Selection of Muscle-Activity-Based Cost Function in Human-in-the-Loop Optimization of Multi-Gait Ankle Exoskeleton Assistance

Using “human-in-the-loop” (HIL) optimization can obtain suitable exoskeleton assistance patterns to improve walking economy. However, there are differences in these under different gait conditions, and currently most HIL optimizations use metabolic cost, which requires long periods be estimated for each control law, as the physiological objective minimize. We aimed construct a muscle-activity-based cost function find appropriate initial of multi-gait ankle assistance. One healthy subject walked assisted by an nine conditions condition was combination speeds, ground slopes load weights. Ten were provided condition. Then we constructed based on surface electromyography signals four lower leg muscles select muscle weight using particle swarm algorithm compose with maximum between patterns. The mean weights medial gastrocnemius, lateral soleus tibialis anterior activity all 0.153, 0.104, 0.953 0.145, respectively. verified effectiveness this validation experiments conducted subjects. Our results expected guide selection functions time efficiency optimization.