A Novel Approach to Monitor Rehabilitation Outcomes in Stroke Survivors Using Wearable Technology
Open accessAbstract
Quantitative assessment of motor abilities in stroke survivors can provide valuable feedback to guide clinical interventions. Numerous clinical scales were developed in the past to assess levels of impairment and functional limitation in individuals after stroke. The Functional Ability Scale is one of these clinical scales. It is a 75-point scale used to evaluate the functional ability of subjects by grading movement quality during performance of 15 motor tasks. Performance of these motor tasks requires subjects to reach for objects (e.g., a pencil on a table) and manipulate them (e.g., lift the pencil). In this paper, we show that accelerometer data recorded during performance of a subset of the motor tasks pertaining to the Functional Ability Scale can be relied upon to derive accurate estimates of the scores provided by a clinician using this scale. Accelerometer-based estimates of clinical scores were obtained by segmenting the recordings into movement components (reaching, manipulation, release/return), extracting data features, selecting features that maximized the separation among classes associated with different clinical scores, feeding these features to Random Forests to estimate scores for individual motor tasks, and using a linear equation to estimate the total Functional Ability Scale score based on the sum of the clinical scores for individual motor tasks derived from the accelerometer data. Results showed that it is possible to achieve estimates of the total Functional Ability Scale score marked by a bias of only 0.04 points of the scale and a standard deviation of only 2.43 points when using as few as three sensors to collect data during performance of only six motor tasks.
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Patel et al showed that using accelerometers placed on the arm, it is possible to derive accurate estimates of upper extremity functional ability.
“Finally, Patel et al. [109] showed that, using accelerometers placed on the arm, it is possible to derive accurate estimates of upper extremity functional ability.”
From A review of wearable sensors and systems with application in rehabilitation (2012) · cites this paper as [109]
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- Authors
- Patel, S., Hughes, R., Hester, T., Stein, J., Akay, M., Dy, J. G., & Bonato, P.
- Affiliations
- Harvard University (US)
- The University of Texas at Austin (US)
- Cornell University (US)
- University of Houston (US)
- Northeastern University (US)
- Venue
- Proceedings of the IEEE
- Published
- 2010-03
- Volume
- 98
- Issue
- 3
- Pages
- 450-461
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- OpenAlex
- W2163901584
- Cited by
- 170
- References
- 36
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 7 · 2023: 8 · 2022: 7 · 2021: 11 · 2020: 23 · 2019: 14 · 2018: 21 · 2017: 15 · 2016: 15 · 2015: 10 · 2014: 17 · 2013: 8 · 2012: 3
- FWCI
- 4.98
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Proceedings of the IEEE (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- National Institutes of Health
- Topics
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- stroke rehabilitation, accelerometer data, wearable sensors, random forest, movement segmentation, feature selection, stroke survivors, motor impairment
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