Automated fall risk assessment of elderly using wearable devices

Marian Haescher; Wencke Chodan; Florian Höpfner; Gerald Bieber; Mario Aehnelt; Karthik Srinivasan; Margit Alt Murphy

doi:10.1177/2055668320946209

Automated fall risk assessment of elderly using wearable devices

J Rehabil Assist Technol Eng. 2020 Dec 4:7:2055668320946209. doi: 10.1177/2055668320946209. eCollection 2020 Jan-Dec.

Authors

Marian Haescher^{1

2}, Wencke Chodan¹, Florian Höpfner¹, Gerald Bieber¹, Mario Aehnelt¹, Karthik Srinivasan³, Margit Alt Murphy⁴

Affiliations

¹ Fraunhofer Institute for Computer Graphics Research IGD, Competence Center Visual Assistance Technologies, Rostock, DE, Germany.
² Institute for Visual and Analytic Computing, Department of Multimedia Communication, University of Rostock, DE, Germany.
³ Next Step Dynamics AB, SE, Germany.
⁴ Institute of Neuroscience and Physiology, Department of Clinical Neuroscience, Rehabilitation Medicine, Sahlgrenska Academy, University of Gothenburg, SE, Germany.

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.

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