Research on Human Gait Phase Recognition Algorithm Based on Multi-Source Information Fusion

摘要

Gait phase detection is of great significance in the field of motion analysis and exoskeleton-assisted walking, and can realize the accurate control of exoskeleton robots. Therefore, in order to obtain accurate gait information and ensure good gait phase detection accuracy, a gait recognition framework based on the New Hidden Markov Model (NHMM) is proposed to improve the accuracy of gait phase detection. A multi-sensor gait data acquisition system was developed and used to collect the training data of eight healthy subjects to measure the acceleration and plantar pressure of the human body. Accuracy of the recognition framework, filtering algorithm and window selection, and the missing validation of the generalization performance of the method were evaluated. The experimental results show that the overall accuracy of NHMM is 94.7%, which is better than all other algorithms. The generalization of the performance is 84.3%. The results of this study provide a theoretical basis for the design and control of the exoskeleton.