Contactless Body Movement Recognition during Sleep via WiFi Signals

Yangjie Cao, Fuchao Wang, Xinxin Lu, Nan Lin*, Bo Zhang, Zhi Liu, Stephan Sigg

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
112 Downloads (Pure)

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.

Original languageEnglish
Article number8936937
Pages (from-to)2028-2037
Number of pages10
JournalIEEE Internet of Things Journal
Volume7
Issue number3
DOIs
Publication statusPublished - 1 Mar 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • Body movement recognition
  • channel state information (CSI) data
  • deep learning
  • RNN
  • WiFi

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