Deep-Learning Control of Lower-Limb Exoskeletons via Simplified Therapist Input

Partial-assistance exoskeletons hold significant potential for gait rehabilitation by promoting active participation during (re)learning of "normal" walking patterns. Typically, the control of interaction torques in partial-assistance exoskeletons relies on a hierarchical control structure. These approaches require extensive calibration due to the complexity of the controller and user-specific parameter tuning, especially for activities like stair or ramp navigation. To address the limitations of hierarchical control in exoskeletons, this work proposes a three-step, data-driven approach: (1) using recent sensor data to probabilistically infer locomotion states (landing step length, landing step height, walking velocity, step clearance, gait phase), (2) allowing therapists to modify these features via a user interface, and (3) using the adjusted locomotion features to predict the desired joint posture and model stiffness in a spring-damper system based on prediction uncertainty. We evaluated the proposed approach with two healthy participants performing treadmill walking and stair ascent/descent at varying speeds, with and without external modification of the gait features through a user interface. Results showed a variation in kinematics according to the gait characteristics and a negative interaction power ($-2.1 \pm 1.6 \mathrm{W}$ for the hip and $-0.6 \pm 1.4 \mathrm{W}$ for the knee joints) suggesting exoskeleton assistance across the different conditions.