement smoothness [88] Root mean square of the rotation angle of the steering wheel [89] Total movement duration of each limb and the ratio between the movement duration in the paretic and nonparetic limb [81] Amplitude, time, and frequency data from inertial sensors on upper body Interlimb coordination ...
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Background: Upper extremity (UE) impairment affects up to 80% of stroke survivors and accounts for most of the rehabilitation after discharge from the hospital release. Compensation, commonly used by stroke survivors during UE rehabilitation, is applied to adapt to the loss of motor function and may impede the rehabilitation process in the long term and lead to new orthopedic problems. Intensive monitoring of compensatory movements is critical for improving the functional outcomes during rehabilitation. Objective: This review analyzes how technology-based methods have been applied to assess and detect compensation during stroke UE rehabilitation. Methods: We conducted a wide database search. All studies were independently screened by 2 reviewers (XW and YF), with a third reviewer (BY) involved in resolving discrepancies. The final included studies were rated according to their level of clinical evidence based on their correlation with clinical scales (with the same tasks or the same evaluation criteria). One reviewer (XW) extracted data on publication, demographic information, compensation types, sensors used for compensation assessment, compensation measurements, and statistical or artificial intelligence methods. Accuracy was checked by another reviewer (YF). Four research questions were presented. For each question, the data were synthesized and tabulated, and a descriptive summary of the findings was provided. The data were synthesized and tabulated based on each research question. Results: A total of 72 studies were included in this review. In all, 2 types of compensation were identified: disuse of the affected upper limb and awkward use of the affected upper limb to adjust for limited strength, mobility, and motor control. Various models and quantitative measurements have been proposed to characterize compensation. Body-worn technology (25/72, 35% studies) was the most used sensor technology to assess compensation, followed by marker-based motion capture system (24/72, 33% studies) and marker-free vision sensor technology (16/72, 22% studies). Most studies (56/72, 78% studies) used statistical methods for compensation assessment, whereas heterogeneous machine learning algorithms (15/72, 21% studies) were also applied for automatic detection of compensatory movements and postures. Conclusions: This systematic review provides insights for future research on technology-based compensation assessment and detection in stroke UE rehabilitation. Technology-based compensation assessment and detection have the capacity to augment rehabilitation independent of the constant care of therapists. The drawbacks of each sensor in compensation assessment and detection are discussed, and future research could focus on methods to overcome these disadvantages. It is advised that open data together with multilabel classification algorithms or deep learning algorithms could benefit from automatic real time compensation detection. It is also recommended that technology-based compensation predictions be explored.
... Several studies (Piraniy et Ballester et al., 2015) demonstrated the potential of IoTenabled wearable devices to provide real-time health monitoring, personalized feedback, and improved patient outcomes. These devices have shown promising results in managing various health conditions, such as Alzheimer's disease, Parkinson's disease, diabetes, and stroke. ...
The Internet of Things (IoT) has revolutionized various aspects of our daily lives, particularly in the healthcare sector. The integration of IoT with wearable devices has opened up new possibilities for healthcare monitoring, enabling the continuous tracking of patients' physiological parameters and promoting personalized medical care. This systematic review examines the current landscape of IoT-enabled wearable devices for healthcare monitoring, their potential applications, and the associated challenges. We conducted a thorough literature search to identify the most relevant and recent studies on IoT-enabled wearable devices for healthcare monitoring. Several devices were discussed, including smartwatches, fitness trackers, wearable electrocardiogram (ECG) monitors, continuous glucose monitoring systems, and smart patches for vital sign monitoring. These wearables offer numerous advantages, such as real-time monitoring, improved patient adherence, early detection of potential health issues, and enhanced patient-physician communication. The review also explores the potential drawbacks and challenges of implementing IoT-enabled wearable devices in healthcare, such as data privacy concerns, device interoperability, and the need for standardized data collection and analysis methods. Moreover, we discuss potential solutions and future research directions to overcome these challenges and promote the widespread adoption of IoT-enabled wearables for healthcare monitoring. In conclusion, IoT-enabled wearable devices have the potential to transform the healthcare sector by facilitating remote patient monitoring, improving treatment outcomes, and reducing healthcare costs. However, addressing the existing challenges and incorporating user feedback in the design and development process is essential for the successful integration of IoT-enabled wearables into the healthcare ecosystem.
