Prediction of Fall Risk Among Community-Dwelling Older Adults Using a Wearable System

Authors

Thurmon Lockhart, Arizona State UniversityFollow
Rahul Soangra, Chapman UniversityFollow
Hyunsoo Yoon, Yonsei University
Teresa Wu, Arizona State University
Christopher W. Frames, Arizona State UniversityFollow
Raven Weaver, Washington State University
Karen A. Roberto, Virginia Polytechnic Institute and State University

Document Type

Article

Publication Date

10-25-2021

Abstract

Falls are among the most common cause of decreased mobility and independence in older adults and rank as one of the most severe public health problems with frequent fatal consequences. In the present study, gait characteristics from 171 community-dwelling older adults were evaluated to determine their predictive ability for future falls using a wearable system. Participants wore a wearable sensor (inertial measurement unit, IMU) affixed to the sternum and performed a 10-m walking test. Measures of gait variability, complexity, and smoothness were extracted from each participant, and prospective fall incidence was evaluated over the following 6-months. Gait parameters were refined to better represent features for a random forest classifier for the fall-risk classification utilizing three experiments. The results show that the best-trained model for faller classification used both linear and nonlinear gait parameters and achieved an overall 81.6 ± 0.7% accuracy, 86.7 ± 0.5% sensitivity, 80.3 ± 0.2% specificity in the blind test. These findings augment the wearable sensor's potential as an ambulatory fall risk identification tool in community-dwelling settings. Furthermore, they highlight the importance of gait features that rely less on event detection methods, and more on time series analysis techniques. Fall prevention is a critical component in older individuals’ healthcare, and simple models based on gait-related tasks and a wearable IMU sensor can determine the risk of future falls.

Comments

This article was originally published in Scientific Reports, volume 11, in 2021. https://doi.org/10.1038/s41598-021-00458-5

Recommended Citation

Lockhart, T.E., Soangra, R., Yoon, H. et al. Prediction of fall risk among community-dwelling older adults using a wearable system. Sci Rep 11, 20976 (2021). https://doi.org/10.1038/s41598-021-00458-5

Peer Reviewed

1

Copyright

The authors

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.