Citation

Kim T, Yu X, Xiong S. Ergonomics 2023; ePub(ePub): ePub.

Copyright

(Copyright © 2023, Informa - Taylor and Francis Group)

DOI

10.1080/00140139.2023.2202845

PMID

37079340

Abstract

Falls among older people are a major health concern. This study aims to develop a multifactorial fall risk assessment system for older people using a low-cost, markerless Microsoft Kinect. A Kinect-based test battery was designed to comprehensively assess major fall risk factors. A follow-up experiment was conducted with 102 older participants to assess their fall risks. Participants were divided into high and low fall risk groups based on their prospective falls over a 6-month period.

RESULTS showed that the high fall risk group performed significantly worse on the Kinect-based test battery. The developed random forest classification model achieved an average classification accuracy of 84.7%. In addition, the individual's performance was computed as the percentile value of a normative database to visualize deficiencies and targets for intervention. These findings indicate that the developed system can not only screen out 'at risk' older individuals with good accuracy, but also identify potential fall risk factors for effective fall intervention.

Language: en

Keywords

Women; Aging; Fall Risk; Kinect Sensor; Machine Learning; Multifactorial Assessment