... Unaware of the manipulations, participants reported making internal attributions of the success they experienced through training and showed a higher probability of using their more affected arm (Figure 2B). We obtained similar results in a pilot study evaluating the effect of using an accelerometerembedded bracelet device paired with ecological momentary assessment ("EMA" delivered on a smartphone) to monitor the amount of arm use and provide knowledge of progress in chronic stroke survivors (47). Participants received hourly haptic feedback and visual activity reports indicating the change from baseline in paretic arm use. ...
Full-text available
Large doses of movement practice have been shown to restore upper extremities' motor function in a significant subset of individuals post-stroke. However, such large doses are both difficult to implement in the clinic and highly inefficient. In addition, an important reduction in upper extremity function and use is commonly seen following rehabilitation-induced gains, resulting in “rehabilitation in vain”. For those with mild to moderate sensorimotor impairment, the limited spontaneous use of the more affected limb during activities of daily living has been previously proposed to cause a decline of motor function, initiating a vicious cycle of recovery, in which non-use and poor performance reinforce each other. Here, we review computational, experimental, and clinical studies that support the view that if arm use is raised above an effective threshold, one enters a virtuous cycle in which arm use and function can reinforce each other via self-practice in the wild. If not, one enters a vicious cycle of declining arm use and function. In turn, and in line with best practice therapy recommendations, this virtuous/vicious cycle model advocates for a paradigm shift in neurorehabilitation whereby rehabilitation be embedded in activities of daily living such that self-practice with the aid of wearable technology that reminds and motivates can enhance paretic limb use of those who possess adequate residual sensorimotor capacity. Altogether, this model points to a user-centered approach to recovery post-stroke that is tailored to the participant's level of arm use and designed to motivate and engage in self-practice through progressive success in accomplishing meaningful activities in the wild.
... Remote monitoring using wearable sensors and/or mobile apps has potential as both intervention and assessment tools. 19,20 However, what data are collected and how these are fed back to feedback to participants should be considered. Research in this area will be further boosted by the integration of artificial intelligence techniques for data processing, classification, and adaptive control. ...
Aims The aim of this rapid review and opinion paper is to present the state of the current evidence and present future directions for telehealth research and clinical service delivery for stroke rehabilitation. Methods We conducted a rapid review of published trials in the field. We searched Medline using key terms related to stroke rehabilitation and telehealth or virtual care. We also searched clinical trial registers to identify key ongoing trials. Results The evidence for telehealth to deliver stroke rehabilitation interventions is not strong and is predominantly based on small trials prone to Type 2 error. To move the field forward, we need to progress to trials of implementation that include measures of adoption and reach, as well as effectiveness. We also need to understand which outcome measures can be reliably measured remotely, and/or develop new ones. We present tools to assist with the deployment of telehealth for rehabilitation after stroke. Conclusion The current, and likely long-term, pandemic means that we cannot wait for stronger evidence before implementing telehealth. As a research and clinical community, we owe it to people living with stroke internationally to investigate the best possible telehealth solutions for providing the highest quality rehabilitation.
... Wearable sensors help in the continuous, uninterrupted, and objective measurement of arm movements in natural settings. [11][12][13][14] Popular wearable sensors like inertial measurement units are attached to the forearm, 15,16 upper arm, 17,18 or fingers 19,20 of the participant to record its linear acceleration and angular velocity. The recorded sensor data is generated from both functional movements like activities of daily living, and non-functional movements like arm swing during ambulation. ...
Full-text available
Introduction Accelerometry-based activity counting for measuring arm use is prone to overestimation due to non-functional movements. In this paper, we used an inertial measurement unit (IMU)-based gross movement (GM) score to quantify arm use. Methods In this two-part study, we first characterized the GM by comparing it to annotated video recordings of 5 hemiparetic patients and 10 control subjects performing a set of activities. In the second part, we tracked the arm use of 5 patients and 5 controls using two wrist-worn IMUs for 7 and 3 days, respectively. The IMU data was used to develop quantitative measures (total and relative arm use) and a visualization method for arm use. Results From the characterization study, we found that GM detects functional activities with 50–60% accuracy and eliminates non-functional activities with >90% accuracy. Continuous monitoring of arm use showed that the arm use was biased towards the dominant limb and less paretic limb for controls and patients, respectively. Conclusions The gross movement score has good specificity but low sensitivity in identifying functional activity. The at-home study showed that it is feasible to use two IMU-watches to monitor relative arm use and provided design considerations for improving the assessment method. Clinical trial registry number: CTRI/2018/09/015648
... We have successfully deployed such an approach in the domain of stroke rehabilitation. We have successfully deployed such an approach in the domain of stroke rehabilitation [64,65]. To this end, in future studies, we shall more systematically investigate the specific factors that may influence the participants' affective ratings, including personality type, as well as other symptoms that might indicate abnormal psychological states, such as insomnia. ...
