The use of a finger-worn accelerometer for monitoring of hand use in ambulatory settings
Abstract
Objective assessment of stroke survivors' upper limb movements in ambulatory settings can provide clinicians with important information regarding the real impact of rehabilitation outside the clinic and help to establish individually-tailored therapeutic programs. This paper explores a novel approach to monitor the amount of hand use, which is relevant to the purposeful, goal-directed use of the limbs, based on a body networked sensor system composed of miniaturized finger- and wrist-worn accelerometers. The main contributions of this paper are twofold. First, this paper introduces and validates a new benchmark measurement of the amount of hand use based on data recorded by a motion capture system, the gold standard for human movement analysis. Second, this paper introduces a machine learning-based analytic pipeline that estimates the amount of hand use using data obtained from the wearable sensors and validates its estimation performance against the aforementioned benchmark measurement. Based on data collected from 18 neurologically intact individuals performing 11 motor tasks resembling various activities of daily living, the analytic results presented herein show that our new benchmark measure is reliable and responsive, and that the proposed wearable system can yield an accurate estimation of the amount of hand use (normalized root mean square error of 0.11 and average Pearson correlation of 0.78). This study has the potential to open up new research and clinical opportunities for monitoring hand function in ambulatory settings, ultimately enabling evidence-based, patient-centered rehabilitation and healthcare.
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Liu et al. estimated the amount of affected hand use compared to the unaffected hand.
“while Liu et.al [53] estimated the amount of the affected hand use compared to the unaffected hand.”
From Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review (2022) · cites this paper as [53]
Some included studies did not specify or did not use participants.
“while some studies did not specify or did not use participants [61,53].”
From Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review (2022) · cites this paper as [53]
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- Authors
- Liu, X., Rajan, S., Ramasarma, N., Bonato, P., & Lee, S. I.
- Affiliations
- University of Massachusetts Amherst (US)
- Spaulding Rehabilitation Hospital (US)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2019-03
- Volume
- 23
- Issue
- 2
- Pages
- 599-606
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W2794850934
- Cited by
- 39
- References
- 32
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 3 · 2023: 8 · 2022: 7 · 2021: 5 · 2020: 3 · 2019: 8
- FWCI
- 2.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- wearable sensors, stroke rehabilitation, upper limb movement, ambulatory activity monitoring, machine learning estimation, activities of daily living, wrist-worn accelerometers
- MeSH terms
- Stroke Rehabilitation, Wearable Electronic Devices, Adolescent, Fingers, Hand, Humans, Movement, Signal Processing, Computer-Assisted, Monitoring, Ambulatory, Upper Extremity, Young Adult, Accelerometry
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