Real-time Wireless System based on Multiple Bio-signal Parameters for Drowsiness Detection

In recent years, traffic accident is one of the critical reasons to cause deaths of drivers. Drivers’ drowsiness has been implicated as a causal factor in many accidents because of the marked decline in drivers’ perception of risk and recognition of danger, and diminished vehicle handling abilities. Consequently, if the mental state of drivers could be real-time monitored, drowsiness detection and warning could effectively avoid disasters such as vehicle crashes in working environments. Some previous researches used non-physiological method, as eye closure with CCD image tracking, such as the pupil recognition, blink detection or identification of the drivers head shaking frequency. However, for CCD image tracking, users couldn’t move for free, and the images detecting performance were easily be interfered by external flash light. Other studies used physiological parameters to increase the accuracy of drowsy detection, like pulse wave analysis with neural network, electrooculography (EOG), electromyography (EMG), and electroencephalogram (EEG) measurement. In this study, we proposed a real-time wireless system for drowsiness detection. A wearable, wireless and real-time bio-signal acquisition system was designed for long-term