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The COVID-19 crisis resulted in a large proportion of the world’s population having to employ social distancing measures and self-quarantine. Given that limiting social interaction impacts mental health, we assessed the effects of quarantine on emotive perception as a proxy of affective states. To this end, we conducted an online experiment whereby 112 participants provided affective ratings for a set of normative images and reported on their well-being during COVID-19 self-isolation. We found that current valence ratings were significantly lower than the original ones from 2015. This negative shift correlated with key aspects of the personal situation during the confinement, including working and living status, and subjective well-being. These findings indicate that quarantine impacts mood negatively, resulting in a negatively biased perception of emotive stimuli. Moreover, our online assessment method shows its validity for large-scale population studies on the impact of COVID-19 related mitigation methods and well-being.
... Wearable sensors help in the continuous, uninterrupted, objective measurement of arm-use in a natural setting [10][11][12][13] . Accelerometry-based activity counts that detect any upper-limb movement is currently the most popular measurement of arm-use [14][15][16][17][18] . ...
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Background: The most popular method for measuring upper limb activity is based on accelerometry. However, this method is prone to overestimation and is agnostic to the functional utility of a movement. In this study, we used an inertial measurement unit(IMU)-based gross movement score to quantify arm-use in hemiparetic patients at home. Objectives: (i) Validate the gross movement score detected by wrist-worn IMUs against functional movements identified by human assessors. (ii) Test the feasibility of using wrist-worn IMUs to measure arm-use in patients' natural settings. Methods: To validate the gross movement score two independent assessors analyzed and annotated the video recordings of 5 hemiparetic patients and 10 healthy controls performing a set of activities while wearing IMUs. The second study tracked arm-use of 5 hemiparetic patients and 5 healthy controls using two wrist-worn IMUs for 7 days and 3 days, respectively. The IMU data obtained from this study was used to develop quantitative measures (total and relative arm-use (RAU)) and a visualization method for arm-use. Results: The gross movement score detects functional movement with 50-60% accuracy in hemiparetic patients, and is robust to non-functional movements. Healthy controls showed a slight bias towards the dominant arm (RAU: 40.52)°. Patients' RAU varied between 15-47° depending upon their impairment level and pre-stroke hand dominance. Conclusions: The gross movement score performs moderately well in detecting functional movements while rejecting non-functional movements. The patients' total arm-use is less than healthy controls, and their relative arm-use is skewed towards the less-impaired arm.
After a stroke, a great number of patients experience persistent motor impairments such as hemiparesis or weakness in one entire side of the body. As a result, the lack of use of the paretic limb might be one of the main contributors to functional loss after clinical discharge. We aim to reverse this cycle by promoting the use of the paretic limb during activities of daily living (ADLs). To do so, we describe the key components of a system composed of a wearable bracelet (i.e., a smartwatch) and a mobile phone, designed to bring a set of neurorehabilitation principles that promote acquisition, retention and generalization of skills to the home of the patient. A fundamental question is whether the loss in motor function derived from learned–non–use may emerge as a consequence of decision–making processes for motor optimization. Our system is based on well-established rehabilitation strategies that aim to reverse this behaviour by increasing the reward associated with action execution and implicitly reducing the expected cost of using the paretic limb, following the notion of reinforcement–induced movement therapy (RIMT). Here we validate an accelerometer-based measure of arm use and its capacity to discriminate different activities that require increasing movement of the arm. The usability and acceptance of the device as a rehabilitation tool is tested using a battery of self–reported and objective measurements obtained from acute/subacute patients and healthy controls. We believe that an extension of these technologies will allow for the deployment of unsupervised rehabilitation paradigms during and beyond hospitalization time.
