Automated fall risk assessment of elderly using wearable devices
| Autor: | Mario Aehnelt, Marian Haescher, Florian Hopfner, Margit Alt Murphy, Wencke Chodan, Karthik Srinivasan, Gerald Bieber |
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| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science Wearable computer wearable computing ambient assisted living (AAL) medicine.disease mobile assisted living (MAL) fall prevention 03 medical and health sciences 0302 clinical medicine Health care medicine Fall risk assessment Original Article 030212 general & internal medicine Medical emergency business 030217 neurology & neurosurgery Wearable technology Fall prevention |
| Zdroj: | Journal of Rehabilitation and Assistive Technologies Engineering |
| ISSN: | 2055-6683 |
| Popis: | 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. Automated algorithms 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. |
| Databáze: | OpenAIRE |
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