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1. Smart Prosthesis System: Continuous Automatic Prosthesis Fitting Adjustment and Real-time Stress Visualization

   https://doi.org/10.1109/BIOCAS.2018.8584784  

   Cai, Yi ; Chen, Jia ; Chen, Diliang ; Qu, Guanzhou ; Zhao, Hongping ; Ansari, Rahila ; Huang, Ming-Chun ( October 2018 , IEEE Biomedical Circuits and Systems Conference)

   Prosthetic devices have significantly improved mobility and quality of life for amputees. Significant engineering advancements have been made in artificial limb biomechanics, joint control systems, and light-weight materials. Amputees report that the primary problem they face with their artificial limbs is a poor fitting socket. In this paper, we propose a smart prosthesis system, in which we measure the real-time force distributions within a prothetic socket and dynamically visualize the results in a mobile application. A major part of the overall proposed system, a wireless pressure sensing system and a force visualization method, are evaluated at current stage. Finally, corresponding works for fully completing this smart prosthesis system are discussed as well.
2. Shortcomings of human-in-the-loop optimization of an ankle-foot prosthesis emulator: a case series

   https://doi.org/10.1098/rsos.202020  

   Welker, Cara Gonzalez ; Voloshina, Alexandra S. ; Chiu, Vincent L. ; Collins, Steven H. ( May 2021 , Royal Society Open Science)

   Human-in-the-loop optimization allows for individualized device control based on measured human performance. This technique has been used to produce large reductions in energy expenditure during walking with exoskeletons but has not yet been applied to prosthetic devices. In this series of case studies, we applied human-in-the-loop optimization to the control of an active ankle-foot prosthesis used by participants with unilateral transtibial amputation. We optimized the parameters of five control architectures that captured aspects of successful exoskeletons and commercial prostheses, but none resulted in significantly lower metabolic rate than generic control. In one control architecture, we increased the exposure time per condition by a factor of five, but the optimized controller still resulted in higher metabolic rate. Finally, we optimized for self-reported comfort instead of metabolic rate, but the resulting controller was not preferred. There are several reasons why human-in-the-loop optimization may have failed for people with amputation. Control architecture is an unlikely cause given the variety of controllers tested. The lack of effect likely relates to changes in motor adaptation, learning, or objectives in people with amputation. Future work should investigate these potential causes to determine whether human-in-the-loop optimization for prostheses could be successful.
3. Motor Control and Sensory Feedback Enhance Prosthesis Embodiment and Reduce Phantom Pain After Long-Term Hand Amputation

   https://doi.org/10.3389/fnhum.2018.00352  

   Page, David M. ; George, Jacob A. ; Kluger, David T. ; Duncan, Christopher ; Wendelken, Suzanne ; Davis, Tyler ; Hutchinson, Douglas T. ; Clark, Gregory A. ( September 2018 , Frontiers in Human Neuroscience)
4. Stand-Up, Squat, Lunge, and Walk With a Robotic Knee and Ankle Prosthesis Under Shared Neural Control

   https://doi.org/10.1109/OJEMB.2021.3104261  

   Hunt, Grace ; Hood, Sarah ; Lenzi, Tommaso ( January 2021 , IEEE Open Journal of Engineering in Medicine and Biology)
5. Knowledge-Guided Reinforcement Learning Control for Robotic Lower Limb Prosthesis

   https://doi.org/10.1109/ICRA40945.2020.9196749  

   Gao, Xiang ; Si, Jennie ; Wen, Yue ; Li, Minhan ; Huang, He Helen ( May 2020 , 2020 IEEE International Conference on Robotics and Automation (ICRA))