ReviewAnnals of biomedical engineering2026
AI-Driven Activity of Daily Living Monitoring: A Comprehensive Review of Ambient, Wearable, and Fusion-Based Sensing Technologies.
Review in Annals of biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increasing prevalence of chronic diseases, functional decline, and cognitive impairments, together with the limitations of episodic clinical assessments, has created a critical need for automated and continuous monitoring of Activities of Daily Living (ADLs) to support timely intervention, personalized care, and independent living. This review systematically examines how ambient sensing, wearable devices, and fusion-based systems have been applied to ADL monitoring in home environments. We analyze 109 peer-reviewed studies published between 2013 and 2024, focusing on reported performance trends, study scale, sensing configurations, and methodological characteristics. Given the substantial heterogeneity across experimental settings, participant populations, activity definitions, and evaluation protocols, reported metrics are interpreted descriptively rather than as directly comparable measures. Overall, wearable-based systems frequently report higher performance metrics in controlled settings, ambient systems offer advantages related to privacy and unobtrusiveness, and fusion-based approaches provide richer contextual information but face scalability challenges. Across all modalities, limited dataset diversity, small sample sizes, inconsistent evaluation practices, and insufficient reporting of real-world deployments remain persistent gaps. This review synthesizes recent advances in sensor-based activity recognition for monitoring activities of daily living, with a particular emphasis on AI-driven approaches.
Indexed as
Identifiers
41820775What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.