Abstract
Aiming at the requirement of rapid recognition of the wearer’s gait stage in the process of intelligent hybrid control of an exoskeleton, this paper studies the human body mixed motion pattern recognition technology based on multi-source feature parameters. We obtain information on human lower extremity acceleration and plantar analyze the relationship between these parameters and gait cycle studying the motion state recognition method based on feature evaluation and neural network. Based on the actual requirements of exoskeleton per use, 15 common gait patterns were determined. Using this, the studies were carried out on the time domain, frequency domain, and energy feature extraction of multi-source lower extremity motion information. The distance-based feature screening method was used to extract the optimal features. Finally, based on the multi-layer BP (back propagation) neural network, a nonlinear mapping model between feature quantity and motion state was established. The experimental results showed that the recognition accuracy in single motion mode can reach up to 98.28%, while the recognition accuracy of the two groups of experiments in mixed motion mode was found to be 92.7% and 97.4%, respectively. The feasibility and effectiveness of the model were verified.
Highlights
In order to achieve compliant human computer interaction and coordinated motion, the exoskeleton control strategy develops from a passive position control mode to a mixture of multiple control modes, and the correct recognition of the gait is the basis of the hybrid control of the exoskeleton robot
The exoskeleton can be trained to switch the movement mode autonomously according to the recognized posture or movement pattern of the human body, making its movement more flexible to adapt to more application scenarios, and the obtained human motion information can be used to rectify the results of other perception methods of motion intentions
In order to improve the accuracy of the movement state recognition of the lower extremity exoskeleton robot, this paper proposes a motion state recognition model based on feature evaluation and a multi-layer BP neural network
Summary
In order to achieve compliant human computer interaction and coordinated motion, the exoskeleton control strategy develops from a passive position control mode to a mixture of multiple control modes, and the correct recognition of the gait is the basis of the hybrid control of the exoskeleton robot. As a key technical link in the flexible motion control of exoskeleton robots, gait analysis is a technique based on the acquisition, description, and analysis of human kinematics, dynamics and physiological information to understand the laws of human motion and walking mechanism [1]. The dynamic measurement results of the human lower limb motion posture data are provided in real time, and the exoskeleton is assisted to obtain more gait learning samples to decode, quickly and accurately, the collected lower extremity motion information for obtaining the current motion state of the wearer. The exoskeleton can be trained to switch the movement mode autonomously according to the recognized posture or movement pattern of the human body, making its movement more flexible to adapt to more application scenarios, and the obtained human motion information can be used to rectify the results of other perception methods of motion intentions (such as EEG and EMG). A good recognition model can obtain higher recognition with lower computational load in a shorter time with better precision
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