This paper is in the following e-collection/theme issue:

AI in Older Adult Care (42) Frailty Detection, Assessment and Prediction (68) Machine Learning (2167) Computer-Aided Diagnosis (268) Artificial Intelligence (2249) Fitness Trackers and Smart Pedometers/Accelerometers (683) Wearable Devices and Sensors (1175)

Published on in Vol 8 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/77140, first published .
Machine Learning Approach for Frailty Detection in Long-Term Care Using Accelerometer-Measured Gait and Daily Physical Activity: Model Development and Validation Study

Machine Learning Approach for Frailty Detection in Long-Term Care Using Accelerometer-Measured Gait and Daily Physical Activity: Model Development and Validation Study

Machine Learning Approach for Frailty Detection in Long-Term Care Using Accelerometer-Measured Gait and Daily Physical Activity: Model Development and Validation Study

Authors of this article:

Xiaoping Zheng1 Author Orcid Image ;   Ziwei Zeng1 Author Orcid Image ;   Kimberley S van Schooten2, 3 Author Orcid Image ;   Yijian Yang1, 4 Author Orcid Image
Article Authors Cited by (1) Tweetations Metrics

Journals

  1. Kumar M, Lee S, Chien Y, Hsiao-Kuang Wu E, Chen C, Yeh S. A Clinically Validated Multi-Model Fusion Framework Integrating Machine Learning and LSTM Networks for Real-Time Geriatric Frailty Assessment. IEEE Access 2026;14:1552 View