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

• Main Authors: Liu, Yu-Hang, 劉育航
• Other Authors: Lin, Chin-Teng
• Format: Others
• Language: en_US
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/09512488734913348083

碩士 === 國立交通大學 === 生醫工程研究所 === 99 === 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 monitoring. In the other hand, not only EEG but also EOG signals were acquired by our system to increase the accuracy of drowsiness detection. Furthermore, an algorithm of drowsiness detection was also proposed to reduce the computation complexity, and was implemented in a portable DSP module with bio-feedback as bio-stimulator or buzzer. In order to estimate the level of drowsiness, a lane-keeping driving experiment was designed and the drowsiness level of drivers was indirectly assessed by the reaction time under Virtual Reality Driving Simulation Environment. The advantage of this unsupervised algorithm can remove the differences between individual and environment in different people or measurements. For the purpose of verifying the accuracy and feasibility of our proposed unsupervised algorithm, drowsiness status estimated by driving performance was compared with the results obtained by our proposed unsupervised algorithm. The results of comparison showed that our algorithm can detect driver’s drowsiness status precisely. In addition, our system can be successfully applied in practice to prevent traffic accidents caused by drowsy driving.