Open AccessTowards fully domain‐independent gesture recognition using COTS WiFi device
Author(s) -
Yin Yuqing,
Zhang Zixin,
Yang Xu,
Yan Faren,
Niu Qiang
Publication year - 2021
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/ell2.12097
Subject(s) - discriminator , computer science , domain (mathematical analysis) , feature (linguistics) , gesture recognition , artificial intelligence , gesture , key (lock) , extractor , pattern recognition (psychology) , feature extraction , speech recognition , machine learning , computer vision , engineering , computer security , mathematical analysis , telecommunications , linguistics , philosophy , mathematics , detector , process engineering
Abstract This letter proposes a fully domain‐independent WiFi‐based gesture recognition system based on the multi‐label adversarial network. Unlike pioneer machine learning works, our system does not require retraining in the target domain, which benefits from our key idea of eliminating fully domain information such as orientations, locations, environment information. The system proposed by us includes three parts: Feature extractor, domain discriminator, and gesture recogniser. The feature extractor attempts to deceive the domain discriminator based on the adversarial network structure so that it is difficult to judge the input domain label, thereby obtaining domain‐independent features and realising fully domain‐independent gesture recognition. Extensive experiments are conducted on the Widar 3.0 dataset and our dataset to evaluate system performance. The results show that our system can achieve an in‐domain recognition accuracy of 93.2% and a cross‐domain recognition accuracy of 87.1%, which is superior to other state‐of‐the‐art works.