| 凤鹏飞,薛培友,卢明宇.基于脑电信号的不同年龄段驾驶员疲劳水平评估[J].唐山学院学报,2023,36(6):17-21,37 |
| 基于脑电信号的不同年龄段驾驶员疲劳水平评估 |
| Assessment of Fatigue Level for Drivers of Different Age Groups Based on Electroencephalogram Signals |
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| DOI：10.16160/j.cnki.tsxyxb.2023.06.004 |
| 中文关键词: 交通安全 脑电信号 疲劳驾驶 驾驶员年龄 |
| 英文关键词: traffic safety electroencephalogram signals(EEG) fatigue driving driver's age |
| 基金项目:安徽省高等学校自然科学研究项目（KJ2020A0797）；安徽省普通高校交通信息与安全重点实验室项目（JTX202302） |
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| 中文摘要: |
| 为研究不同年龄段驾驶员的疲劳累积情况，对比其疲劳驾驶的差异性，获取最优驾驶时间，文章通过eegoTMmylab全移动脑电记录分析系统采集脑电数据，结合主观调查法对被试者疲劳状态进行调查。采用ASA软件对原始数据进行数据预处理，并通过积分法获得不同时段的α波、β波、θ波的平均功率谱密度，计算出脑电指标Rα/β，Rθ/β及R（α+θ）/β，通过SPSS对处理后数据进行分析；以R（α+θ）/β作为驾驶疲劳指标，分别求出不同年龄段驾驶员的R（α+θ）/β，并将其与驾驶时间进行拟合，分析不同年龄段驾驶员与驾驶疲劳累积速度之间的关系。结果表明：在2 h内青年及中年驾驶员疲劳累积速度较慢，老年驾驶员疲劳累积速度较快，青年、中年、老年驾驶员最优驾驶时间分别为105~120 min，105~120 min及75~90 min。 |
| 英文摘要: |
| In order to study the fatigue accumulation for drivers of different age groups, to compare the differences of their fatigue driving, and to obtain the optimal driving time, this paper has collected electroencephalogram data through the eegoTM mylab full mobile brainwave recording and analysis system, and combines subjective survey methods to investigate the fatigue state of subjects. The ASA software is used to preprocess the original data, and the integration method is used to obtain the average power spectral density of α, β and θ waves, and to calculate the brainwave indexes Rα/β, Rθ/β And R(α+θ)/β. Then the processed data are analyzed through SPSS; R(α+θ)/β is used as an indicator of fatigue driving. The R(α+θ)/β of drivers of different age groups are obtained and they are fitted with driving time to analyze the relationship between drivers of different age groups and the fatigue accumulation rate. The results show that within 2 hours, the fatigue accumulation rate of young and middle-aged drivers is slower, while that of elderly drivers is faster. The optimal driving time for young, middle-aged, and elderly drivers are 105~120 min, 105~120 min, and 75~90 min respectively. |
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