Cited by 119Stroke Rehabilitation and RecoveryContext-Aware Activity Recognition SystemsProsthetics and Rehabilitation Robotics

Enabling stroke rehabilitation in home and community settings: a wearable sensor-based approach for upper-limb motor training

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Lee, Sunghoon I., Adans-Dester, Catherine P., Grimaldi, Matteo, Dowling, Ariel V., Horak, Peter C., Black-Schaffer, Randie M., Bonato, Paolo, Gwin, Joseph T.·2018·IEEE Journal of Translational Engineering in Health and Medicine

Abstract

High-dosage motor practice can significantly contribute to achieving functional recovery after a stroke. Performing rehabilitation exercises at home and using, or attempting to use, the stroke-affected upper limb during Activities of Daily Living (ADL) are effective ways to achieve high-dosage motor practice in stroke survivors. This paper presents a novel technological approach that enables 1) detecting goal-directed upper limb movements during the performance of ADL, so that timely feedback can be provided to encourage the use of the affected limb, and 2) assessing the quality of motor performance during in-home rehabilitation exercises so that appropriate feedback can be generated to promote high-quality exercise. The results herein presented show that it is possible to detect 1) goal-directed movements during the performance of ADL with a [Formula: see text]-statistic of 87.0% and 2) poorly performed movements in selected rehabilitation exercises with an [Formula: see text]-score of 84.3%, thus enabling the generation of appropriate feedback. In a survey to gather preliminary data concerning the clinical adequacy of the proposed approach, 91.7% of occupational therapists demonstrated willingness to use it in their practice, and 88.2% of stroke survivors indicated that they would use it if recommended by their therapist.

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Crossref · 10.1109/jtehm.2018.2829208

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  • Data bias causing assessment algorithms not to generalise to new patients with different degrees of severity was very common among the reviewed papers, with researchers complaining about poor generalisation.

    “In the same context, data bias which is another issue can cause the assessment algorithm to evolve in an unpredictable manner and not generalise to new patients that have different degrees of severity.This was very common among the reviewed papers, where researchers complained about their algorithms not generalising well.[41,49,52,58,60].”

    From Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review (2022) · cites this paper as [52]

    The Discussion and Conclusions (page not provided) link a small dataset to potential bias and say the results may not generalize even to the target population. The paper does not show that the algorithm fails for new patients with different impairment severity, behaves unpredictably, or that poor generalization was common among the reviewed papers. (LLM, conf=0.86)

    Experimental — accuracy not yet measured
  • Random Forest were less used than Support Vector Machines.

    “RF were less used than the former [46,47,52,58]”

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References 59 · Cited by 119

Research works printed but not linked: 1

  1. [28]Saini P., “ Philips stroke rehabilitation exerciser: A usability test,” presented at the IASTED Int. Conf. Telehealth/Assistive Technol, Baltimore, MD, USA, 2008.

Authors
Lee, S. I., Adans-Dester, C. P., Grimaldi, M., Dowling, A. V., Horak, P. C., Black-Schaffer, R. M., Bonato, P., & Gwin, J. T.
Affiliations
  • University of Massachusetts Amherst (US)
  • Harvard University (US)
  • BioSensics (United States) (US)
  • Rensselaer Polytechnic Institute (US)
Corresponding author
Lee, Sunghoon I.
Venue
IEEE Journal of Translational Engineering in Health and Medicine
Published
2018
Volume
6
Pages
1-11
ISSN
2168-2372 (ISSN-L)
OpenAlex
W2802211033
Cited by
119
References
59
Citations by year
2026: 9 · 2025: 14 · 2024: 21 · 2023: 18 · 2022: 16 · 2021: 17 · 2020: 15 · 2019: 8 · 2018: 1
FWCI
8.76
Type
journal article
Language
en
Open access
Locations
  • IEEE Journal of Translational Engineering in Health and Medicine (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
  • PORTO@Iris (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Center for Med Rehab Res, National Institute for Child Health and Human Development (R44HD084035)
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
stroke rehabilitation, wearable sensors, home-based rehabilitation, activities of daily living, motor performance assessment, real-time feedback, stroke survivors, occupational therapists, user acceptance

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