V2iFi: in-Vehicle Vital Sign Monitoring via Compact RF Sensing
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 0 of its 69 references that are in the map. First posted on arXiv 28 Oct 2021.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 1.
3 papers.
- 2023AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Drivingcited by 32
- 2021RF-Net: a Unified Meta-learning Framework for RF-enabled One-shot Human Activity Recognitioncited by 24
- 2021MoRe-Fi: Motion-robust and Fine-grained Respiration Monitoring via Deep-Learning UWB Radarcited by 11
- 2023AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving17 Feb 2023
- 2021MoRe-Fi: Motion-robust and Fine-grained Respiration Monitoring via Deep-Learning UWB Radar16 Nov 2021
- 2021RF-Net: a Unified Meta-learning Framework for RF-enabled One-shot Human Activity Recognition29 Oct 2021