Adaptive Automatic Controller for Swing Assist by Pneumatic Artificial Muscle

Abstract

Pneumatic Artificial Muscle (PAM) is essential to support patients with weak muscles in daily activities and physical rehabilitation process. Hence, the research on PAM as an assistive device is crucial for elderly and patients in the hospital, especially patients suffering from hemiplegic gait. Based on the understanding of the lower-limb muscle activation and human gait analysis, we proposed PAM Automatic Controller to drive PAM adaptively to support patient’s gait in real-time. Automatic Controller primarily leverages the swing phase during gait to set the start of activation and muscle contraction duration to control the PAM using the smartphone wireless PAM driver. An evaluation experiment on Automatic Controller was conducted against the manual settings of PAM. Compared with manual setting, the Automatic Controller provides comparable results to the best performance of static settings. In addition, it adjusts the contraction time in real-time without having the participants to carry out the calibration by running the 13 cases to set the parameters manually.