TY - GEN
T1 - User Controlled Interface for Tuning Robotic Knee Prosthesis
AU - Alili, Abbas
AU - Nalam, Varun
AU - Li, Minhan
AU - Liu, Ming
AU - Si, Jennie
AU - Huang, He Helen
N1 - Funding Information:
*Research supported by NSF 1926998, 1563454/1563921, 1808752/1808898. (Abbas Alili and Varun Nalam are co-first authors.) A. Alili is with the Department of Electrical and Computer Engineering, NC State University, Raleigh, NC, 27606 USA. V. Nalam, M. Li, M. Liu and H. Huang are with the NCSU/UNC Department of Biomedical Engineering, NC State University, Raleigh, NC,
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The tuning process for a robotic prosthesis is a challenging and time-consuming task both for users and clinicians. An automatic tuning approach using reinforcement learning (RL) has been developed for a knee prosthesis to address the challenges of manual tuning methods. The algorithm tunes the optimal control parameters based on the provided knee joint profile that the prosthesis is expected to replicate during gait safely. This paper presents an intuitive interface designed for the prosthesis users and clinicians to choose the preferred knee joint profile during gait and use the autotuner to replicate in the prosthesis. The interface-based approach is validated by observing the ability of the tuning algorithm to successfully converge to various alternate knee profiles by testing on two able-bodied subjects walking with a robotic knee prosthesis. The algorithm was found to converge successfully in an average duration of 1.15 min for the first subject and 2.31 min for the second subject. Further, the subjects displayed different preferences for optimal profiles reinforcing the need to tune alternate profiles. The implications of the results in the tuning of robotic prosthetic devices are discussed.
AB - The tuning process for a robotic prosthesis is a challenging and time-consuming task both for users and clinicians. An automatic tuning approach using reinforcement learning (RL) has been developed for a knee prosthesis to address the challenges of manual tuning methods. The algorithm tunes the optimal control parameters based on the provided knee joint profile that the prosthesis is expected to replicate during gait safely. This paper presents an intuitive interface designed for the prosthesis users and clinicians to choose the preferred knee joint profile during gait and use the autotuner to replicate in the prosthesis. The interface-based approach is validated by observing the ability of the tuning algorithm to successfully converge to various alternate knee profiles by testing on two able-bodied subjects walking with a robotic knee prosthesis. The algorithm was found to converge successfully in an average duration of 1.15 min for the first subject and 2.31 min for the second subject. Further, the subjects displayed different preferences for optimal profiles reinforcing the need to tune alternate profiles. The implications of the results in the tuning of robotic prosthetic devices are discussed.
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U2 - 10.1109/IROS51168.2021.9636264
DO - 10.1109/IROS51168.2021.9636264
M3 - Conference contribution
AN - SCOPUS:85124348623
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 6190
EP - 6195
BT - IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
Y2 - 27 September 2021 through 1 October 2021
ER -