Human-Prosthesis Cooperation: Combining Adaptive Prosthesis Control with Visual Feedback Guided Gait

Background: : Personalizing prosthesis control is often structured as human-in-the-loop optimization. However, gait performance is influenced by both human control and intelligent prosthesis control. Hence, we need to consider both human and prosthesis control to achieve desired gait patterns. We developed a novel paradigm that engages human gait control via visual feedback (FB) of stance time to cooperate with automatic prosthesis tuning. Three questions were studied: 1.) does user control of gait timing (via visual FB) help the prosthesis tuning algorithm to converge faster? 2.) in turn, does the prosthesis control influence the user’s ability to reach and maintain the feedback goal? and 3.) does the prosthesis control parameters tuned with extended stance time on prosthesis side allow the user to maintain this potentially beneficial behavior even after feedback is removed (short- and long-term retention)? Methods: : A reinforcement learning algorithm was used to achieve prosthesis control to meet normative knee kinematics in walking. A visual FB system was used to cue human control of stance time to facilitate the prosthesis tuning goal. Seven individuals without amputation (ND) and four individuals with transfemoral amputation (TFA) walked with a powered knee prosthesis on a treadmill. Participants completed prosthesis autotuning with three visual feedback conditions: no FB, self-selected stance time FB (SS FB), and increased stance time FB (Inc FB). The retention of FB effects was studied by comparing the gait performance across three different prosthesis controls, tuned with different visual FB. Results: 1) Human control of gait timing reduced the tuning duration in individuals without amputation, but not for individuals with TFA. 2) The change of prosthesis control did not influence users’ ability to reach and maintain the visual FB goal. 3) All participants increased their prosthesis-side stance time with the feedback and maintain it right after feedback was removed. However, in the post-test, the tuned prosthesis control parameters with visual FB only supported a few participants with longer stance time and better stance time symmetry. Conclusions: : The study gained insights on human-prosthesis cooperation, which may guide the future successful adoption of this paradigm in prosthesis control personalization or human-in-the-loop optimization for improved gait performance.