Automated fall risk assessment of elderly using wearable devices.
Marian HaescherWencke ChodanFlorian HöpfnerGerald BieberMario AehneltKarthik SrinivasanMargit Alt MurphyPublished in: Journal of rehabilitation and assistive technologies engineering (2020)
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.