Research on motion pattern recognition of exoskeleton robot based on multimodal machine learning model

Exoskeleton as a real-time interaction with the wearer’s intelligent robot, in recent years, becomes a hot topic mouth class research in the field of robotics. Wearable exoskeleton outside the body, combined with the organic body, plays a role in the protection and support. By wearing an exoskeleton robot, it is possible to expand the wearer’s athletic ability, increase muscle endurance, and enable the wearer to complete tasks that he or she cannot perform under natural conditions. Based on the above advantages, the exoskeleton robot in military medical care and rehabilitation has broad application prospects. This paper describes the multimodal model of machine learning research status and research significance of the text on the exoskeleton robot applications, and on the basis of a detailed study of gait. It mainly involves: analysis and planning and obtaining motion information processing, pattern recognition and analysis of gait and the gait conversion process, and the EEG and joint position, foot pressure, such as different modes of data as input to machine learning models to improve the timeliness, accuracy and safety of gait planning. Since the common movement process involves the transformation process of gait, this paper studies the gait transformation process including squatting, walking on the ground and standing.