abstract
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Various backdrivable exoskeletons have demonstrated the mechanical capabilities necessary to assist volitional motions of able-bodied users and people with mild to moderate gait disorders, but there does not exist a control framework that can be deployed on any joint to assist any activity of daily life in a provably stable manner. This paper presents the modular, multi-task optimal energy shaping (M-TOES) framework based on a convex, data-driven optimization that concurrently considers multiple activities to design a versatile and adaptable exoskeleton controller for any lower-limb joint configuration. Besides modularity and convexity, M-TOES improves upon our prior energy-shaping method by enabling torque goal adjustments, distributed processing across two devices, and integration of stance-to-swing transitions into the stability analysis. We evaluated controllers for four joint configurations (unilateral/bilateral, hip/knee) on eight able-bodied users navigating a multi-activity course, including sit-stand, level walking, ramps, and stair ascent/descent. The two unilateral conditions significantly lowered overall muscle activation across all tasks and subjects (p<0.001). In contrast, bilateral configurations had a minimal impact, possibly attributable to device weight and physical constraints.