RT-Fall: A Real-Time and Contactless Fall Detection System with Commodity WiFi Devices
RT-Fall: A Real-Time and Contactless Fall Detection System with Commodity WiFi Devices
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RT-Fall：采用商用 WiFi 设备的实时非接触式跌倒检测系统
DOI：
10.1109/tmc.2016.2557795
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发表时间：
2017-02-01
影响因子：
7.9
通讯作者：
Li, Shengjie
中科院分区：
文献类型：
--
作者：
Wang, Hao;Zhang, Daqing;Li, Shengjie
本文介绍了一种基于商用WiFi设备的实时、非接触式、低成本、高精度的室内跌倒检测系统RT-Fall的设计与实现。RT-Fall利用了商用WiFi设备中可访问的细粒度通道状态信息(CSI)的相位和幅度，首次实现了实时自动分割和检测跌倒的目标，使用户无需佩戴任何身体设备即可自然而连续地进行日常活动。这项工作有两个关键的技术贡献。首先，我们发现两个天线上的CSI相位差对于活动识别来说是一个比幅度更敏感的基本信号，这可以非常可靠地分割坠落和类似坠落的活动。其次，我们发现了跌倒在时频域的尖峰功率谱下降模式，并进一步挖掘了新的特征提取和准确的跌倒分割/检测的洞察力。在四个室内场景中的实验结果表明，RT-Fall的性能始终优于最先进的WiFall方法，敏感度平均提高14%，特异度平均提高10%。
This paper presents the design and implementation of RT-Fall, a real-time, contactless, low-cost yet accurate indoor fall detection system using the commodity WiFi devices. RT-Fall exploits the phase and amplitude of the fine-grained Channel State Information (CSI) accessible in commodity WiFi devices, and for the first time fulfills the goal of segmenting and detecting the falls automatically in real-time, which allows users to perform daily activities naturally and continuously without wearing any devices on the body. This work makes two key technical contributions. First, we find that the CSI phase difference over two antennas is a more sensitive base signal than amplitude for activity recognition, which can enable very reliable segmentation of fall and fall-like activities. Second, we discover the sharp power profile decline pattern of the fall in the time-frequency domain and further exploit the insight for new feature extraction and accurate fall segmentation/detection. Experimental results in four indoor scenarios demonstrate that RT-fall consistently outperforms the state-of-the-art approach WiFall with 14 percent higher sensitivity and 10 percent higher specificity on average.