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A physiological measures-based method for detecting inattention in drivers using machine learning approach

Sahayadhas A., Sundaraj K., Murugappan M., Palaniappan R.

Biocybernetics and Biomedical Engineering

2015

Vol. 35, no. 3

198--205

Background: In recent years, as a result of the usage of electronic gadgets in vehicles, driver inattention has become one of the major causes of road accidents that lead to severe physical injuries, deaths and significant economic losses. Statistics ensure the need of a reliable driver inattention detection system that can alert the driver before a mishap happens. Methods: In this work, we aimed to develop a system that can detect inattention using electrocardiogram (ECG) and surface electromyogram (sEMG) signals. Cognitive and visual inattention was manipulated by asking the driver to respond to phone calls and short messaging services, respectively. A total of 15 male subjects participated in the data collection process. The subjects were asked to drive for two hours in a simulated environment at three different times of the day. ECG, sEMG and video were obtained throughout the experiment. The gathered physiological signals were preprocessed to remove noises and artefacts. The inattention features were extracted from the preprocessed signals using conventional statistical, higher-order statistical and higher-order spectral features. The features were classified using k-nearest neighbour analysis, linear discriminant analysis and quadratic discriminant analysis. Results: The bispectral features gave overall maximum accuracies of 98.12% and 90.97% for the ECG and EMG signals, respectively. Conclusion: We conclude that ECG and EMG signals can be explored further to develop a robust and reliable inattention detection system.

Classification of Driver Drowsiness Level using Wireless EEG

Wali M. K., Murugappan M., Badlishah-Ahmad R.

Przegląd Elektrotechniczny

2013

R. 89, nr 6

113--117

In this work, wireless Electroencephalogram (EEG) signals are used to classify the driver drowsiness levels (neutral, drowsy, high drowsy and sleep stage1) based on Discrete Wavelet Packet Transform (DWPT). Two statistical features (spectral centroid, and power spectral density) were extracted from four EEG frequency bands (delta, theta, alpha, and beta) using Fast Fourier Transform (FFT). These features are used to classify the driver drowsiness level using three classifiers namely, subtractive fuzzy clustering, probabilistic neural network, and K nearest neighbour. Results of this study indicates that the best average accuracy of 84.41% is achieved using subtractive fuzzy classifier based on power spectral density feature extracted by db4 wavelet function.

W artykule zaprezentowano możliwość wykorzystania dyskretnej transformaty falkowej do analizy sygnału elektroencefalografii w badaniach senności kierowcy. Parametry statystyczne sygnału analizowano z wykorzystaniem dyskretnej transformaty Fouriera. Stwierdzono że najlepsza dokładność uzyskuje się stosując klasyfikator rozmyty i funkcję falkową db4.