Estimation of drivers´ drowsiness level using a Neural Network Based ‘Error Correcting Output Coding’ method

This paper presents an improved method for estimating a driver´s drowsiness level using eyelid closure and heart rate variability. Laboratory experiments were conducted by using a proprietary driving simulator, which induced drowsiness among the test drivers. The purposes of these experiments were to obtain an electrocardiogram (ECG) and eye-blink video sequences. The drivers were also monitored through a video camera. The changes in facial expression of the drivers were used as a standard index of drowsiness level. Error-Correcting Output Coding (ECOC) was employed as a multi-class classifier to estimate drowsiness level. The ordinary ECOC method was extended using a loss function for the decoding procedure to obtain class tendencies of each drowsiness level. We used a Neural Network-Based Decoding method to classify the drowsiness level. As a result, we obtained an extraordinarily high accuracy for estimation of drowsiness level. Comparisons were also made with the result of those from using the Loss-based Decoding ECOC (LD-ECOC).