Conference Paper
Full-text available
Given the high prevalence of cognitive impairment and functional dependence after Acquired Brain Injury (such as stroke and traumatic brain injury), degenerative conditions (such as dementia and multiple sclerosis) and psychiatric disorders (such as post-traumatic stress disorders and schizophrenia), finding effective cognitive assessment and rehabilitation solutions has been a primary goal for many research studies in the field of Virtual Reality (VR). Existent cognitive assessment and rehabilitation approaches rely on theoretically valid principles, however paper-and-pencil tasks with static stimuli, may be demotivating and lack minimal resemblance to everyday life demands. The issue of ecological validity started being discussed in 1982 when Neisser argued that cognitive psychology experiments were conducted in artificial settings and employed measures with no counterparts in everyday life. In opposition, Banaji and Crowder (1989) advocated that ecological approaches lack the internal validity and experimental control needed for scientific progress. In 1996, Franzen and Wilhelm conceptualized ecological validity as having two aspects; veridicality, in which the person's performance on a construct-driven measure should predict some feature(s) of the person's everyday life functioning, and verisimilitude, in which the requirements of a neuropsychological measure and the testing conditions should resemble requirements found in a person's activities of daily living (ADLs). Since then, the tradeoff and the search for a balance between everyday activities and laboratory control has a long history. One methodology that has potential for a laboratory vs. everyday functioning rapprochement is VR. Performance of many ADLs, such as doing the groceries, implies getting to outdoor locations, such as supermarkets or shopping malls. Walking across streets, street crossing and driving are demanding tasks that require multiple and complex cognitive and motor skills that are commonly impaired. Their practice in real environments can be dangerous because of intrinsic hazards such as traffic or pedestrians, and are extremely resource-intensive in terms of staff management and financial costs, which are scarce in most clinics. These limitations have motivated the use of VR to safely recreate different scenarios such as streets, kitchens, cities, supermarkets, apartments and offices. VR allows precise presentation and control of dynamic stimuli, providing ecologically valid experiences that combine the control and rigor of laboratory measures with a simulation that depicts real life situations in a balance between naturalistic observation and the need for control key variables. Indeed, over the last years, VR-based methodologies have been developed as promising solutions to improve cognitive and motor functions, via immersive and non-immersive technologies. This symposium goal is to highlight the potential of VR environments for enhanced ecological validity in cognitive assessment and rehabilitation.
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Health is one of the most important aspects of life. However, people still could not get proper health services. It is caused by limitation to the used technology in hospitals and limitations to get to the hospital. Internet-of-Things (IoT) as one of the most trending topics nowadays, already giving so many solutions in many ways, for example, in healthcare. IoT is implemented in many healthcare ways, including detecting disease as a precaution, treating a disease as a healing solution, and monitoring a disease as a healing process itself. As part of IoT, wearable devices have been developed to help people get the right treatment for themselves. In this paper, a systematic literature review of wearable IoT devices for healthcare is conducted. The review is started by searching the references by pre-defined keywords and filtering the references based on the relevance and the published year. In this review, we discuss the utilization of wearable devices in handling the problem in the healthcare aspects, including disease detection, monitoring, and curing. Other than that, this review paper also explained how the architecture of wearable devices applied. Finally, the future challenge of research in this field is defined.
Conference Paper
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An open question in stroke rehabilitation is, if and how chronic patients can still make improvements after they reached a plateau in motor recovery. Previous research has shown that Constraint-Induced Movement Therapy (CIMT) might be effective in treating hemiparesis and supporting functional improvements in chronic patients, but that it might also be associated with higher costs in terms of demand, resources and inconvenience for the patient. Here, we offer a new therapeutic approach that combines CIMT with a positive reinforcement component. We suggest that this new therapy, called Reinforcement-Induced Movement Therapy (RIMT), might be similarly effective as CIMT and could be suitable for a broader population of chronic stroke patients. We first implemented a computational model to study the potential outcome of different CIMT and RIMT therapy combinations. Then we present the results of an ongoing clinical trial that supports predictions from the model. We conclude that an optimally combined CIMT and RIMT therapy might propose a novel and powerful rehabilitation approach, addressing the specific needs of chronic stroke patients.
