Wearable AI for on-device frailty assessment.

ABSTRACT: Continuously operating wearables offer detailed insight into chronic health conditions and have the potential to reshape diagnostic and screening tools. However, the energy demands and large datasets created by constant monitoring over weeks to months are difficult or impossible to integrate into existing clinical practice, limiting the utility of this device class. Machine learning offers the opportunity to condense these large datasets into streamlined, digestible trends with the potential for significant clinical impact, although off-device inference requires advanced network infrastructure and substantial power availability for radios. Here, we introduce a device framework that integrates artificial intelligence with clinical grade biosignal acquisition at the edge, performing on-devic

SUBMITTER: Kasper KA 

PROVIDER: S-EPMC12847780 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature