• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

中距离行程中驾驶员疲劳和困倦的道路检测

On-Road Detection of Driver Fatigue and Drowsiness during Medium-Distance Journeys.

作者信息

Salvati Luca, d'Amore Matteo, Fiorentino Anita, Pellegrino Arcangelo, Sena Pasquale, Villecco Francesco

机构信息

Department of Industrial Engineering, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano, Italy.

Department of Pharmacy, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano, Italy.

出版信息

Entropy (Basel). 2021 Jan 21;23(2):135. doi: 10.3390/e23020135.

DOI:10.3390/e23020135
PMID:33494447
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7912473/
Abstract

The detection of driver fatigue as a cause of sleepiness is a key technology capable of preventing fatal accidents. This research uses a fatigue-related sleepiness detection algorithm based on the analysis of the pulse rate variability generated by the heartbeat and validates the proposed method by comparing it with an objective indicator of sleepiness (PERCLOS). : changes in alert conditions affect the autonomic nervous system (ANS) and therefore heart rate variability (HRV), modulated in the form of a wave and monitored to detect long-term changes in the driver's condition using real-time control. : the performance of the algorithm was evaluated through an experiment carried out in a road vehicle. In this experiment, data was recorded by three participants during different driving sessions and their conditions of fatigue and sleepiness were documented on both a subjective and objective basis. The validation of the results through PERCLOS showed a 63% adherence to the experimental findings. : the present study confirms the possibility of continuously monitoring the driver's status through the detection of the activation/deactivation states of the ANS based on HRV. The proposed method can help prevent accidents caused by drowsiness while driving.

摘要

检测驾驶员疲劳作为嗜睡的一个原因是一项能够预防致命事故的关键技术。本研究使用一种基于对心跳产生的脉搏率变异性进行分析的与疲劳相关的嗜睡检测算法，并通过将其与嗜睡的客观指标（闭眼时间百分比）进行比较来验证所提出的方法。：警觉状态的变化会影响自主神经系统（ANS），进而影响心率变异性（HRV），HRV以波动形式被调制，并通过实时控制进行监测以检测驾驶员状态的长期变化。：通过在道路车辆上进行的实验对该算法的性能进行了评估。在该实验中，三名参与者在不同的驾驶时段记录数据，并在主观和客观基础上记录他们的疲劳和嗜睡状况。通过闭眼时间百分比对结果进行验证，结果显示与实验结果的符合率为63%。：本研究证实了基于心率变异性通过检测自主神经系统的激活/失活状态来持续监测驾驶员状态的可能性。所提出的方法有助于预防驾驶时因困倦导致的事故。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2088/7912473/0219ef972842/entropy-23-00135-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2088/7912473/dd3783133e02/entropy-23-00135-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2088/7912473/08577aefe2cc/entropy-23-00135-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2088/7912473/086d1e75486c/entropy-23-00135-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2088/7912473/d4bba36a0b80/entropy-23-00135-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2088/7912473/f6612ffc8255/entropy-23-00135-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2088/7912473/b0fa913591d6/entropy-23-00135-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2088/7912473/0219ef972842/entropy-23-00135-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2088/7912473/dd3783133e02/entropy-23-00135-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2088/7912473/08577aefe2cc/entropy-23-00135-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2088/7912473/086d1e75486c/entropy-23-00135-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2088/7912473/d4bba36a0b80/entropy-23-00135-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2088/7912473/f6612ffc8255/entropy-23-00135-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2088/7912473/b0fa913591d6/entropy-23-00135-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2088/7912473/0219ef972842/entropy-23-00135-g007.jpg

相似文献

1
On-Road Detection of Driver Fatigue and Drowsiness during Medium-Distance Journeys.中距离行程中驾驶员疲劳和困倦的道路检测
Entropy (Basel). 2021 Jan 21;23(2):135. doi: 10.3390/e23020135.
2
Driver drowsiness estimation using EEG signals with a dynamical encoder-decoder modeling framework.基于动态编解码器建模框架的 EEG 信号驾驶员困倦估计。
Sci Rep. 2022 Feb 16;12(1):2650. doi: 10.1038/s41598-022-05810-x.
3
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.
4
Deriving heart rate variability indices from cardiac monitoring-An indicator of driver sleepiness.从心脏监测中提取心率变异性指标——驾驶员困倦的指标。
Traffic Inj Prev. 2019;20(3):249-254. doi: 10.1080/15389588.2018.1548766. Epub 2019 Apr 12.
5
Detecting driver fatigue using heart rate variability: A systematic review.使用心率变异性检测驾驶员疲劳：系统评价。
Accid Anal Prev. 2022 Dec;178:106830. doi: 10.1016/j.aap.2022.106830. Epub 2022 Sep 22.
6
The effects of physical vibration on heart rate variability as a measure of drowsiness.身体振动对作为嗜睡指标的心率变异性的影响。
Ergonomics. 2018 Sep;61(9):1259-1272. doi: 10.1080/00140139.2018.1482373. Epub 2018 Jul 17.
7
Optical flow and driver's kinematics analysis for state of alert sensing.用于警觉状态感测的光流和驾驶员运动学分析。
Sensors (Basel). 2013 Mar 28;13(4):4225-57. doi: 10.3390/s130404225.
8
Multi-Index Driver Drowsiness Detection Method Based on Driver's Facial Recognition Using Haar Features and Histograms of Oriented Gradients.基于 Haar 特征和方向梯度直方图的驾驶员人脸识别的多指标驾驶员瞌睡检测方法。
Sensors (Basel). 2024 Aug 31;24(17):5683. doi: 10.3390/s24175683.
9
Heart Rate Variability for Driver Sleepiness Classification in Real Road Driving Conditions.真实道路驾驶条件下用于驾驶员困倦分类的心率变异性
Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:6537-6540. doi: 10.1109/EMBC.2019.8857229.
10
Effects of a Motion Seat System on Driver's Passive Task-Related Fatigue: An On-Road Driving Study.运动座椅系统对驾驶员被动任务相关疲劳的影响：一项道路驾驶研究。
Sensors (Basel). 2020 May 8;20(9):2688. doi: 10.3390/s20092688.

