TY - GEN
T1 - Feature Matching-Based Gait Phase Prediction for Obstacle Crossing Control of Powered Transfemoral Prosthesis
AU - Zhang, Jiaxuan
AU - Leng, Yuquan
AU - Guo, Yixuan
AU - Fu, Chenglong
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - For amputees with powered transfemoral prosthetics, navigating obstacles or complex terrain remains challenging. This study addresses this issue by using an inertial sensor on the sound ankle to guide obstacle-crossing movements. A genetic algorithm computes the optimal neural network structure to predict the required angles of the thigh and knee joints. A gait progression prediction algorithm determines the actuation angle index for the prosthetic knee motor, ultimately defining the necessary thigh and knee angles and gait progression. Results show that when the standard deviation of Gaussian noise added to the thigh angle data is less than 1, the method can effectively eliminate noise interference, achieving 100% accuracy in gait phase estimation under 150 Hz, with thigh angle prediction error being 8.71% and knee angle prediction error being 6.78%. These findings demonstrate the method's ability to accurately predict gait progression and joint angles, offering significant practical value for obstacle negotiation in powered transfemoral prosthetics.
AB - For amputees with powered transfemoral prosthetics, navigating obstacles or complex terrain remains challenging. This study addresses this issue by using an inertial sensor on the sound ankle to guide obstacle-crossing movements. A genetic algorithm computes the optimal neural network structure to predict the required angles of the thigh and knee joints. A gait progression prediction algorithm determines the actuation angle index for the prosthetic knee motor, ultimately defining the necessary thigh and knee angles and gait progression. Results show that when the standard deviation of Gaussian noise added to the thigh angle data is less than 1, the method can effectively eliminate noise interference, achieving 100% accuracy in gait phase estimation under 150 Hz, with thigh angle prediction error being 8.71% and knee angle prediction error being 6.78%. These findings demonstrate the method's ability to accurately predict gait progression and joint angles, offering significant practical value for obstacle negotiation in powered transfemoral prosthetics.
KW - Lower limb prosthetics
KW - cross obstacles
KW - phase phase prediction
UR - https://www.scopus.com/pages/publications/105029983799
U2 - 10.1109/IROS60139.2025.11247543
DO - 10.1109/IROS60139.2025.11247543
M3 - 会议稿件
AN - SCOPUS:105029983799
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 11999
EP - 12004
BT - IROS 2025 - 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems, Conference Proceedings
A2 - Laugier, Christian
A2 - Renzaglia, Alessandro
A2 - Atanasov, Nikolay
A2 - Birchfield, Stan
A2 - Cielniak, Grzegorz
A2 - De Mattos, Leonardo
A2 - Fiorini, Laura
A2 - Giguere, Philippe
A2 - Hashimoto, Kenji
A2 - Ibanez-Guzman, Javier
A2 - Kamegawa, Tetsushi
A2 - Lee, Jinoh
A2 - Loianno, Giuseppe
A2 - Luck, Kevin
A2 - Maruyama, Hisataka
A2 - Martinet, Philippe
A2 - Moradi, Hadi
A2 - Nunes, Urbano
A2 - Pettre, Julien
A2 - Pretto, Alberto
A2 - Ranzani, Tommaso
A2 - Ronnau, Arne
A2 - Rossi, Silvia
A2 - Rouse, Elliott
A2 - Ruggiero, Fabio
A2 - Simonin, Olivier
A2 - Wang, Danwei
A2 - Yang, Ming
A2 - Yoshida, Eiichi
A2 - Zhao, Huijing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2025
Y2 - 19 October 2025 through 25 October 2025
ER -