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Stroke-induced impairments result from both primary and secondary causes, i.e. damage to the brain and the acquired non-use of the impaired limbs. Indeed, stroke patients often under-utilize their paretic limb despite sufficient residual motor function. We hypothesize that acquired non-use can be overcome by reinforcement-based training strategies. Hemiparetic stroke patients (n = 20, 11 males, 9 right-sided hemiparesis) were asked to reach targets appearing in either the real world or in a virtual environment. Sessions were divided into 3 phases: baseline, intervention and washout. During the intervention the movement of the virtual representation of the patients' paretic limb was amplified towards the target. We found that the probability of using the paretic limb during washout was significantly higher in comparison to baseline. Patients showed generalization of these results by displaying a more substantial workspace in real world task. These gains correlated with changes in effector selection patterns. The amplification of the movement of the paretic limb in a virtual environment promotes the use of the paretic limb in stroke patients. Our findings indicate that reinforcement-based therapies may be an effective approach for counteracting learned non-use and may modulate motor performance in the real world.
Conference Paper
Full-text available
This paper reports a structured literature survey of research in wearable technology for upper-extremity rehabilitation, e.g., after stroke, spinal cord injury, for multiple sclerosis patients or even children with cerebral palsy. A keyword based search returned 61 papers relating to this topic. Examination of the abstracts of these papers identified 19 articles describing distinct wearable systems aimed at upper extremity rehabilitation. These are classified in three categories depending on their functionality: movement and posture monitoring; monitoring and feedback systems that support rehabilitation exercises, serious games for rehabilitation training. We characterize the state of the art considering respectively the reported performance of these technologies, availability of clinical evidence, or known clinical applications.
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The aim of this review was to identify and summarise publications, which have reported clinical applications of upper limb accelerometry for stroke within free-living environments and make recommendations for future studies. Data was searched from MEDLINE®, Scopus, IEEExplore and Compendex databases. The final search was 31st October 2013. Any study was included which reported clinical assessments in parallel with accelerometry in a free- living hospital or home setting. Study quality is reflected by participant numbers, methodological approach, technical details of the equipment used, blinding of clinical measures, whether safety and compliance data was collected. First author screened articles for inclusion and inclusion of full text articles and data extraction was confirmed by the third author. Out of 1375 initial abstracts, 8 articles were included. All participants were stroke patients. Accelerometers were worn for either 24 hours or 3 days. Data were collected as summed acceleration counts over a specified time or as the duration of active/inactive periods. Activity in both arms was reported by all studies and the ratio of impaired to unimpaired arm activity was calculated in six studies. The correlation between clinical assessments and accelerometry was tested in five studies and significant correlations were found. The efficacy of a rehabilitation intervention was assessed using accelerometry by three studies: in two studies both accelerometry and clinical test scores detected a post-treatment difference but in one study accelerometry data did not change despite clinical test scores showing motor and functional improvements. Further research is needed to understand the additional value of accelerometry as a measure of upper limb use and function in a clinical context. A simple and easily interpretable accelerometry approach is required.
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Estimation of Energy Expenditure during Sedentary Work with Upper Limb Movement: Kunio TSURUMI, et al. Health Sciences of Life, Work and Environment, Department of Environmental Health Science and Health Promotion, Graduate School of Medical Sciences, Nagoya City University—This study aims to evaluate the availability of surface- electrode electromyogram (EMG) and acceleration to predict energy expenditure during sedentary work with upper limb movement. The following variables were measured in 12 female subjects: oxygen consumption (VO2), heart rate, EMG from the medial and anterior part of the deltoid muscle, and acceleration of wrist movement. The subjects were requested to perform four different sedentary tasks. In tasks 1, 2 and 3, subjects touched two points on a table (height 70 cm) alternatively. The distance between the two points was 50 cm in tasks 1 and 3, and 100 cm in task 2. The frequency of the movement was 100 touches per minute in tasks 1 and 2, and 152 touches in task 3. In task 4, the points were located vertically on a wall, so they had to move their upper limb vertically in this task. The height of the points was 10 cm below and 40 cm above the acromion height of the subject, and task frequency was 100 touches per minute. The correlation coefficient was 0.285, 0.581 and 0.676, between VO2 and heart rates, VO2 and acceleration, and VO2 and EMG from the deltoid, respectively. The coefficient of determination was 0.648, when employing multiple regression analysis, with acceleration and EMG as independent variables. These results suggest that energy expenditure during sedentary work with upper limb movement can be well estimated by using the data from acceleration of wrist movement and the EMG of
