Understanding Human-Prosthesis Interaction via Reinforcement Learning-Based Echo Control: A Case Study
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-70316-5_112
Reference8 articles.
1. V. Creylman, I. Knippels, P. Janssen, E. Biesbrouck, K. Lechler, L. Peeraer, Assessment of transfemoral amputees using a passive microprocessor-controlled knee versus an active powered microprocessor-controlled knee for level walking. Biomed. Eng. Online 15(3), 142 (2016)
2. B.J. Hafner, R.L. Askew, Physical performance and self-report outcomes associated with use of passive, adaptive, and active prosthetic knees in persons with unilateral, transfemoral amputation: Randomized crossover trial. J. Rehabil. Res. & Dev. 52(6) (2015)
3. D. Grimes, W. Flowers, Multi-mode above-knee prosthesis controller, in Control Aspects of Prosthetics and Orthotics (Elsevier 1983), pp. 43–53
4. D. Joshi, R. Singh, R. Ribeiro, S. Srivastava, U. Singh, S. Anand, Development of echo control strategy for AK prosthesis: An embedded system approach, in 2010 International Conference on Systems in Medicine and Biology (IEEE 2010), pp. 143–147
5. M. Li, X. Gao, Y. Wen, J. Si, H.H. Huang, Offline policy iteration based reinforcement learning controller for online robotic knee prosthesis parameter tuning, in 2019 International Conference on Robotics and Automation, ICRA 2019 (Institute of Electrical and Electronics Engineers Inc., 2019), pp. 2831–2837
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