TY - GEN
T1 - Device-Free Sensing for Gesture Recognition by Wi-Fi Communication Signal Based on Auto-encoder/decoder Neural Network
AU - Zhong, Yi
AU - Huang, Yan
AU - Jiang, Ting
N1 - Publisher Copyright:
© 2020, Springer Nature Singapore Pte Ltd.
PY - 2020
Y1 - 2020
N2 - Gesture recognition has been found to be a vital mission for a variety of applications, such as smart surveillance, elder care, virtual reality, advanced user interface, etc. Recently, an emerging sensing technology, namely device-free sensing (DFS), has been introduced to the domain of gesture recognition which only uses radio-frequency (RF) signals without the need to equip any devices or extra hardware support; thus, it would be a natural choice to fully leverage ubiquitous Wi-Fi signals in almost every modern building. Although the feasibility of using this technology for gesture recognition has been explored to some extent, we observe that it still cannot perform promisingly for some gestures which maybe look nearly identical in a certain instant. Therefore, in this paper, we conduct experiments with several typical hand gestures in the opposite direction based on a proposed Auto-Encoder/Decoder (Auto-ED) deep neural network to address gesture recognition in our case. Compared with several traditional learning methods, experimental results demonstrate that our proposed approach can best tackle the challenge of gesture recognition for identical motions, which indicates its potential application values in the near future.
AB - Gesture recognition has been found to be a vital mission for a variety of applications, such as smart surveillance, elder care, virtual reality, advanced user interface, etc. Recently, an emerging sensing technology, namely device-free sensing (DFS), has been introduced to the domain of gesture recognition which only uses radio-frequency (RF) signals without the need to equip any devices or extra hardware support; thus, it would be a natural choice to fully leverage ubiquitous Wi-Fi signals in almost every modern building. Although the feasibility of using this technology for gesture recognition has been explored to some extent, we observe that it still cannot perform promisingly for some gestures which maybe look nearly identical in a certain instant. Therefore, in this paper, we conduct experiments with several typical hand gestures in the opposite direction based on a proposed Auto-Encoder/Decoder (Auto-ED) deep neural network to address gesture recognition in our case. Compared with several traditional learning methods, experimental results demonstrate that our proposed approach can best tackle the challenge of gesture recognition for identical motions, which indicates its potential application values in the near future.
KW - Auto-encoder and decoder (Auto-ED)
KW - Device-free sensing (DFS)
KW - Gesture recognition
UR - http://www.scopus.com/inward/record.url?scp=85084758174&partnerID=8YFLogxK
U2 - 10.1007/978-981-13-9409-6_103
DO - 10.1007/978-981-13-9409-6_103
M3 - Conference contribution
AN - SCOPUS:85084758174
SN - 9789811394089
T3 - Lecture Notes in Electrical Engineering
SP - 887
EP - 894
BT - Communications, Signal Processing, and Systems - Proceedings of the 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019
A2 - Liang, Qilian
A2 - Wang, Wei
A2 - Liu, Xin
A2 - Na, Zhenyu
A2 - Jia, Min
A2 - Zhang, Baoju
PB - Springer
T2 - 8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019
Y2 - 20 July 2019 through 22 July 2019
ER -