Human-Centered Geodesics for Motion Planning

This paper addresses the challenge of designing human-like reference trajectories for exoskeleton-aided rehabilitation, with a focus on mimicking human joint coordination while addressing clinical requirements. Redundant kinematic chains in human biomechanics pose challenges to trajectory planning: state-of-the-art algorithms often do not explicitly address the problem of replicating natural movements nor do they provide a suitable performance over a wide range of human motions. To address this challenge, this paper proposes a geodesics-based computational method that incorporates joint-level constraints, in addition to energy and level of comfort criteria to solve the problem of redundancy and better emulate human movements. Using upper-limb data retrieved with an exoskeleton platform, the advanced method demonstrated significant performance gains over standard approaches like the minimum-jerk model and cubic polynomial planning, and leads to human-like trajectories, while closely aligning with human demonstrations, both at the configuration (joints) and task-space (hand) level. In particular, we provide detailed comparisons across various motion types and subjects, demonstrating the versatility of the proposed method and its strong potential for application in clinical and assisted living settings.