Adaptive Assist-as-Needed Control With Hybrid Torque Fusion for Pneumatic Artificial Muscle-Powered Ankle Exoskeleton

Abstract

Assist-as-needed (AAN) assistance can encourage active participation during human walking. However, individuals exhibit diverse walking patterns, which makes it challenging for exoskeleton to provide personalized assistance. This paper presents an innovative adaptive AAN control strategy with hybrid torque fusion, including two modules of hybrid torque fusion estimation and adaptive control to promote voluntary participation. Specifically, the hybrid torque fusion module fuses torque estimated from surface electromyography (sEMG) signals with dynamic model estimation using regularized particle filter fusion (RPFF) algorithm to evaluate the human’s active participation. The control module incorporates a globally continuous extended assistance level function (EALF) that integrates joint motion tracking error, human-robot interaction force, and voluntary deficit to quantify subject performance and enable smooth transitions between four training modes, thereby continuously adjusts the torque output. Comprehensive comparisons with existing AAN strategies under multiple scenarios demonstrate that the proposed method improves trajectory tracking accuracy by approximately 16.1% and reduces human-robot interaction force by 4.01% during slope walking and simulated gait impairment. NASA-TLX and Likert scale assessments further validate the method’s effectiveness in enhancing active participation and wearing comfort. Note to Practitioners—To address insufficient active participation and unsmooth assistance in walking rehabilitation using ankle exoskeleton, caused by incomplete quantification of human motor ability, this study introduces a human active torque fusion mechanism. We developed a personalized human-robot collaboration model and proposed an AAN control algorithm based on an assistance function. This algorithm can potentially be transferred or extended to other rehabilitation robotic platforms. Although the system demonstrates promising application potential, this study acknowledges certain limitations, such as a small sample size and insufficient exploration of diverse patient population needs. Future research can build on this work by integrating additional sensing technologies, optimizing control strategies to enhance system adaptability, and exploring more efficient adaptive or online learning methods to simplify the parameter calibration process before training. This study offers the potential to significantly improve daily living for individuals with mobility limitations, laying the groundwork for more personalized and effective rehabilitation solutions.