Wearables and Machine Learning Use for Activity of Daily Life and Fall Management in the Elderly: A Systematic Review (Preprint)

BACKGROUND The growing elderly population can directly impact countries’ productivity and pose significant challenges for governments, becoming a potential public health concern due to the increasing prevalence of health issues such as frailty, dementia, mobility limitations, and cardiovascular diseases. One promising approach is to integrate emerging technologies, such as wearable devices, machine learning, and smart sensors, to support older adults in their daily activities. These technologies can promote independence by enabling the safe execution of essential tasks while allowing continuous, 24-hour monitoring through mobile health systems. OBJECTIVE This research paper aims to evaluate the current use of consumer-grade wearable technologies, in combination with machine learning techniques, to promote autonomy and enhance daily activities among older adults. METHODS We conducted a systematic review in accordance with the Cochrane Handbook and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to synthesize evidence on the use of wearable technologies combined with artificial intelligence, particularly machine learning methods, to support daily living and prevent falls among older adults. The search was conducted in PubMed, MEDLINE, Scopus, the Institute of Electrical and Electronics Engineers (IEEE) Xplore, and the Association for Computing Machinery (ACM) Digital Library from their inception through April 2025, with no date or language restrictions. RESULTS Twenty-four studies were included. Mos studiest were observational or methodological and relied primarily on inertial sensing from wrist- or waist-mounted devices. The main application domains were activities of daily living monitoring, gait and mobility assessment, cognitive impairment, Parkinson’s disease symptoms, fall risk and detection, and frailty assessment. Classical machine learning models (e.g., support vector machines and random forests) and deep learning architectures (e.g., CNNs and LSTMs) were both widely used. However, studies were highly heterogeneous, frequently involved small samples, and rarely performed external validation or reported clinically actionable outcomes. CONCLUSIONS Consumer-grade wearable devices, when combined with machine learning, show promise in supporting autonomy, daily activity monitoring, and fall-related safety in older adults. Nevertheless, the current evidence base is limited by methodological heterogeneity, small sample sizes, scarce external validation, and limited clinical integration. Future research should prioritize real-world evaluations, standardized reporting (e.g., TRIPOD-AI), interdisciplinary co-design, and patient-centered outcomes to enable translation into routine care. CLINICALTRIAL International Prospective Register of Systematic Reviews (PROSPERO) Registration: CRD420251044449