TY - GEN
T1 - Foot gesture recognition with flexible high-density device based on convolutional neural network
AU - Lin, Chengyu
AU - Tang, Yuxuan
AU - Zhou, Yong
AU - Zhang, Kuangen
AU - Fan, Zixuan
AU - Yang, Yang
AU - Leng, Yuquan
AU - Fu, Chenglong
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/7/3
Y1 - 2021/7/3
N2 - Upper-Limb prosthesis control is a huge challenge for high-level amputees or amputated patients with weak residual muscles signal. Previous researches achieved the control of prosthesis by foot electromyography (EMG). However, low adaptability and gesture classification accuracy due to muscle movement and device limits restrict the performance. Therefore, this paper proposes a flexible high-density wearable device based on convolutional neural network for foot gestures recognition. The flexible wearable device stretches with muscle movement and makes the recognition process more accurate and efficient. Nine classes of foot gestures that intuitively map the movements of prosthesis are classified by the convolutional neural network classifiers. This paper reaches an average classification accuracy of 93.98% for nine classes of foot gestures. High-accuracy recognition based on the flexible wearable device provides a possibility for the control of upper-limb prosthesis.
AB - Upper-Limb prosthesis control is a huge challenge for high-level amputees or amputated patients with weak residual muscles signal. Previous researches achieved the control of prosthesis by foot electromyography (EMG). However, low adaptability and gesture classification accuracy due to muscle movement and device limits restrict the performance. Therefore, this paper proposes a flexible high-density wearable device based on convolutional neural network for foot gestures recognition. The flexible wearable device stretches with muscle movement and makes the recognition process more accurate and efficient. Nine classes of foot gestures that intuitively map the movements of prosthesis are classified by the convolutional neural network classifiers. This paper reaches an average classification accuracy of 93.98% for nine classes of foot gestures. High-accuracy recognition based on the flexible wearable device provides a possibility for the control of upper-limb prosthesis.
UR - https://www.scopus.com/pages/publications/85116191862
U2 - 10.1109/ICARM52023.2021.9536141
DO - 10.1109/ICARM52023.2021.9536141
M3 - 会议稿件
AN - SCOPUS:85116191862
T3 - 2021 6th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2021
SP - 306
EP - 311
BT - 2021 6th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2021
Y2 - 3 July 2021 through 5 July 2021
ER -