Design and EMG Signal Evaluation of Ankle Assistance Exosuit

doi:10.3901/JME.2023.17.067

Abstract

Abstract: With the development of national defense equipment and the aggravation of population aging, the demand of lower limb flexible exoskeletons for battlefield soldiers, army logistics and the elderly population has risen sharply. Based on this urgent demand, a portable ankle assistance Exosuit system with good wearability, high human-machine compatibility and excellent assistance performance has been developed. According to the physiological characteristics of the ankle joint and the parameters of lower limbs, the ergonomic design and development of the Exosuit body and driving unit are achieved where the driving unit is placed at the waist to reduce the added mass of legs and feet as much as possible. Based on the biological torque of the ankle joint, the model of expected auxiliary force is established. The system stiffness of the assistance Exosuit and local soft tissue is obtained by the experimental measurement. Then the established model is transformed into the displacement model of the driving unit. Based on the gait periodicity of lower limbs, a controller with iterative learning control algorithm as the core is designed to track the force model through iteratively learning the output errors of historical time. In order to evaluate the assistance performance of the Exosuit system, the degree of muscle activation and fatigue changes were analyzed by measuring the EMG signals of the plantarflexion muscles. Under the condition of Power-ON, the degree of muscle activation under three walking speeds decreased significantly, especially lateral gastrocnemius muscle. The muscle activation degree of the medial gastrocnemius muscle decreased by an average of 9.62% ± 1.12%. The absolute value of the change slope of mean power frequency (MPF) and median frequency (MF) descended obviously. Hence, the rate and degree of muscle fatigue decreased and the walking endurance of lower limbs increased.

Key words: soft exoskeleton, ankle assistance, iterative learning control, EMG signal, performance evaluation

CLC Number:

TP24

ZHANG Leiyu, YE Tuxian, GAO Xiang, ZHANG Feiran, LI Jianfeng, SU Peng. Design and EMG Signal Evaluation of Ankle Assistance Exosuit[J]. Journal of Mechanical Engineering, 2023, 59(17): 67-78.

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