引用本文的文献

1
Exploiting heart rate variability for driver drowsiness detection using wearable sensors and machine learning.利用可穿戴传感器和机器学习技术，通过心率变异性检测驾驶员疲劳状态。
Sci Rep. 2025 Jul 10;15(1):24898. doi: 10.1038/s41598-025-08582-2.
2
Driver Fatigue Detection Using Heart Rate Variability Features from 2-Minute Electrocardiogram Signals While Accounting for Sex Differences.使用 2 分钟心电图信号的心率变异性特征检测驾驶员疲劳，同时考虑性别差异。
Sensors (Basel). 2024 Jul 3;24(13):4316. doi: 10.3390/s24134316.
3
PERCLOS-based technologies for detecting drowsiness: current evidence and future directions.

本文引用的文献

1
Noise Reduction in Spur Gear Systems.圆柱齿轮系统中的降噪
Entropy (Basel). 2020 Nov 16;22(11):1306. doi: 10.3390/e22111306.
2
Modeling of Eddy Current Welding of Rail: Three-Dimensional Simulation.钢轨涡流焊接建模：三维模拟
Entropy (Basel). 2020 Aug 28;22(9):947. doi: 10.3390/e22090947.
3
Unconstrained Vital Sign Monitoring System Using an Aortic Pulse Wave Sensor.基于主动脉脉搏波传感器的非约束生命体征监测系统。
基于PERCLOS的嗜睡检测技术：当前证据与未来方向。
Sleep Adv. 2023 Jan 24;4(1):zpad006. doi: 10.1093/sleepadvances/zpad006. eCollection 2023.
4
A Recognition Method for Road Hypnosis Based on Physiological Characteristics.基于生理特征的道路催眠识别方法。
Sensors (Basel). 2023 Mar 23;23(7):3404. doi: 10.3390/s23073404.
5
Research on Recognition of Road Hypnosis in the Typical Monotonous Scene.典型单调场景下道路催眠识别研究。
Sensors (Basel). 2023 Feb 3;23(3):1701. doi: 10.3390/s23031701.
6
A real-time driver fatigue identification method based on GA-GRNN.基于 GA-GRNN 的实时驾驶员疲劳识别方法。
Front Public Health. 2022 Oct 20;10:991350. doi: 10.3389/fpubh.2022.991350. eCollection 2022.
7
Investigation of Impact of Walking Speed on Forces Acting on a Foot-Ground Unit.行走速度对足-地单位受力影响的研究。
Sensors (Basel). 2022 Apr 18;22(8):3098. doi: 10.3390/s22083098.
8
Optimization of Three-Phase Hybrid Stepper Motors for Noise Reduction.用于降噪的三相混合式步进电机的优化
Sensors (Basel). 2022 Jan 4;22(1):356. doi: 10.3390/s22010356.
9
Advanced e-Call Support Based on Non-Intrusive Driver Condition Monitoring for Connected and Autonomous Vehicles.基于非侵入式驾驶员状态监测的联网自动驾驶汽车高级电子路侧求助系统。
Sensors (Basel). 2021 Dec 10;21(24):8272. doi: 10.3390/s21248272.
10
Human Behavior Recognition Model Based on Feature and Classifier Selection.基于特征和分类器选择的人类行为识别模型。
Sensors (Basel). 2021 Nov 23;21(23):7791. doi: 10.3390/s21237791.
Sci Rep. 2019 Nov 25;9(1):17475. doi: 10.1038/s41598-019-53808-9.
4
Unconstrained Monitoring of Biological Signals Using an Aortic Pulse Wave Sensor.使用主动脉脉搏波传感器对生物信号进行无约束监测。
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:4327-4330. doi: 10.1109/EMBC.2018.8513291.
5
Study of the Grape Cryo-Maceration Process at Different Temperatures.不同温度下葡萄冷浸渍过程的研究
Foods. 2018 Jul 6;7(7):107. doi: 10.3390/foods7070107.
6
A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness.一种用于早期检测驾驶员困倦的情境感知脑电图耳机系统。
Sensors (Basel). 2015 Aug 21;15(8):20873-93. doi: 10.3390/s150820873.
7
Fusion of optimized indicators from Advanced Driver Assistance Systems (ADAS) for driver drowsiness detection.用于驾驶员困倦检测的先进驾驶辅助系统（ADAS）优化指标融合
Sensors (Basel). 2014 Jan 9;14(1):1106-31. doi: 10.3390/s140101106.
8
Detection of driver drowsiness using wavelet analysis of heart rate variability and a support vector machine classifier.利用心率变异性的小波分析和支持向量机分类器检测驾驶员困倦。
Sensors (Basel). 2013 Dec 2;13(12):16494-511. doi: 10.3390/s131216494.
9
Heart rate variability in normal and pathological sleep.正常睡眠和病理睡眠中的心率变异性
Front Physiol. 2013 Oct 16;4:294. doi: 10.3389/fphys.2013.00294.
10
Automatic detection of drowsiness in EEG records based on multimodal analysis.基于多模态分析的脑电图记录中瞌睡的自动检测。
Med Eng Phys. 2014 Feb;36(2):244-9. doi: 10.1016/j.medengphy.2013.07.011. Epub 2013 Aug 20.