TY - GEN
T1 - Flexible Non-contact Capacitive Sensing for Hand Gesture Recognition
AU - Wang, Tiantong
AU - Zhao, Yunbiao
AU - Wang, Qining
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Hand gesture recognition has become a popular research topic of human machine interface (HMI), and effective wearable sensor is an important component in the loop of hand gesture recognition system. In this paper, we introduce a flexible non-contact capacitive wristband that can be used to detect both wrist and finger gestures. To demonstrate the effectiveness and performance of the designed prototype, nine wrist gestures and ten finger gestures were selected. Five subjects participated in the experiment. To validate the importance of considering spacial relationship among channels, especially when discriminating intricate finger gestures, CNN was implemented and compared with LDA. In the wrist gesture recognition task, LDA achieved the average accuracy of 98.38%, and CNN achieved the average accuracy of 99.81%. In the finger gesture recognition task, LDA achieved the average accuracy of 90.04%, and CNN achieved the average accuracy of 95.54%. This study suggested that the designed flexible non-contact capacitive wristband could be used as an alternative for hand gesture recognition, and considering spacial relationship among channels on different measuring location yields better recognition result.
AB - Hand gesture recognition has become a popular research topic of human machine interface (HMI), and effective wearable sensor is an important component in the loop of hand gesture recognition system. In this paper, we introduce a flexible non-contact capacitive wristband that can be used to detect both wrist and finger gestures. To demonstrate the effectiveness and performance of the designed prototype, nine wrist gestures and ten finger gestures were selected. Five subjects participated in the experiment. To validate the importance of considering spacial relationship among channels, especially when discriminating intricate finger gestures, CNN was implemented and compared with LDA. In the wrist gesture recognition task, LDA achieved the average accuracy of 98.38%, and CNN achieved the average accuracy of 99.81%. In the finger gesture recognition task, LDA achieved the average accuracy of 90.04%, and CNN achieved the average accuracy of 95.54%. This study suggested that the designed flexible non-contact capacitive wristband could be used as an alternative for hand gesture recognition, and considering spacial relationship among channels on different measuring location yields better recognition result.
KW - Capacitive sensing
KW - Hand gesture recognition
KW - Pattern recognition
UR - https://www.scopus.com/pages/publications/85118205859
U2 - 10.1007/978-3-030-89095-7_58
DO - 10.1007/978-3-030-89095-7_58
M3 - 会议稿件
AN - SCOPUS:85118205859
SN - 9783030890940
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 611
EP - 621
BT - Intelligent Robotics and Applications - 14th International Conference, ICIRA 2021, Proceedings
A2 - Liu, Xin-Jun
A2 - Nie, Zhenguo
A2 - Yu, Jingjun
A2 - Xie, Fugui
A2 - Song, Rui
PB - Springer Science and Business Media Deutschland GmbH
T2 - 14th International Conference on Intelligent Robotics and Applications, ICIRA 2021
Y2 - 22 October 2021 through 25 October 2021
ER -