Using a Minimum Set of Wearable Sensors to Assess Quality of Movement in Stroke Survivors
Abstract
The study herein summarized was focused on the development of a method to derive reliable estimates of the quality of movement of stroke survivors via the analysis of wearable sensor data. Data was collected from 34 subjects while they performed a battery of functional movements that are part of a standard clinical assessment. The quality of movement was assessed using the Functional Ability Scale, a validated clinical scale based on visual observation of movement patterns by a clinical expert. Two wearable sensors were positioned on the stroke-affected wrist and the sternum, respectively. Wearable sensor data was processed to derive data features that were in turn used as input to a regression-based Random Forest algorithm that was trained using a leave-one-subject-out method. The Random Forest algorithm generated estimates of the Functional Ability Scale scores. We found out that the estimates generated via analysis of the wearable sensor data were highly correlated (R2=0.97) with the scores generated by the clinical expert.
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Two included studies used the Wolf Motor Function Test.
“Two included studies used the WMFT [65,67,73].”
From Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review (2022) · cites this paper as [65]
Two included works used the coefficient of determination R2.
“Two works [65,73] used the coefficient of determination, denoted R 2”
From Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review (2022) · cites this paper as [65]
Cites
Go, A. S., Mozaffarian, D., Roger, V. L., Benjamin, E. J., Berry, J. D., Blaha, M. J., Dai, S., Ford, E. S., Fox, C. S., Franco, S., et al. (2014) · Circulation · doi:10.1161/01.cir.0000442015.53336.12 · Cited by 1,408 · see citing works
A Novel Approach to Monitor Rehabilitation Outcomes in Stroke Survivors Using Wearable Technology
Patel, S., Hughes, R., Hester, T., Stein, J., Akay, M., Dy, J. G., & Bonato, P. (2010) · Proceedings of the IEEE · doi:10.1109/jproc.2009.2038727 · Cited by 170 · see citing works
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Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review
Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., & McDonald-Maier, K. D. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103197 · Cited by 180 · see citing works
Panwar, M., Biswas, D., Bajaj, H., Jobges, M., Turk, R., Maharatna, K., & Acharyya, A. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2899927 · Cited by 158 · see citing works
- Authors
- Sapienza, S., Adans-Dester, C., OBrien, A., Vergara-Diaz, G., Lee, S., Patel, S., Black-Schaffer, R., Zafonte, R., Bonato, P., Meagher, C., Hughes, A.-M., Burridge, J., & Demarchi, D.
- Affiliations
- Harvard University (US)
- University of Southampton (GB)
- Politecnico di Torino (IT)
- ORCID
- Venue
- 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 284-285
- ISSN
- 2832-2967 (ISSN-L)
- OpenAlex
- W2749830906
- Cited by
- 9
- References
- 2
- Citations by year
- 2024: 1 · 2023: 1 · 2022: 2 · 2021: 2 · 2020: 2 · 2019: 1
- FWCI
- 2.07
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (publishedVersion)
- PORTO@Iris (submittedVersion)
- 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 survivors, wearable sensors, movement quality, movement assessment, random forest regression, clinical evaluation, functional movement
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