Anomaly state assessing of human using walker-type support system based on statistical analysis

In this paper, we propose a method to assess an extent of anomaly state of human using a walker-type support system. The elderly and the handicapped people use the walker-type support system to keep their balance and support their weight. Although the walker-type support system is easy to move based on the applied force of the user, several accidents such as falling and colliding with the obstacle have been reported. The anomaly state that causes a severe injury of the user should be detected before accident and the walker-type support system should prevent such accidents. In this paper, we focus on assessing the extent of the anomaly state of the user based on the statistical analysis of the applied force of the user. This research models the applied force of the user in real time by using the Gaussian Mixture Model (GMM), and we determine each parameter of GMM statistically. In addition, we assess the extent of the anomaly state of the user by using the Hellinger score, which can compare the data set of the normal state with that of anomaly state. The proposed method is applied to developed walker-type support system with simple force sensor, and we conduct the experiments in the several walking states and the environmental conditions.