Pilot study on human-in-the-loop optimization of ankle exoskeleton assistance based on plantar pressure interaction

Lower limb exoskeletons can enhance human locomotion performance and provide aid in rehabilitation. Due to human interpersonal differences, identifying a proper assistance strategy is challenging. The uptake of embodied intelligence that learns individuals' needs and tasks' requirements will help exoskeleton systems achieve their potential under different scenarios. Utilizing the evolution strategy to explore human reaction under exoskeleton assistance, "human-in-the- loop" (HIL) optimization is promising to obtain suitable assistance patterns. However, most current HIL optimizations use physiological signals, such as metabolic consumption and muscle activity, as the objective function to minimize, which need a long time to be evaluated and are inconvenient for real-life use. In the study, we aimed to construct a HIL optimization strategy to search effective exoskeleton assistance patterns based on the human-robot interactive force measured by wearable sensors. We first used a unilateral cable-driven ankle exoskeleton to explore the characteristics of human-robot interaction under 20 assistance patterns. A plantar-pressure-based cost function was constructed and real-timely evaluated for HIL optimization. A pilot experiment was conducted with a single participant. Optimized exoskeleton assistance can improve the individual walking economy by a 41.2% reduction in soleus muscle activity and a 21.3% decrease in metabolic cost. The proposed method is promising to improve the HIL optimization time efficiency and promote more effective real-life exoskeleton applications.