Drowsiness detection is critical to driver safety, considering thousands of deaths caused by drowsy driving annually. Professional equipment is capable of providing high detection accuracy, but the high cost limits their applications in practice. The use of mobile devices such as smart watches and smart phones holds the promise of providing a more convenient, practical, non-invasive method for drowsiness detection. In this article, we propose a real-time driver drowsiness detection system based on mobile devices, referred to as dWatch, which combines physiological measurements with motion states of a driver to achieve high detection accuracy and low power consumption. Specifically, based on heart rate measurements, we design different methods for calculating heart rate variability (HRV) and sensing yawn actions, respectively, which are combined with steering wheel motion features extracted from motion sensors for drowsiness detection. We also design a driving posture detection algorithm to control the operation of the heart rate sensor to reduce system power consumption. Extensive experimental results show that the proposed system achieves a detection accuracy up to 97.1% and reduces energy consumption by 33%.

中文翻译：

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dWatch

考虑到每年因困倦驾驶导致数千人死亡，困倦检测对驾驶员安全至关重要。专业设备能够提供较高的检测精度，但高昂的成本限制了其在实践中的应用。智能手表和智能手机等移动设备的使用有望提供一种更方便、实用、无创的睡意检测方法。在本文中，我们提出了一种基于移动设备的实时驾驶员睡意检测系统，称为 dWatch，它将生理测量与驾驶员的运动状态相结合，以实现高检测精度和低功耗。具体来说，基于心率测量，我们设计了不同的方法来分别计算心率变异性 (HRV) 和感知打哈欠动作，结合从运动传感器中提取的方向盘运动特征，用于睡意检测。我们还设计了一种驾驶姿势检测算法来控制心率传感器的运行，从而降低系统功耗。大量实验结果表明，该系统的检测准确率高达 97.1%，能耗降低 33%。