  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.
