TY - GEN
T1 - Portable Gesture Recognition Based on Flexible Stretchable Electronic Skin
AU - Xing, Jian
AU - Yang, Xin
AU - Yan, Liangliang
AU - Xu, Yuhan
AU - Cao, Zimei
AU - Wu, Yigong
AU - Song, Wenlong
AU - Yu, Duli
AU - Guo, Xiaoliang
AU - Wu, DI
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - In response to the large size and high price of traditional wearable gesture recognition systems, we presented a gesture recognition system based on self-developed flexible electronic skin (e-skin) with the advantages of easy portability, low cost and high recognition rate. Fourteen flexible sensors were integrated into the e-skin gesture recognition glove, which could monitor the movement of each finger joint. Meanwhile, an accelerometer was integrated, which could collect rollover information of the wrist. Furthermore, we innovatively proposed an improved dynamic time warping (DTW) algorithm to build a gesture template library, which is used to recognize complex and continuously changing gestures. Finally, the recognition results are displayed in the form of text, pictures and even the voice broadcast. Continuous recognition experiments on eight daily gestures were carried out, and the segmentation accuracy and recognition accuracy of continuous gestures is 97.49% and 93.73% respectively, which can meet the needs of deaf-mute people for normal communication with the outside world.
AB - In response to the large size and high price of traditional wearable gesture recognition systems, we presented a gesture recognition system based on self-developed flexible electronic skin (e-skin) with the advantages of easy portability, low cost and high recognition rate. Fourteen flexible sensors were integrated into the e-skin gesture recognition glove, which could monitor the movement of each finger joint. Meanwhile, an accelerometer was integrated, which could collect rollover information of the wrist. Furthermore, we innovatively proposed an improved dynamic time warping (DTW) algorithm to build a gesture template library, which is used to recognize complex and continuously changing gestures. Finally, the recognition results are displayed in the form of text, pictures and even the voice broadcast. Continuous recognition experiments on eight daily gestures were carried out, and the segmentation accuracy and recognition accuracy of continuous gestures is 97.49% and 93.73% respectively, which can meet the needs of deaf-mute people for normal communication with the outside world.
KW - DTW algorithm
KW - accelerometer
KW - flexible electronic skin
KW - gesture recognition
UR - http://www.scopus.com/inward/record.url?scp=85116358601&partnerID=8YFLogxK
U2 - 10.1109/ICISCE50968.2020.00478
DO - 10.1109/ICISCE50968.2020.00478
M3 - Conference contribution
AN - SCOPUS:85116358601
T3 - Proceedings - 2020 7th International Conference on Information Science and Control Engineering, ICISCE 2020
SP - 2437
EP - 2441
BT - Proceedings - 2020 7th International Conference on Information Science and Control Engineering, ICISCE 2020
A2 - Li, Shaozi
A2 - Dai, Ying
A2 - Ma, Jianwei
A2 - Cheng, Yun
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Information Science and Control Engineering, ICISCE 2020
Y2 - 18 December 2020 through 20 December 2020
ER -