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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.

A conceptual analysis of cognitive distraction for transit bus drivers

D'Souza K. A., Siegfeldt D. V., Hollinshead A.

Management and Production Engineering Review

2013

Vol. 4, No. 1

10--19

The purpose of this paper is to analyze cognitive distraction data to determine its impact of transit bus drivers' capability. Much of the theory and results applied in this paper are from the work of researchers working on similar projects. In order to understand cognitive distraction and how it can be mitigated, a Cognitive Distraction Model is outlined. The model was analyzed to evaluate the correlation between driver capability, and demographics and driving patterns. A model that provides an understanding about cognitive workload and driver capability could provide better psychological solutions to mitigate the number of accidents due to cognitive distraction and develop relevant driver training programs. Through additional research from the neurological and behavioral sciences, regulators could develop a better understanding of the causal factors and ways to control cognitive distraction.