Device-Free RF Human Body Fall Detection and Localization in Industrial Workplaces
Device-Free RF Human Body Fall Detection and Localization in Industrial Workplaces
复制标题
工业工作场所中的无设备RF人体跌倒检测和定位
DOI：
10.1109/jiot.2016.2624800
复制
发表时间：
2017-04-01
影响因子：
10.6
通讯作者：
Giussani, Matteo
中科院分区：
文献类型：
--
作者：
Kianoush, Sanaz;Savazzi, Stefano;Giussani, Matteo
工作空间内操作人员的跌倒检测和定位是确保安全工作环境的主要问题。最近的研究表明，无线通信中常用的射频 (RF) 信号的扰动也可以用作无设备人体运动检测的传感工具。基于射频的无设备人体传感应用范围从无标签身体定位到人类福祉的检测和监控（电子健康）。在本文中，我们提出了一种实时人体运动传感系统，特别关注关节身体定位和跌倒检测。所提出的系统持续监控和处理在 2.4 GHz ISM 频段运行并支持机器对机器通信功能的符合行业标准的无线电设备发出的射频信号。利用影响射频信号传播的人为衍射和多径现象进行身体定位，而对于跌倒检测，应用隐藏的马尔可夫模型来辨别操作员的不同姿势，并通过跟踪接收到的信号强度指示器足迹来检测安全相关事件。跌倒检测性能通过不同环境下的大量实验测量得到证实。此外，我们还提出了一种传感器融合工具，能够将无设备的基于射频的传感系统集成到工业图像传感器框架内。在现场试验测量期间进行的初步结果证实了所提出的方法在定位精度以及从撞击前姿势正确检测跌倒事件的灵敏度/特异性方面的有效性。
Fall detection and localization of human operators inside a workspace are major issues in ensuring a safe working environment. Recent research has shown that the perturbations of the radio-frequency (RF) signals commonly adopted for wireless communications can also be used as sensing tools for device-free human motion detection. Device-free RF-based human sensing applications range from tag-less body localization to detection and monitoring of human well-being (e-Health). In this paper, we propose a real-time system for human body motion sensing with special focus on joint body localization and fall detection. The proposed system continuously monitors and processes the RF signals emitted by industry-compliant radio devices operating in the 2.4 GHz ISM band and supporting machine-to-machine communication functions. Human-induced diffraction and multipath phenomena that affect RF signal propagation are leveraged for body localization while for fall detection a hidden Markov model is applied to discern different postures of the operator and to detect safety-relevant events by tracking the received signal strength indicator footprints. Fall detection performances are corroborated by extensive experimental measurements in different settings. In addition, we propose also a sensor fusion tool that is able to integrate the device-free RF-based sensing system within an industrial image sensors framework. Preliminary results, conducted during field trial measurements, confirm the effectiveness of the proposed approach in terms of localization accuracy, and sensitivity/specificity to correctly detect a fall event from preimpact postures.