Human Body Mixed Motion Pattern Recognition Method Based on Multi-Source Feature Parameter Fusion

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.

Review of adaptive control for stroke lower limb exoskeleton rehabilitation robot based on motion intention recognition

“…The kinematic provided by IMU and pressure-sensitive sensors cannot accurately identify spatial position information of stroke patients, so it is necessary to introduce motion capture to make compensation. Song et al ( 2020 ), built a motion state recognition model based on feature evaluation and multi-layer BP (BackPropagation) neural network, collected kinematic, plantar pressure, and parameters collected by VICON, and performed multi-source feature parameter fusion. Finally, the two sets of multi-information source fusion models were verified, and the average recognition accuracy rate for 15 motion patterns was 95.05%.…”

Review on Control Strategies for Lower Limb Rehabilitation Exoskeletons

“…The simplest approach is On-Off Control, which relies on pattern recognition to separate human intention into a several classes [106], [107]. The predefined trajectory is replayed when a class is detected.…”

Systematic Review of Intelligent Algorithms in Gait Analysis and Prediction for Lower Limb Robotic Systems

“…A BPNN is implemented by Song et al [86], for locomotion mode detection. They detected 4 static, and 11 dynamic modes, a total of 15 locomotion modes including sitting, standing, level walking, level walking with weight etc.…”