A Fall Risk Assessment Model for Community-Dwelling Elderly Individuals Based on Gait Parameters

Assessing fall risk accurately is vital for the older adult population. However, existing fall risk assessments mainly depend on scales, which are inconvenient, subjective, and imprecise. The aim of this study was to explore a machine learning model based on gait parameters to evaluate the risk of falls in older adults living in the community over a one-year period. A total of 46 elderly subjects were recruited in this study. Information on demographics, disease history, and fall history was collected via questionnaire. Moreover, this study used a gait analysis system based on inertial measurement unit and Azure Kinect to acquire the spatiotemporal parameters of the subjects' gait. Based on the above data, various machine learning models, including k-nearest neighbor, support vector machine, gradient boosting decision tree, and voting classifier, were built to estimate the fall risk level of elderly individuals. K-nearest neighbor performed best among all the models with an accuracy of 0.80 on the individual test set, an F1 score of 0.67, and an area under the receiver operating characteristic curve of 0.83. Gait frequency was found to be the most significant feature associated with fall risk, followed by body mass index and gait cycle variability. The findings suggest that the k-nearest neighbor model can provide a quantitative and objective evaluation of fall risk for older adults living in the community and that the evaluation is more accurate when both gait parameters and disease history are taken into account.