Post-Stroke Rehabilitation Monitoring Using Wireless Surface Electromyography: A Case Study

Author(s):  
Arvind C Ganesh ◽  
B S Renganathan ◽  
C Rajakumaran ◽  
S P Preejith ◽  
Khandelwal Shubham ◽  
...  
2019 ◽  
Vol 40 (1) ◽  
pp. 51-57
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D. Simpson ◽  
M. Ehrensberger ◽  
P. Broderick ◽  
F. Horgan ◽  
C. Blake ◽  
...  

2021 ◽  
Author(s):  
Waidah Ismail ◽  
Crina Grosan ◽  
Zul Hilmi Abdullah ◽  
Ali Y. Aldailamy ◽  
Nurezayana Zainal ◽  
...  

2018 ◽  
Vol 9 ◽  
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Alexander A. Frolov ◽  
Pavel D. Bobrov ◽  
Elena V. Biryukova ◽  
Anna V. Silchenko ◽  
Anna A. Kondur ◽  
...  

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Brandon Hays ◽  
Antuan Vazquez ◽  
Joshua Mena ◽  
James Sauerwald ◽  
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2014 ◽  
Vol 4 (1) ◽  
pp. 71 ◽  
Author(s):  
Amirur Rehman ◽  
Jocelyn Berry ◽  
Muhammad Siddiqui

2021 ◽  
Vol 4 (2) ◽  
pp. 32
Author(s):  
Heather A. Feldner ◽  
Christina Papazian ◽  
Keshia M. Peters ◽  
Claire J. Creutzfeldt ◽  
Katherine M. Steele

Arm recovery varies greatly among stroke survivors. Wearable surface electromyography (sEMG) sensors have been used to track recovery in research; however, sEMG is rarely used within acute and subacute clinical settings. The purpose of this case study was to describe the use of wireless sEMG sensors to examine changes in muscle activity during acute and subacute phases of stroke recovery, and understand the participant’s perceptions of sEMG monitoring. Beginning three days post-stroke, one stroke survivor wore five wireless sEMG sensors on his involved arm for three to four hours, every one to three days. Muscle activity was tracked during routine care in the acute setting through discharge from inpatient rehabilitation. Three- and eight-month follow-up sessions were completed in the community. Activity logs were completed each session, and a semi-structured interview occurred at the final session. The longitudinal monitoring of muscle and movement recovery in the clinic and community was feasible using sEMG sensors. The participant and medical team felt monitoring was unobtrusive, interesting, and motivating for recovery, but desired greater in-session feedback to inform rehabilitation. While barriers in equipment and signal quality still exist, capitalizing on wearable sensing technology in the clinic holds promise for enabling personalized stroke recovery.


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