Research on Elderly Fall Prediction for Walking Posture of Elderly-Assistant Robot

Abstract

A prediction method of elderly fall based on RBF neural network and multi-sensor information fusion was proposed in this paper, which could help the user of elderly-assistant robot walk outside safe and reduce the damage caused by falls. Firstly, overall scheme of the elderly fall prediction system was designed. Tactile sensors, tri-axial acceleration sensor and gyroscope were used to collect the touch information of user's hands, the acceleration information and angle of inclination information of human trunk, respectively. Then, three kinds of feature information were extracted separately and fused up by RBF neural network to get probability of elderly fall. If the probability exceeded the threshold, it was judged that the human body had a tendency to fall. Finally, experimental system construction and verification were carried out and the results showed that the prediction method of the user fall was reliable, and the overall prediction accuracy rate was 98%. Among them, the prediction accuracy of normal samples and user fall samples were 100% and 96%), respectively. This method can predict the user fall more accurately, and it provides some guarantees for preventing elderly fall.