2020/03/17 by Rachel Gehlhar, Yuxiao Chen, Gehlhar, Rachel +3
Engineering · #Ergonomics and Human Factors #FOS: Computer and information sciences #Muscle activation and electromyography studies #Prosthetics and Rehabilitation Robotics #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2003.07524
openalex publication_date 2020/03/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper proposes a data-driven method for powered prosthesis control that\nachieves stable walking without the need for additional sensors on the human.\nThe key idea is to extract the nominal gait and the human interaction\ninformation from motion capture data, and reconstruct the walking behavior with\na dynamic model of the human-prosthesis system. The walking behavior of a human\nwearing a powered prosthesis is obtained through motion capture, which yields\nthe limb and joint trajectories. Then a nominal trajectory is obtained by\nsolving a gait optimization problem designed to reconstruct the walking\nbehavior observed by motion capture. Moreover, the interaction force profiles\nbetween the human and the prosthesis are recovered by simulating the model\nfollowing the recorded gaits, which are then used to construct a force tube\nthat covers all the interaction force profiles. Finally, a robust Control\nLyapunov Function (CLF) Quadratic Programming (QP) controller is designed to\nguarantee the convergence to the nominal trajectory under all possible\ninteraction forces within the tube. Simulation results show this controller's\nimproved tracking performance with a perturbed force profile compared to other\ncontrol methods with less model information.\n