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增强道路安全：基于Huber-Kalman和自相关算法的快速准确非接触式驾驶员心率变异性检测

Enhancing Road Safety: Fast and Accurate Noncontact Driver HRV Detection Based on Huber-Kalman and Autocorrelation Algorithms.

作者信息

Luo Yunlong, Yang Yang, Ma Yanbo, Huang Runhe, Qi Alex, Ma Muxin, Qi Yihong

机构信息

School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China.

Pontosense Inc., Toronto, ON M5C3G8, Canada.

出版信息

Biomimetics (Basel). 2024 Aug 9;9(8):481. doi: 10.3390/biomimetics9080481.

DOI:10.3390/biomimetics9080481
PMID:39194460
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11351964/
Abstract

Enhancing road safety by monitoring a driver's physical condition is critical in both conventional and autonomous driving contexts. Our research focuses on a wireless intelligent sensor system that utilizes millimeter-wave (mmWave) radar to monitor heart rate variability (HRV) in drivers. By assessing HRV, the system can detect early signs of drowsiness and sudden medical emergencies, such as heart attacks, thereby preventing accidents. This is particularly vital for fully self-driving (FSD) systems, as it ensures control is not transferred to an impaired driver. The proposed system employs a 60 GHz frequency-modulated continuous wave (FMCW) radar placed behind the driver's seat. This article mainly describes how advanced signal processing methods, including the Huber-Kalman filtering algorithm, are applied to mitigate the impact of respiration on heart rate detection. Additionally, the autocorrelation algorithm enables fast detection of vital signs. Intensive experiments demonstrate the system's effectiveness in accurately monitoring HRV, highlighting its potential to enhance safety and reliability in both traditional and autonomous driving environments.

摘要

在传统驾驶和自动驾驶环境中，通过监测驾驶员的身体状况来提高道路安全性至关重要。我们的研究重点是一种无线智能传感器系统，该系统利用毫米波（mmWave）雷达监测驾驶员的心率变异性（HRV）。通过评估HRV，该系统可以检测出嗜睡和突发医疗紧急情况（如心脏病发作）的早期迹象，从而预防事故。这对于全自动驾驶（FSD）系统尤为重要，因为它可确保控制权不会转移给身体不适的驾驶员。所提出的系统采用一个置于驾驶员座椅后方的60GHz调频连续波（FMCW）雷达。本文主要描述如何应用包括Huber-Kalman滤波算法在内的先进信号处理方法来减轻呼吸对心率检测的影响。此外，自相关算法能够快速检测生命体征。大量实验证明了该系统在准确监测HRV方面的有效性，凸显了其在传统驾驶和自动驾驶环境中提高安全性和可靠性的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/000d/11351964/4db909280147/biomimetics-09-00481-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/000d/11351964/96cb068ddb54/biomimetics-09-00481-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/000d/11351964/b4a624c2af0e/biomimetics-09-00481-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/000d/11351964/c25526c64a8a/biomimetics-09-00481-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/000d/11351964/09c153696f8b/biomimetics-09-00481-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/000d/11351964/43e570a89c53/biomimetics-09-00481-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/000d/11351964/9da1068e0a16/biomimetics-09-00481-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/000d/11351964/75f8c75377eb/biomimetics-09-00481-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/000d/11351964/4db909280147/biomimetics-09-00481-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/000d/11351964/96cb068ddb54/biomimetics-09-00481-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/000d/11351964/b4a624c2af0e/biomimetics-09-00481-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/000d/11351964/c25526c64a8a/biomimetics-09-00481-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/000d/11351964/09c153696f8b/biomimetics-09-00481-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/000d/11351964/43e570a89c53/biomimetics-09-00481-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/000d/11351964/9da1068e0a16/biomimetics-09-00481-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/000d/11351964/75f8c75377eb/biomimetics-09-00481-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/000d/11351964/4db909280147/biomimetics-09-00481-g008.jpg

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本文引用的文献

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2
High-Precision Vital Signs Monitoring Method Using a FMCW Millimeter-Wave Sensor.利用 FMCW 毫米波传感器的高精度生命体征监测方法。
Sensors (Basel). 2022 Oct 5;22(19):7543. doi: 10.3390/s22197543.
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Heart Rate Variability-Based Driver Drowsiness Detection and Its Validation With EEG.基于心率变异性的驾驶员困倦检测及其 EEG 验证。
IEEE Trans Biomed Eng. 2019 Jun;66(6):1769-1778. doi: 10.1109/TBME.2018.2879346. Epub 2018 Nov 2.
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