Contactless Body Movement Recognition during Sleep via WiFi Signals

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

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

37 Sitaatiot (Scopus)
319 Lataukset (Pure)

Abstrakti

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.

AlkuperäiskieliEnglanti
Artikkeli8936937
Sivut2028-2037
Sivumäärä10
JulkaisuIEEE Internet of Things Journal
Vuosikerta7
Numero3
DOI - pysyväislinkit
TilaJulkaistu - 1 maalisk. 2020
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Sormenjälki

Sukella tutkimusaiheisiin 'Contactless Body Movement Recognition during Sleep via WiFi Signals'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä