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Cited by 26Stroke Rehabilitation and RecoveryContext-Aware Activity Recognition SystemsArtificial Intelligence in Healthcare

Use of Accelerometry for Long Term Monitoring of Stroke Patients

Open access
Lucas, Alfredo, Hermiz, John, Labuzetta, Jamie, Arabadzhi, Yevgeniy, Karanjia, Navaz, Gilja, Vikash·2019·IEEE Journal of Translational Engineering in Health and Medicine
DOI 10.1109/jtehm.2019.2897306PMC PMC6588341
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Abstract

Stroke patients are monitored hourly by physicians and nurses in an attempt to better understand their physical state. To quantify the patients' level of mobility, hourly movement (i.e. motor) assessment scores are performed, which can be taxing and time-consuming for nurses and physicians. In this paper, we attempt to find a correlation between patient motor scores and continuous accelerometer data recorded in subjects who are unilaterally impaired due to stroke. The accelerometers were placed on both upper and lower extremities of four severely unilaterally impaired patients and their movements were recorded continuously for 7 to 14 days. Features that incorporate movement smoothness, strength, and characteristic movement patterns were extracted from the accelerometers using time-frequency analysis. Support vector classifiers were trained with the extracted features to test the ability of the long term accelerometer recordings in predicting dependent and antigravity sides, and significantly above baseline performance was obtained in most instances ([Formula: see text]). Finally, a leave-one-subject-out approach was carried out to assess the generalizability of the proposed methodology, and above baseline performance was obtained in two out of the three tested subjects. The methodology presented in this paper provides a simple, yet effective approach to perform long term motor assessment in neurocritical care patients.

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Authors
Lucas, A., Hermiz, J., Labuzetta, J., Arabadzhi, Y., Karanjia, N., & Gilja, V.
Affiliations
University of California San Diego (US)
Corresponding author
Gilja, Vikash
ORCID
Venue
IEEE Journal of Translational Engineering in Health and Medicine
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2019
Volume
7
Pages
1-10
ISSN
2168-2372 (ISSN-L)
DOI
10.1109/jtehm.2019.2897306
PMC
PMC6588341
OpenAlex
W2913602685
Cited by
26
References
35
Citations by year
2026: 1 · 2025: 1 · 2024: 4 · 2023: 7 · 2022: 6 · 2021: 6 · 2020: 1
FWCI
2.27
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Journal of Translational Engineering in Health and Medicine (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • UCSD ECE Department Startup
  • NSF I-Corps
  • Dennis Washington Leadership Scholarship
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, accelerometry, long-term patient monitoring, motor function assessment, time-frequency analysis, support vector machine classification, motion features, neurocritical care, wearable sensors

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