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The goal of this clinical trial is to understand the feasibility and effectiveness of using reinforcement learning to personalize robotic prosthetic legs (an experimental prototype) for unilateral transfemoral amputees. The main questions it aims to answer are: * With the developed RL-based Recommendation Interfacing System (RISE), clinicians are able to personalize prosthetic legs faster compared with existing manual personalization procedures. * With the developed RL-based Recommendation Interfacing System (RISE), clinicians are able to personalize prosthetic legs without detailed knowledge about how the prosthetic legs are controlled. * Patients perform better when the prosthetic legs are personalized with RISE system compared with the ones personalized manually Researchers will compare two arms (RISE guided personalization and manual personalization) to see if the tuning speed will
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