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Background Stroke is one of the major causes of morbidity and mortality. Its recovery and treatment depends on close clinical monitoring by a clinician especially during the first few hours after the onset of stroke. Patients who do not exhibit early motor recovery post thrombolysis may benefit from more aggressive treatment. Method A novel approach for monitoring stroke during the first few hours after the onset of stroke using a wireless accelerometer based motor activity monitoring system is developed. It monitors the motor activity by measuring the acceleration of the arms in three axes. In the presented proof of concept study, the measured acceleration data is transferred wirelessly using iMote2 platform to the base station that is equipped with an online algorithm capable of calculating an index equivalent to the National Institute of Health Stroke Score (NIHSS) motor index. The system is developed by collecting data from 15 patients. Results We have successfully demonstrated an end-to-end stroke monitoring system reporting an accuracy of calculating stroke index of more than 80%, highest Cohen’s overall agreement of 0.91 (with excellent κ coefficient of 0.76). Conclusion A wireless accelerometer based ‘hot stroke’ monitoring system is developed to monitor the motor recovery in acute-stroke patients. It has been shown to monitor stroke patients continuously, which has not been possible so far with high reliability.
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"Use it and improve it, or lose it" is one of the axioms of motor therapy after stroke. There is, however, little understanding of the interactions between arm function and use in humans post-stroke. Here, we explored putative non-linear interactions between upper extremity function and use by developing a first-order dynamical model of stroke recovery with longitudinal data from participants receiving constraint induced movement therapy (CIMT) in the EXCITE clinical trial. Using a Bayesian regression framework, we systematically compared this model with competitive models that included, or not, interactions between function and use. Model comparisons showed that the model with the predicted interactions between arm function and use was the best fitting model. Furthermore, by comparing the model parameters before and after CIMT intervention in participants receiving the intervention one year after randomization, we found that therapy increased the parameter that controls the effect of arm function on arm use. Increase in this parameter, which can be thought of as the confidence to use the arm for a given level of function, lead to increase in spontaneous use after therapy compared to before therapy.
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Stroke leaves the majority of its survivors with an impairment of the upper extremity that affects their ability to live independently and their quality of life. Rehabilitation research shows that practice of everyday life activities in a natural context may sustain or even improve arm-hand performance, even during chronic stages after stroke. Based on this insight we designed, developed and evaluated Us'em; this consists of two watch-like accelerometry devices that provide feedback to stroke patients regarding the usage of their impaired versus their non-affected upper extremity. System usability and treatment credibility/expectancy were evaluated positively by therapists and patients.
To study the validity of accelerometry in the assessment of arm activity of patients with impaired arm function after stroke. Cross-sectional concurrent validity study. Rehabilitation center. Patients (N=45) at different stages after stroke. Not applicable. All patients wore 2 triaxial accelerometers around their wrists during 3 consecutive days. Arm activity was assessed, based on unilateral (activity of the affected arm) and bilateral accelerometry (ratio between the activity of the affected and nonaffected arm). The Motor Activity Log-26 (MAL-26) Amount of Use (AOU) scale was used as the main external criterion to test the concurrent validity of arm accelerometry. In addition, the MAL-26 Quality of Movement (QOM) scale and the Stroke Impact Scale (SIS) subscale Hand Function were used. To test the divergent validity, the SIS subscale Mobility was used. Spearman correlation coefficients were calculated. In an additional regression analysis, the hypothesized confounding influence of spasm, therapy intensity, and interobserver differences was studied. Both unilateral (ρ=.58, P<.001) and bilateral (ρ=.60, P<.001) accelerometry were significantly related to the MAL-AOU scale. Associations of both unilateral and bilateral accelerometry with the MAL-QOM and SIS subscale Hand Function corroborated these findings. The SIS subscale Mobility was not significantly associated with unilateral accelerometry (ρ=.41, P=.01) or bilateral accelerometry (ρ=.23, P=.11). None of the hypothesized confounders influenced these associations significantly. Based on the results, both the concurrent and divergent validity of unilateral and bilateral arm accelerometry for measuring arm activity after stroke are good.