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Applying neural network analysis on heart rate variability data to assess driver fatigue

机译:将神经网络分析应用于心率变异性数据以评估驾驶员疲劳

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摘要

Long duration driving is a significant cause of fatigue related accidents on motorways. Fatigue caused by driving for extended hours can acutely impair driver's alertness and performance. This papers presents an artificial intelligence based system which could detect early onset of fatigue in drivers using heart rate variability (HRV) as the human physiological measure. The detection performance of neural network was tested using a set of electrocardiogram (ECG) data recorded under laboratory conditions. The neural network gave an accuracy of 90%. This HRV based fatigue detection technique can be used as a fatigue countermeasure.
机译:长时间驾驶是导致高速公路疲劳相关事故的重要原因。长时间驾驶造成的疲劳会严重损害驾驶员的机敏性和性能。本文提出了一种基于人工智能的系统,该系统可以使用心率变异性(HRV)作为人体生理指标来检测驾驶员疲劳的早期发作。使用在实验室条件下记录的一组心电图(ECG)数据测试了神经网络的检测性能。神经网络的准确性为90%。这种基于HRV的疲劳检测技术可以用作疲劳对策。

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