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Open AccessArticle

Danger-Pose Detection System Using Commodity Wi-Fi for Bathroom Monitoring

by 1,*,†, 1,†, 2,† and 1,†
1
Graduate School/Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, Japan
2
Faculty of Informatics, Kansai University, Osaka 569-1095, Japan
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2019, 19(4), 884; https://doi.org/10.3390/s19040884
Received: 18 January 2019 / Revised: 10 February 2019 / Accepted: 18 February 2019 / Published: 20 February 2019
(This article belongs to the Special Issue From Sensors to Ambient Intelligence for Health and Social Care)
A bathroom has higher probability of accidents than other rooms due to a slippery floor and temperature change. Because of high privacy and humidity, we face difficulties in monitoring inside a bathroom using traditional healthcare methods based on cameras and wearable sensors. In this paper, we present a danger-pose detection system using commodity Wi-Fi devices, which can be applied to bathroom monitoring, preserving privacy. A machine learning-based detection method usually requires data collected in target situations, which is difficult in detection-of-danger situations. We therefore employ a machine learning-based anomaly-detection method that requires a small amount of data in anomaly conditions, minimizing the required training data collected in dangerous conditions. We first derive the amplitude and phase shift from Wi-Fi channel state information (CSI) to extract low-frequency components that are related to human activities. We then separately extract static and dynamic features from the CSI changes in time. Finally, the static and dynamic features are fed into a one-class support vector machine (SVM), which is used as an anomaly-detection method, to classify whether a user is not in bathtub, bathing safely, or in dangerous conditions. We conducted experimental evaluations and demonstrated that our danger-pose detection system achieved a high detection performance in a non-line-of-sight (NLOS) scenario. View Full-Text
Keywords: channel state information (CSI); non-line-of-sight (NLOS); one-class support vector machine (SVM); anomaly detection; healthcare channel state information (CSI); non-line-of-sight (NLOS); one-class support vector machine (SVM); anomaly detection; healthcare
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MDPI and ACS Style

Zhang, Z.; Ishida, S.; Tagashira, S.; Fukuda, A. Danger-Pose Detection System Using Commodity Wi-Fi for Bathroom Monitoring. Sensors 2019, 19, 884. https://doi.org/10.3390/s19040884

AMA Style

Zhang Z, Ishida S, Tagashira S, Fukuda A. Danger-Pose Detection System Using Commodity Wi-Fi for Bathroom Monitoring. Sensors. 2019; 19(4):884. https://doi.org/10.3390/s19040884

Chicago/Turabian Style

Zhang, Zizheng; Ishida, Shigemi; Tagashira, Shigeaki; Fukuda, Akira. 2019. "Danger-Pose Detection System Using Commodity Wi-Fi for Bathroom Monitoring" Sensors 19, no. 4: 884. https://doi.org/10.3390/s19040884

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