Wearable accelerometry-based technology capable of assessing functional activities in neurological populations in community settings: a systematic review
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BACKGROUND: Integrating rehabilitation services through wearable systems has the potential to accurately assess the type, intensity, duration, and quality of movement necessary for procuring key outcome measures. OBJECTIVES: This review aims to explore wearable accelerometry-based technology (ABT) capable of assessing mobility-related functional activities intended for rehabilitation purposes in community settings for neurological populations. In this review, we focus on the accuracy of ABT-based methods, types of outcome measures, and the implementation of ABT in non-clinical settings for rehabilitation purposes. DATA SOURCES: Cochrane, PubMed, Web of Knowledge, EMBASE, and IEEE Xplore. The search strategy covered three main areas, namely wearable technology, rehabilitation, and setting. STUDY SELECTION: Potentially relevant studies were categorized as systems either evaluating methods or outcome parameters. METHODS: Methodological qualities of studies were assessed by two customized checklists, depending on their categorization and rated independently by three blinded reviewers. RESULTS: Twelve studies involving ABT met the eligibility criteria, of which three studies were identified as having implemented ABT for rehabilitation purposes in non-clinical settings. From the twelve studies, seven studies achieved high methodological quality scores. These studies were not only capable of assessing the type, quantity, and quality measures of functional activities, but could also distinguish healthy from non-healthy subjects and/or address disease severity levels. CONCLUSION: While many studies support ABT's potential for telerehabilitation, few actually utilized it to assess mobility-related functional activities outside laboratory settings. To generate more appropriate outcome measures, there is a clear need to translate research findings and novel methods into practice.
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Parkinson's disease and stroke are the two neurological conditions where accelerometry-based technology has been applied most.
“Parkinson’s disease (PD) and stroke are the two neurological conditions, where accelerometry-based technology has been applied most [1].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [1]
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- Authors
- Steins, D., Dawes, H., Esser, P., & Collett, J.
- Affiliations
- Oxford Brookes University (GB)
- Corresponding author
- Steins, Dax
- Venue
- Journal of NeuroEngineering and Rehabilitation
- Published
- 2014
- Volume
- 11
- Issue
- 1
- Pages
- 36
- ISSN
- 1743-0003 (ISSN-L)
- PMC
- PMC4007563
- OpenAlex
- W2135932677
- Cited by
- 71
- References
- 65
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 3 · 2023: 2 · 2022: 5 · 2021: 4 · 2020: 6 · 2019: 8 · 2018: 9 · 2017: 8 · 2016: 12 · 2015: 6 · 2014: 1
- FWCI
- 2.53
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of NeuroEngineering and Rehabilitation (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Radar (Oxford Brookes University) (submittedVersion)
- Funders
- Oxford Brookes University
- National Institute for Health and Care Research
- NIHR Oxford Biomedical Research Centre
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Keywords
- wearable accelerometry, accelerometer, neurorehabilitation, community-based rehabilitation, mobility assessment, telerehabilitation, outcome measures, device accuracy, non-clinical settings
- MeSH terms
- Humans, Monitoring, Physiologic, Nervous System Diseases, Telemedicine, Accelerometry
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