Given the significant amount of time people spend in vehicles, health issues under driving condition have become a major concern. Such issues may vary from fatigue, asthma, stroke, to even heart attack, yet they can be adequately indicated by vital signs and abnormal activities. Therefore, in-vehicle vital sign monitoring can help us predict and hence prevent these issues. Whereas existing sensor-based (including camera) methods could be used to detect these indicators, privacy concern and system complexity both call for a convenient yet effective and robust alternative. This paper aims to develop V2iFi, an intelligent system performing monitoring tasks using a COTS impulse radio mounted on the windshield. V2iFi is capable of reliably detecting driver's vital signs under driving condition and with the presence of passengers, thus allowing for potentially inferring corresponding health issues. Compared with prior work based on Wi-Fi CSI, V2iFi is able to distinguish reflected signals from multiple users, and hence provide finer-grained measurements under more realistic settings. We evaluate V2iFi both in lab environments and during real-life road tests; the results demonstrate that respiratory rate, heart rate, and heart rate variability can all be estimated accurately. Based on these estimation results, we further discuss how machine learning models can be applied on top of V2iFi so as to improve both physiological and psychological wellbeing in driving environments.
翻译:鉴于人们在车辆上花费了大量时间,驾驶条件下的健康问题已成为一个主要关切问题,这些问题可能从疲劳、哮喘、中风到心脏病发病等不同,但生命迹象和异常活动可以充分表明这些问题,因此,车辆内重要信号监测可以帮助我们预测并因此防止这些问题。虽然现有的基于传感器(包括相机)的方法可以用来检测这些指标、隐私关切和系统复杂性,两者都需要一种方便、有效和稳健的替代方法。本文的目的是开发V2Fi,一个智能系统,利用在挡风玻璃上安装的COTS脉冲无线电执行监测任务。V2Fi能够可靠地探测司机在驾驶状态下和乘客在场的情况下的重要信号,从而有可能推断出相应的健康问题。与以前基于Wi-Fi CSI的工作相比,V2Fi能够区分多个用户反映的信号,从而在更现实的环境中提供精细的测量。我们在实验室环境中和在真实的路上测试中评价V2FiFi;结果表明呼吸率、心率和心率变化率能够可靠地探测到司机在驾驶状况上如何精确地评估。