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

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 pre...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2019-02, Vol.19 (4), p.884
Main Authors: Zhang, Zizheng, Ishida, Shigemi, Tagashira, Shigeaki, Fukuda, Akira
Format: Article
Language:English
Subjects:
Accidental Falls - prevention & control
Algorithms
anomaly detection
Biosensing Techniques - methods
channel state information (CSI)
healthcare
Humans
Machine Learning
Monitoring, Physiologic - methods
non-line-of-sight (NLOS)
one-class support vector machine (SVM)
Posture - physiology
Toilet Facilities
Wearable Electronic Devices
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s19040884