Haescher, MarianMarianHaescherChodan, WenckeWenckeChodanHöpfner, FlorianFlorianHöpfnerBieber, GeraldGeraldBieberAehnelt, MarioMarioAehneltSrinivasan, KarthikKarthikSrinivasanAlt Murphy, MargitMargitAlt Murphy2022-03-062022-03-062020https://publica.fraunhofer.de/handle/publica/26571410.1177/2055668320946209Introduction: 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.enwearable computingambient assisted living (AAL)fall detectionLead Topic: Individual HealthResearch Line: Human computer interaction (HCI)006journal article