Cited by 82Stroke Rehabilitation and RecoveryContext-Aware Activity Recognition SystemsProsthetics and Rehabilitation Robotics

Using Wearable Sensors to Measure Motor Abilities following Stroke

Hester, T., Hughes, R., Sherrill, D.M., Knorr, B., Akay, M., Stein, J., Bonato, P.·2006·International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06)

Abstract

Motor abilities of stroke survivors are often severely affected. Post-stroke rehabilitation is guided by the use of clinical assessments of motor abilities. Clinical assessment scores can be predicted by models based on features extracted from the wearable sensor data. Wearable sensors would allow monitoring of subjects in the home and provide accurate assessments to guide the rehabilitation process. We propose the use of a wearable sensor system to assess the motor abilities of stroke victims. Preliminary results from twelve subjects show the ability of this system to predict clinical scores of motor abilities

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Crossref · 10.1109/bsn.2006.57

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3 of 3 references verified0 citation errors · 0 not checkable

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Authors
Hester, T., Hughes, R., Sherrill, D. M., Knorr, B., Akay, M., Stein, J., & Bonato, P.
Affiliations
  • Harvard University (US)
  • Dartmouth College (US)
  • Arizona State University (US)
Venue
International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06)
Publisher
IEEE
Published
2006-04-28
Pages
5-8
ISSN
2376-8886 (ISSN-L)
OpenAlex
W2109945587
Cited by
82
References
3
Citations by year
2025: 1 · 2024: 1 · 2023: 2 · 2022: 3 · 2021: 7 · 2020: 6 · 2019: 3 · 2018: 9 · 2017: 4 · 2016: 4 · 2015: 3 · 2014: 4 · 2013: 2 · 2012: 2
FWCI
11.29
Type
conference paper
Language
en
Open access
closed
Locations
Wearable and Implantable Body Sensor Networks (publishedVersion)
Funders
McMaster University
Topics
  • Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
  • Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
Keywords
wearable sensors, stroke, motor ability assessment, stroke rehabilitation, clinical score prediction, home monitoring

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