AI-driven universal lower-limb exoskeleton system for community ambulation.

Abstract

Exoskeletons offer promising solutions for improving human mobility, but a key challenge is ensuring the controller adapts to changing walking conditions. We present an artificial intelligence (AI)-driven universal exoskeleton system that dynamically switches assistance types between walking modes, modulates assistance levels corresponding to the ground slope, and delivers assistance timely based on the current gait phase in real-time. During treadmill validation, AI-based assistance reduced metabolic cost by 6.5% compared to 3.5% for conventional assistance. We expanded testing the controller in real-world walking, where AI-based assistance showed effective modulation and higher user preference compared to conventional assistance. Leveraging the AI-based approach and a comprehensive dataset, the controller achieved superior performance in environment- and user-state estimations. This approach does not require a separate mode classifier and operates on a user-independent basis, enabling immediate deployment across diverse conditions. This study highlights the potential of AI-driven exoskeletons in facilitating human locomotion in real-world ambulation.