基于逻辑回归的机器学习算法的驾驶员困倦实时检测

2016 IEEE Green Energy and Systems Conference (IGSEC) Pub Date : 2016-11-01 DOI:10.1109/IGESC.2016.7790075

Mohsen Babaeian, N. Bhardwaj, Bianca Esquivel, M. Mozumdar

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引用次数: 22

摘要

由于司机困倦造成的车祸数量非常多。一个自动的非接触式系统可以早期检测到司机的困倦，这可能会挽救生命。基于这一迫切需求，我们提出了一种新颖的方法，通过使用先进的基于逻辑回归的机器学习算法计算心率变化，可以在早期检测驾驶员的睡意。我们开发的技术已经在人类受试者身上进行了测试，它可以在最短的时间内检测到困倦，准确率超过90%。

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Real time driver drowsiness detection using a logistic-regression-based machine learning algorithm

The number of car accidents due to driver drowsiness is very steep. An automated non-contact system that can detect driver's drowsiness early could be lifesaving. Motivated by this dire need, we propose a novel method that can detect driver's drowsiness at an early stage by computing heart rate variation using advanced logistic regression based machine learning algorithm. Our developed technique has been tested with human subjects and it can detect drowsiness in a minimum amount of time, with an accuracy above 90%.

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来源期刊

2016 IEEE Green Energy and Systems Conference (IGSEC)

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