Fall risk classification with posturographic parameters in community-dwelling older adults: a machine learning and explainable artificial intelligence approach

Fall risk classification with posturographic parameters in community-dwelling older adults: a machine learning and explainable artificial intelligence approach

Abstract Background Computerized posturography obtained in standing conditions has been applied to classify fall risk for older adults or disease groups. Combining machine learning (ML) approaches is superior to traditional regression analysis for its ability to handle complex data regarding its cha...

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Bibliographic Details
Main Authors: Huey-Wen Liang, Rasoul Ameri, Shahab Band, Hsin-Shui Chen, Sung-Yu Ho, Bilal Zaidan, Kai-Chieh Chang, Arthur Chang
Format: Article
Language:English
Published: BMC 2024-01-01
Series:Journal of NeuroEngineering and Rehabilitation
Subjects:
Falls
Machine learning
Older adults
Risk classification
Trunk sway
Online Access:https://doi.org/10.1186/s12984-024-01310-3

Internet

https://doi.org/10.1186/s12984-024-01310-3

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