Fall Risk Analysis for Elderly with Wearable Accelerometers Measurements

Emerging with aging populations in developed countries, continuously rising incidences of fall has consistently raised the concern in elderly and long-term care. In this research paper, the relationship between Berg Balance Scale (BBS) and elderly movement data captured by wearable accelerometers are determined. In situations where it is not known if the elderly is prone to falls, a clustering model is established to segment different groups of distinguished gait pattern and evaluate the feasibility to stratify the risk of falling variates with the harmonic ratio of measurements in three perpendicular directions during gait pattern assessment. It purely relies on the measurements of body movement without any judgement subjected to individual medical professional which opinions even can be variated among each other. Robustness of this model are further verified by appropriate tests of statistical significance. The present study, as an authentic complement, evaluates the effectiveness of using wearable accelerometers to assess the fall risk for elderly. It is also an alternative reference for IoT or wearable device manufacturers to further research and develop a system to estimate fall risk individually, predict any fall activity and thus prevent from its occurrence. © 2023 Newswood Limited. All rights reserved.