Reducing Muscular Effort in Exoskeletons: A Bioinspired Design and Hierarchical Motion Recognition Framework

This study aims to address the limitations of traditional exoskeleton designs by developing a biomimetic actuation path and a hierarchical motion recognition framework to improve integration with human biomechanics and reduce muscular effort during walking. Methods: A musculoskeletal model was used to quantify lower limb muscle force patterns, enabling the design of actuation paths aligned with natural muscle contraction trajectories. A hierarchical motion recognition system, combining an auto-encoder and an artificial neural network (ANN), was developed for real-time identification of gait events, activity levels, and walking speeds. Two biomechanical-inspired control strategies were implemented to replicate natural movement patterns and adapt to dynamic forces during walking. Results: Experimental validation through EMG-based walking trials demonstrated a significant reduction in muscle activity. Specifically, the exoskeleton reduced the maximum voluntary isometric contraction (%MVIC) of the soleus muscle by 12.39% and the gastrocnemius by 12.32% compared to unassisted walking. Conclusion: The proposed design effectively integrates human-exoskeleton interaction, reduces muscular effort, and provides precise motion assistance, offering a novel approach to incorporating muscle force analysis in wearable robotics. Significance: This work advances the field of exoskeleton technology by introducing a quantitative biomechanical approach for actuation path optimization and real-time motion recognition, with potential applications in rehabilitation, assistive devices, and human locomotion enhancement.