Drowsy driving has become a serious concern over the last few decades. The rise in the number of automobiles as well as the stress and fatigue induced due to lifestyle factors have been major contributors to this problem. Accidents due to drowsy driving have caused innumerable deaths and losses to the state. Therefore, detecting drowsiness accurately and within a short period of time before it impairs the driver has become a major challenge. Previous researchers have found that the Electrocardiogram (ECG/EKG) is an important parameter to detect drowsiness. Incorporating machine learning (ML) algorithms like Logistic Regression (LR) can help in detecting drowsiness accurately to some extent. Accuracy in LR can be increased with a larger data set and more features for a robust machine learning model. However, having a larger dataset and more features increases detection time, which can be fatal if the driver is drowsy. Reducing the dataset size for faster detection causes the problem of overfitting, in which the model performs well with training data than test data.
In this thesis, we increased the accuracy, reduced detection time, and solved the problem of overfitting using a machine learning model based on Ensemble Logistic Regression (ELR). The ECG signal after filtering was first converted from the time domain to the frequency domain using Wavelet Transform (WT) instead of the traditional Short Term Fourier Transform (STFT). Frequency features were then extracted and an ensemble based logistic regression model was trained to detect drowsiness. The model was then tested on twenty-five male and female subjects who varied between 20 and 60 years of age. The results were compared with traditional methods for accuracy and detection time.
The model outputs the probability of drowsiness. Its accuracy is between 90% and 95% within a detection time of 20 to 30 seconds. A successful implementation of the above system can significantly reduce road accidents due to drowsy driving.
|Commitee:||Ahmed, Aftab, Kwon, Seok-Chul (Sean)|
|School:||California State University, Long Beach|
|School Location:||United States -- California|
|Source:||MAI 57/01M(E), Masters Abstracts International|
|Subjects:||Electrical engineering, Computer science|
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