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Arduino based Real Time Drowsiness and Fatigue Detection for Bikers using Helmet

机译:使用头盔的基于Arduino的骑行者实时睡意和疲劳检测

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Vehicle accidents are rapidly increasing in many countries. Among many other factors, drowsiness and fatigue are playing a major role in these accidents and systems which can monitor it are currently being developed. Among them, Electroencephalography (EEG) proved to be very reliable. The conventional vehicle and the vision based detection for drowsiness is very much essential only when the driver is about to sleep and every so often very late in preventing fatalities on road. This paper is specially developed to improve the safety of the bikers. The proposed system has EEG-sensors which are implemented within the helmet to detect the drowsy state of the driver. The biomedical signal from the driver's brain is sensed by a Brain-wave sensor. This system provides real-time drowsiness and fatigue detection for the bikers by making a helmet to play a vital part with warning platform as a miniaturized sensor and to provide mind machine interface (MMI) to address the challenges like drowsiness and fatigue. When the biker is detected to be in drowse state the system alerts the biker by an alarm and motor gets slow down and stopped.
机译:在许多国家,交通事故正在迅速增加。除其他因素外,睡意和疲劳在这些事故中也起着主要作用,目前正在开发可对其进行监视的系统。其中,脑电图(EEG)被证明是非常可靠的。仅当驾驶员即将入睡且通常在预防道路上的死亡事故时很晚时,常规车辆和基于视觉的睡意检测才非常重要。本文是专门为提高骑车人的安全性而开发的。所提出的系统具有EEG传感器,该传感器安装在头盔内以检测驾驶员的困倦状态。来自驾驶员大脑的生物医学信号由脑波传感器感测。该系统通过使头盔在带有微型传感器的预警平台上发挥重要作用,并提供心智机接口(MMI)来解决嗜睡和疲劳等挑战,从而为骑车人提供实时的嗜睡和疲劳检测。当检测到骑车人处于沉睡状态时,系统会通过警报向骑车人发出警报,并且电动机会减速并停止运转。

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