Real-time Wireless Brain Computer Interface for Drowsiness Detection
|關鍵字:||瞌睡監控;腦波圖;無線可攜式;腦波擷取系統;數位訊號處理平台;虛擬實境模擬環境;開車偏移量;非監督式分析法;drowsiness detection;electroencephalogram;portable EEG acquisition module;DSP module;Virtual Reality Driving Simulation Environment;driving performance;unsupervised algorithm|
In recent years, traffic accident is one of the critical reasons to cause deaths of drivers. Here, 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. Therefore, if the mental state of drivers can be real-time monitored directly, drowsiness detection and warning can 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 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 light. And others used physiological parameters to increase the accuracy of drowsy detection, like pulse wave analysis with neural network, the electrooculogram (EOG) and the electromyography (EMG) measurement, and the electroencephalogram (EEG). In this study, we proposed a real-time wireless brain computer interface for drowsiness detection. Here, a small, light, and portable EEG acquisition module was designed for long-time EEG monitoring. And a novel algorithm of drowsiness detection based on was also proposed to reduce the computation complexity, and was implemented in a portable DSP module. In order to estimate the level of drowsiness, a lane-keeping driving experiment was designed. The drowsiness level of drivers was indirectly assessed by the reaction time and driving trajectory 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. In order to verify the accurate and feasibility of our proposed unsupervised algorithm, we compared drowsiness status estimated by driving performance with that obtained by our proposed unsupervised algorithm. The results showed that our proposed algorithm can detect driver’s drowsiness status. Finally, our system can successfully be applied in practice to prevent traffic accidents caused by drowsy driving.
|Appears in Collections:||Thesis|
Files in This Item: