Physiological signal-based detection of driver hypovigilance

2^nd International Conference on Robotics and Artificial Intelligence

May 23-24, 2019 | Vienna, Austria

Arun Sahayadhas

Vels Institute of Science, Technology and Advanced Studies (VISTAS), India

Posters & Accepted Abstracts: JET

Abstract

Driver hypovigilance which includes drowsiness, inattention and fatigue are the major reason for road accidents. To detect the driver hypovigilance, the physiological signals needs to be collected and analyzed. In case of hypovigilance, the driver has to be alerted on time so that loss can be avoided. The physiological signals are the graphical representation of human physical condition. Electrocardiogram (ECG), Electrooculogram (EOG) and Electromyogram (EMG) are some of the signals that are used here to provide the state of driver’s abnormal behaviour. Ten subjects participated in the data collection experiment and were asked to drive for two hours at three different timings of the day (00:00 – 02:00 hrs, 03:00 – 05:00 hrs and 14:00 – 16:00 hrs) when their circadian rhythm was low. The five classes namely – normal, visual inattention, cognitive inattention, fatigue and drowsy were analyzed. The Butterworth 6th order filter is applied to remove the noise from the signals. The features that are extracted from the signals can be linear and non-linear. Sixteen Linear features such as mean, median, minimum, maximum, standard deviation, power, skewness, kurtosis, Energy, correlation coefficient, central frequency, peak frequency, first quartile frequency, third quartile frequency, Interquartile Range and Root Mean Square were extracted. Likewise, eight Non-linear features such as Spatial filling index (SFI), Central tendency measure (CTM), Correlation dimension, Approximate Entropy (ApEn), HURST exponent, Largest Lyapunov exponent, Nonlinear Predication error (NLPE) and stoppage criteria were extracted. These extracted features were given as input to the different classifiers (Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Convolutional Neural Networks (CNN)) to obtain the accuracy, sensitivity and scalability. The results show that the features from ECG can be embedded in a smart watch which can alert the driver during hypovigilance.

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