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Measurement of Disturbance-Induced Fall Behavior and Prediction Using Neural Network

In this study, we construct a neural network that learns a falling motion by measuring a behavior that simulates a fall forward due to a trip, in order to realize a system to predict a fall in advance. Recent advances in machine learning techniques have enabled the development of methods for predicting behavior in real time. This is expected to be used for walking to predict a fall and provide support in advance to reduce injuries. Although many systems have been proposed to measure and detect a fall, there are few studies on data measured when a fall is caused by an unexpected disturbance during normal walking. Therefore, we do not know how long it takes for a person to fall over after a disturbance occurs, and we do not have much understanding of how predictable the phenomenon is in principle. In this study, we constructed a system to simulate a fall with a disturbance and measured the 3D skeletal data. From these results, the average time between the disturbance and the start of the fall was calculated. By using a neural network to make predictions, we confirmed that falls can be predicted at the point of the disturbance.