Contactless Body Movement Recognition during Sleep via WiFi Signals

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2020-03-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
10
2028-2037
Series
IEEE Internet of Things Journal, Volume 7, issue 3
Abstract
Body movement is one of the most important indicators of sleep quality for elderly people living alone. Body movement is crucial for sleep staging and can be combined with other indicators, such as breathing and heart rate to monitor sleep quality. Nevertheless, traditional sleep monitoring methods are inconvenient and may invade users' privacy. To solve these problems, we propose a contactless body movement recognition (CBMR) method via WiFi signals. First, CBMR uses commercial off-the-shelf WiFi devices to collect channel state information (CSI) data of body movement and segment the CSI data by sliding window. Then, the context information of the segmented CSI data is learned by a bidirectional recurrent neural network (Bi-RNN). Bi-RNN can fuse the forward and backward propagation information at some point, and input it into a deeper independently recurrent neural network (IndRNN) with residual mechanism to extract the deeper features and capture the time dependencies of CSI data. Finally, the type of body movement can be recognized and classified by the softmax function. CBMR can effectively reduce data preprocessing and the delay caused by manually extracting features. The results of an experiment conducted on a complex body movement data set show that our method gives desirable performance and achieves an average accuracy of greater than 93.5%, which implies a prospect application of CBMR.
Description
Keywords
Body movement recognition, channel state information (CSI) data, deep learning, RNN, WiFi
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Citation
Cao , Y , Wang , F , Lu , X , Lin , N , Zhang , B , Liu , Z & Sigg , S 2020 , ' Contactless Body Movement Recognition during Sleep via WiFi Signals ' , IEEE Internet of Things Journal , vol. 7 , no. 3 , 8936937 , pp. 2028-2037 . https://doi.org/10.1109/JIOT.2019.2960823