TY - GEN
T1 - Performance analysis of hardware acceleration for locomotion mode recognition in robotic prosthetic control
AU - Mai, Jingeng
AU - Chen, Wanwen
AU - Zhang, Shichang
AU - Xu, Dongfang
AU - Wang, Qining
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - This paper aims to analyze computing performance of an on-board locomotion mode recognition system which was designed for robotic transtibial prosthesis. A hard-ware prototype based on field-programmable gate array was developed. Several algorithms, including support vector machine, back-propagation neural network, quadratic discriminant analysis and linear discriminant analysis, were implemented on board. Experiments on a transtibial amputee subject demonstrated that the proposed system can provide satisfactory acceleration effects on the four applied algorithms.
AB - This paper aims to analyze computing performance of an on-board locomotion mode recognition system which was designed for robotic transtibial prosthesis. A hard-ware prototype based on field-programmable gate array was developed. Several algorithms, including support vector machine, back-propagation neural network, quadratic discriminant analysis and linear discriminant analysis, were implemented on board. Experiments on a transtibial amputee subject demonstrated that the proposed system can provide satisfactory acceleration effects on the four applied algorithms.
UR - https://www.scopus.com/pages/publications/85062063687
U2 - 10.1109/CBS.2018.8612257
DO - 10.1109/CBS.2018.8612257
M3 - 会议稿件
AN - SCOPUS:85062063687
T3 - 2018 IEEE International Conference on Cyborg and Bionic Systems, CBS 2018
SP - 607
EP - 611
BT - 2018 IEEE International Conference on Cyborg and Bionic Systems, CBS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Cyborg and Bionic Systems, CBS 2018
Y2 - 25 October 2018 through 27 October 2018
ER -