Danger-Pose Detection System Using Commodity Wi-Fi for Bathroom Monitoring
Danger-Pose Detection System Using Commodity Wi-Fi for Bathroom Monitoring
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DOI：
10.3390/s19040884
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发表时间：
2019-02
期刊：
影响因子：
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通讯作者：
Zizheng Zhang;S. Ishida;S. Tagashira;Akira Fukuda
中科院分区：
文献类型：
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作者：
Zizheng Zhang;S. Ishida;S. Tagashira;Akira Fukuda
由于地板湿滑和温度变化，卫生间比其他房间发生事故的可能性更高。由于隐私和湿度高，我们在使用基于摄像头和可穿戴传感器的传统医疗方法在浴室内进行监控时面临着困难。本文提出了一种基于商用Wi-Fi设备的危险姿态检测系统，该系统可应用于浴室监控，保护隐私。基于机器学习的检测方法通常需要在目标情况下收集数据，这在危险情况下很难检测到。因此，我们采用了基于机器学习的异常检测方法，该方法在异常条件下需要少量数据，最大限度地减少了在危险条件下收集的所需训练数据。我们首先从Wi-Fi信道状态信息(CSI)中提取幅度和相移，以提取与人类活动相关的低频分量。然后从CSI的时间变化中分别提取静态特征和动态特征。最后，将静态和动态特征送入单类支持向量机，作为异常检测方法，对用户是否在浴缸、洗澡安全或处于危险状态进行分类。我们进行了实验评估，证明了我们的危险姿态检测系统在非视距(NLOS)场景下取得了很高的检测性能。
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.