TY - GEN
T1 - Environmental Recognition and Multimode Continuous-Phase Control for a Powered Transfemoral Prosthesis
AU - Yin, Shucong
AU - Chen, Xinxing
AU - Ma, Teng
AU - Wang, Yuxuan
AU - Guo, Yixuan
AU - Leng, Yuquan
AU - Fu, Chenglong
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The continuous-phase-based control method is currently being developed for powered transfemoral prostheses. However, most of the research concentrates on single locomotion mode. The multimode continuous-phase control method for powered transfemoral prostheses is still an open question. To solve the existing problem, this paper proposes a multimode continuous-phase control method for powered transfemoral prostheses. The prosthesis first needs to recognize the surrounding environment and obtain the corresponding environmental features. Then, the gait phase is estimated by phase variable which represents the mapping relationship between thigh angle and gait phase. Finally, the desired trajectories of the knee and ankle joints on various locomotion modes are constructed as functions of the gait phase and environmental features. These functions are called virtual constraints. The proposed phase variable and virtual constraints are evaluated on a public dataset through offline analysis. The root mean squared errors (RMSEs) of the phase variable and virtual constraints in all locomotion modes are less than 3 % and 4 %, respectively. It proves that the proposed method can precisely predict the gait phase and joints angle for powered prostheses in all locomotion modes.
AB - The continuous-phase-based control method is currently being developed for powered transfemoral prostheses. However, most of the research concentrates on single locomotion mode. The multimode continuous-phase control method for powered transfemoral prostheses is still an open question. To solve the existing problem, this paper proposes a multimode continuous-phase control method for powered transfemoral prostheses. The prosthesis first needs to recognize the surrounding environment and obtain the corresponding environmental features. Then, the gait phase is estimated by phase variable which represents the mapping relationship between thigh angle and gait phase. Finally, the desired trajectories of the knee and ankle joints on various locomotion modes are constructed as functions of the gait phase and environmental features. These functions are called virtual constraints. The proposed phase variable and virtual constraints are evaluated on a public dataset through offline analysis. The root mean squared errors (RMSEs) of the phase variable and virtual constraints in all locomotion modes are less than 3 % and 4 %, respectively. It proves that the proposed method can precisely predict the gait phase and joints angle for powered prostheses in all locomotion modes.
KW - environmental recognition
KW - multimode virtual constraints
KW - phase variable
KW - prosthesis
UR - https://www.scopus.com/pages/publications/85182924519
U2 - 10.1109/ICDL55364.2023.10364513
DO - 10.1109/ICDL55364.2023.10364513
M3 - 会议稿件
AN - SCOPUS:85182924519
T3 - 2023 IEEE International Conference on Development and Learning, ICDL 2023
SP - 55
EP - 60
BT - 2023 IEEE International Conference on Development and Learning, ICDL 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Development and Learning, ICDL 2023
Y2 - 9 November 2023 through 11 November 2023
ER -