Graduate School/Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, Japan.
Faculty of Informatics, Kansai University, Osaka 569-1095, Japan.
Sensors (Basel). 2019 Feb 20;19(4):884. doi: 10.3390/s19040884.
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.
浴室比其他房间更容易发生事故，因为地面湿滑和温度变化。由于隐私性高和湿度大，我们在使用基于摄像头和可穿戴传感器的传统医疗保健方法对浴室进行监测时遇到了困难。在本文中，我们提出了一种使用商用 Wi-Fi 设备的危险姿势检测系统，该系统可应用于浴室监测，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，这在危险情况的检测中较为困难。因此，我们采用了一种基于机器学习的异常检测方法，该方法在异常情况下需要少量数据，从而最大限度地减少在危险情况下收集的训练数据。我们首先从 Wi-Fi 信道状态信息 (CSI) 中推导出幅度和相位偏移，以提取与人体活动相关的低频分量。然后，我们分别从 CSI 随时间的变化中提取静态和动态特征。最后，将静态和动态特征输入到作为异常检测方法的单类支持向量机 (SVM) 中，以分类用户是否不在浴缸中、安全洗澡或处于危险状态。我们进行了实验评估，并证明我们的危险姿势检测系统在非视距 (NLOS) 场景下具有很高的检测性能。
