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首页> 外文会议>2016 IEEE First Conference on Connected Health: Applications, Systems and Engineering Technologies >Using Wi-Fi Signals to Characterize Human Gait for Identification and Activity Monitoring
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Using Wi-Fi Signals to Characterize Human Gait for Identification and Activity Monitoring

机译：使用Wi-Fi信号表征人的步态以进行识别和活动监控

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Gait characterization and monitoring technologies are useful for the purposes of biometrics tracking and monitoring subjects (e.g., the elderly, at risk, and patients). Traditional techniques of measuring gait employ image processing or special sensors, which require either direct line of sight or physically attached sensors and thus, are cumbersome and costly. We propose Hoble that uses Wi-Fi signals to characterize multipath and Doppler Effect. Because of the physical property, the ubiquity, and the robustness of Wi-Fi signals, this type of sensing penetrates walls and does not require special signals or attachment of sensors to humans. In contrast to previous techniques Hoble 1) extracts features that identify individuals by their intrinsic body movement during walking without attachments to the body, 2) addresses the need to conduct real-time monitoring of individuals and detecting events such as falling, and 3) creates signatures by measuring Channel State Information (CSI), which provide high-fidelity location, movement, and identity information of human subjects. We implemented Hoble on a National Instruments (NI) Radio Frequency (RF) test-bed and conducted extensive experiments on six individuals at three locations. Our empirical results show that by applying a simple Naive Bayes classifier on the extracted features, the correct identification rate was 87%. The correct localization rate was 90%. We demonstrated line-of-sight (LoS) and with non-line-of-sight (NLoS) scenarios.
机译：步态表征和监视技术对于生物特征跟踪和监视对象（例如，老年人，处于危险中的患者和患者）的目的很有用。测量步态的传统技术采用图像处理或特殊传感器，这需要直接的视线或物理连接的传感器，因此麻烦且昂贵。我们提出了使用Wi-Fi信号表征多径和多普勒效应的Hoble。由于Wi-Fi信号的物理特性，普遍性和鲁棒性，这种类型的感测可以穿透墙壁，不需要特殊的信号或将传感器连接到人身上。与以前的技术相比，霍布勒1）提取通过行走过程中人体固有的运动来识别个体的特征，而无需依附于身体； 2）解决了对个体进行实时监视并检测诸如坠落等事件的需求； 3）创建了特征通过测量信道状态信息（CSI）来进行签名，该信息可提供人类对象的高保真位置，移动和身份信息。我们在美国国家仪器（NI）射频（RF）测试床上实施了霍布（Hoble），并在三个地点对六个人进行了广泛的实验。我们的经验结果表明，通过对提取的特征应用简单的朴素贝叶斯分类器，正确识别率为87％。正确的本地化率为90％。我们演示了视距（LoS）和非视距（NLoS）方案。

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