TY - GEN
T1 - A-mode Ultrasound Driven Sensor Fusion for Hand Gesture Recognition
AU - Boyd, Peter
AU - Liu, Honghai
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - Traditionally, Surface electromyography (sEMG) has been the predominant method of sensing muscle activity in order to control myoelectric prosthesis. While many prosthesis control schemes used simple direct control, an ever increasing focus has moved to pattern recognition based approaches which promise a greater degree of natural control such as to further improve an amputees quality of life. Although pattern recognition based approaches have shown great promise, they have innate limitations due to changes that may occur during long term use which prevent clinical acceptance. Due to these limitations, researchers have increasingly investigated alternative modalities to provide more robust control schemes. A particular modality that has seen increasing interest is ultrasound based sensing due to its capability to better understand deep tissue activity.Within this research, A-mode ultrasound based sensing is proposed not as a replacement for sEMG based sensing but instead to augment and drive sEMG based sensing during activities that may otherwise prove challenging to traditional sEMg based control schemes.
AB - Traditionally, Surface electromyography (sEMG) has been the predominant method of sensing muscle activity in order to control myoelectric prosthesis. While many prosthesis control schemes used simple direct control, an ever increasing focus has moved to pattern recognition based approaches which promise a greater degree of natural control such as to further improve an amputees quality of life. Although pattern recognition based approaches have shown great promise, they have innate limitations due to changes that may occur during long term use which prevent clinical acceptance. Due to these limitations, researchers have increasingly investigated alternative modalities to provide more robust control schemes. A particular modality that has seen increasing interest is ultrasound based sensing due to its capability to better understand deep tissue activity.Within this research, A-mode ultrasound based sensing is proposed not as a replacement for sEMG based sensing but instead to augment and drive sEMG based sensing during activities that may otherwise prove challenging to traditional sEMg based control schemes.
KW - A-mode Ultrasound sEMG Hand Motion Recognition Rehabilitation
UR - https://www.scopus.com/pages/publications/85093844885
U2 - 10.1109/IJCNN48605.2020.9207492
DO - 10.1109/IJCNN48605.2020.9207492
M3 - 会议稿件
AN - SCOPUS:85093844885
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Joint Conference on Neural Networks, IJCNN 2020
Y2 - 19 July 2020 through 24 July 2020
ER -