Citation

Haescher M, Chodan W, Höpfner F, Bieber G, Aehnelt M, Srinivasan K, Murphy MA. J. Rehabil. Assist. Technol. Eng. 2020; 7: e2055668320946209.

Copyright

(Copyright © 2020, SAGE Publishing)

DOI

10.1177/2055668320946209

PMID

33329902 PMCID

Abstract

INTRODUCTION: Falls cause major expenses in the healthcare sector. We investigate the ability of supporting a fall risk assessment by introducing algorithms for automated assessments of standardized fall risk-related tests via wearable devices.

Methods: In a study, 13 participants conducted the standardized 6-Minutes Walk Test, the Timed-Up-and-Go Test, the 30-Second Sit-to-Stand Test, and the 4-Stage Balance Test repeatedly, producing 226 tests in total. Automatedalgorithms computed by wearable devices, as well as a visual analysis of the recorded data streams, were compared to the observational results conducted by physiotherapists.

Results: There was a high congruence between automated assessments and the ground truth for all four test types (ranging from 78.15% to 96.55%), with deviations ranging all well within one standard deviation of the ground truth. Fall risk (assessed by questionnaire) correlated with the individual tests.

Conclusions: The automated fall risk assessment using wearable devices and algorithms matches the validity of the ground truth, thus providing a resourceful alternative to the effortful observational assessment, while minimizing the risk of human error. No single test can predict overall fall risk; instead, a much more complex model with additional input parameters (e.g., fall history, medication etc.) is needed.

Language: en

Keywords

Fall risk assessment; ambient assisted living (AAL); fall prevention; mobile assisted living (MAL); wearable computing