Online Reinforcement Learning Control for the Personalization of a Robotic Knee Prosthesis

• Published in: IEEE transactions on cybernetics Vol. 50; no. 6; pp. 2346 - 2356
• Main Authors: Wen, Yue, Si, Jennie, Brandt, Andrea, Gao, Xiang, Huang, He Helen
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Amputees - rehabilitation; Approximate dynamic programming (ADP); Automation; Biomechanical Phenomena - physiology; direct heuristic dynamic programming (dHDP); Dynamic programming; Exoskeleton Device; Feasibility; Gait; Gait - physiology; Humans; Impedance; Kinematics; Knee; Knee Prosthesis; Learning; Male; Noise measurement; Order parameters; Prostheses; Prosthetics; reinforcement learning (RL); Reinforcement, Psychology; robotic knee prosthesis; Robotics; Robots; Signal Processing, Computer-Assisted; Treadmills; Tuning; Walking; Young Adult

Robotic prostheses deliver greater function than passive prostheses, but we face the challenge of tuning a large number of control parameters in order to personalize the device for individual amputee users. This problem is not easily solved by traditional control designs or the latest robotic techno...