Recognition of Basic Activities of Daily Living Using Wearable Devices for Older Adults: Scoping Review (Preprint)
Open accessPreprint — published as 10.2196/67373 · Paper page
Abstract
BACKGROUND: Tracking the performance of activities of daily living (ADLs) using ADL recognition has the potential to facilitate aging-in-place strategies, allowing older adults to live in their homes longer and enabling their families and caregivers to monitor changes in health status. However, the ADL recognition literature historically has evaluated systems in controlled settings with data from younger populations, creating the question of whether these systems will work in real-world conditions for older populations. OBJECTIVE: This scoping review seeks to establish the state-of-the-art for recognizing basic ADLs using wearable sensors. This primary goal will identify literature gaps and research needed to make ADL tracking viable for aging-in-place solutions. In addition, this paper will quantify how many publications include older adults. This secondary goal assesses how often studies evaluate their system with older adult participants, enhancing the trustworthiness of the approach. METHODS: We conducted a scoping review using the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guidelines. We identify studies focused on basic ADL recognition using wearable sensors within the PubMed, Association of Computing Machinery Digital Library (ACM DL), and Google Scholar databases using papers published in the last 5 calendar years (2019-2024) to identify current trends given the rapid changes in wearable technology devices. Publications must include at least one of the basic ADLs (ie, bathing, dressing, toileting, transferring, continence, and feeding) and include some sort of wearable sensor or device. Studies focusing on instrumental ADLs, general physical activity tracking, fall detection, or only using environmental devices are excluded. Studies that include older adults in the design or evaluation of their ADL recognition system are highlighted. RESULTS: The database search identified 695 papers; 164 papers passed title screening. A total of 58 studies satisfied the inclusion criteria; only 8 studies included older adults despite most studies identifying this population as a focus for their research. Most studies focused on eating (n=27), hygiene (n=24), drinking (n=20), or transitions (n=13). Few works included toileting (n=3), dressing (n=2), or bathing (n=1) activities. Of the 8 studies that included older adults, 5 focused on recognition performance while 3 focused on user experience and system acceptability. CONCLUSIONS: Basic ADLs are unevenly covered in the literature; more research is needed for recognizing bathing, dressing, and toileting activities. Despite all studies stating the importance of tracking ADLs in older adults, only 14% (8/58) of the included works involve older adult participants. A commonality between these outcomes is difficulty collecting or obtaining adequate training data for ADL recognition systems. Many works are predominantly concerned with proving system feasibility and do not assess usability or real-world deployment. For these systems to move from academic experiments to actual systems with clinical utility, ADL recognition systems must consider the design requirements of being part of remote health monitoring systems. CLINICALTRIAL:
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Cherian, J., Ray, S., & Hammond, T. (2021) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3397481.3450673 · Cited by 8 · see citing works
Self-citationIdentification of Free and WHO-Compliant Handwashing Moments Using Low Cost Wrist-Worn Wearables
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Rokni, S. A., & Ghasemzadeh, H. G. (2019) · ACM Transactions on Design Automation of Electronic Systems · doi:10.1145/3318044 · Cited by 7 · see citing works
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Gómez–Carmona, O., Casado–Mansilla, D., López–de–Ipiña, D., & García-Zubía, J. (2019) · International Conference on the Internet of Things (IoT) · doi:10.1145/3365871.3365889 · Cited by 5 · see citing works
Exploring the Impact of the NULL Class on In-the-Wild Human Activity Recognition
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Self-citationAutomated Detection of In-Home Activities with Ultra-Wideband Sensors
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Invited Paper: Hierarchical Activity Recognition with Smartwatch IMU
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Akbari, A., Grimsley, R., & Jafari, R. (2021) · ACM Transactions on Computing for Healthcare · doi:10.1145/3428664 · Cited by 4 · see citing works
Kianpisheh, M., Mariakakis, A. T., & Truong, K. N. (2024) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3643500 · Cited by 4 · see citing works
Experience: Barriers and Opportunities of Wearables for Eating Research
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Staab, S., Bröning, L., Luderschmidt, J., & Martin, L. (2022) · International Conference on Information Technology for Social Good (GoodIT) · doi:10.1145/3524458.3547220 · Cited by 3 · see citing works
(2012) · SpringerReference · doi:10.1007/springerreference_81395 · Cited by 2 · see citing works
Xia, C. (2021) · Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3411763.3451517 · Cited by 1 · see citing works
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Chandrasekaran, K., Buquicchio, L., Agu, E., & Rundensteiner, E. (2025) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi67747.2025.11269539
Arpitha, S., Manjaiah, D. H., & Yogesh, K. M. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-10664-3_34
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Arioui, A., Lohr, C., Andries, M., & Moreno, P. (2026) · IEEE International Workshop on Metrology for Living Environment (MetroLivEnv) · doi:10.1109/metrolivenv70468.2026.11659909
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Dong, C., Zhang, Q., & Wang, D. (2026) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3832005
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- Authors
- Ray, S. J., Cherian, J., Liberty, A. M., Hammond, T. A., & Shireman, P. K.
- Affiliations
- Texas A&M University (US)
- Wake Forest University (US)
- Corresponding author
- Ray, Samantha J
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2024-10-09
- Volume
- 27
- Pages
- e67373-e67373
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC12123235
- OpenAlex
- W4410391346
- Cited by
- 18
- References
- 107
- Citations by year
- 2026: 16 · 2025: 2
- FWCI
- 1.88
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (1952236)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Demography › Technology Use by Older Adults
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Aging, Health, and Disability
- Keywords
- activities of daily living, basic activities of daily living, wearable sensors, older adults, aging in place, activity recognition, bathing, dressing, toileting, eating, alcohol consumption, transition, caregiver monitoring, user experience, system acceptability, real-world evaluation, research gaps, assistive technology for older adults, home care, privacy and data security
- MeSH terms
- Wearable Electronic Devices, Activities of Daily Living, Aged, Humans, Independent Living
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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