V2iFi: in-Vehicle Vital Sign Monitoring via Compact RF Sensing

TL;DR

V2iFi is a novel in-vehicle vital sign monitoring system using compact RF sensing that accurately detects driver and passenger health indicators without privacy concerns, suitable for real-world driving conditions.

Contribution

This work introduces V2iFi, a COTS impulse radio-based system capable of fine-grained vital sign detection in vehicles, outperforming prior Wi-Fi CSI methods in multi-user scenarios.

Findings

Accurately estimates respiratory rate, heart rate, and HRV in lab and real-world tests.

Distinguishes signals from multiple users, enabling detailed health monitoring.

Demonstrates potential for health issue prediction and prevention during driving.

Abstract

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.