The public defence of Hamidur Rahman's licentiate thesis in Computer Science and Engineering
Doctoral thesis and Licentiate seminars
The public defence of Hamidur Rahman's licentiate thesis in Computer Science and Engineering will take place at Mälardalen University on June 19, 2018, at 13.15 in room Kappa, Västerås.
Title: “An Intelligent Non-Contact based Approach for Monitoring Driver’s Cognitive Load”.
Serial number: 268
The examining committee consists of Associate Professor Jerker Westin, Dalarna University; Professor Carolina Wählby, Uppsala University; Associate Professor Mikael Ekström, Mälardalen University. Among the members of the examining committee, Associate Professor Jerker Westin has been appointed the faculty examiner.
Reserve; Associate Professor Saad Mubeen, Mälardalen University.
The modern cars have been equipped with high technical features to make them faster, safer and comfortable. However, to enhance transport security i.e. to avoid unexpected traffic accidents it is necessary to consider a vehicle driver as a part of the environment and need to monitor driver’s health and mental state.
According to the literature, driving behavior-based and physiological parameters based approaches are commonly used to monitor driver’s health and mental state. In many of the previous work, physiological parameters-based approaches based on sensors are often attached to the human body. These sensors provide excellent signals in lab conditions but contaminated with artifacts and often troublesome and inconvenient in driving situations. Physiological parameters extraction based on video images offers a new paradigm for driver’s health and mental state monitoring.
This thesis report presents an intelligent non-contact based approach to monitor driver’s cognitive load based on physiological parameters and vehicular parameters. Here, camera sensor has been used as non-contact and pervasive methods for measuring physiological parameters. The contribution of this thesis is in three folds:
1) camera-based method is implemented to extract physiological parameters e.g., heart rate (HR), heart rate variability (HRV), inter-bit-interval (IBI), oxygen saturation (SpO2) and respiration rate (RR) considering illumination, motion, vibration and movement.
2) Vehicular parameters e.g. lateral speed, steering wheel angle, steering wheel reversal rate, steering wheel torque, yaw rate, lanex, and lateral position are extracted from simulator.
3) Three machine learning algorithms i.e. Logistic Regression (LR), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) are investigated to classify driver’s cognitive load.
According to the experimental work, considering the challenging conditions, the highest correlation coefficient achieved for both HR and SpO2 is 0.96. Again, the Bland Altman plots shows 95% agreement between camera and reference sensor. For the IBI, the quality index (QI) is achieved 97.5% considering 100 ms R-peak error. Finally, the achieved average accuracy for the classification of cognitive load is 91% for study1 and 83% for study2. In future, the proposed approach should be evaluated in real-road driving environment in other complex challenging situations such as high temperature, complete dark/bright environment, unusual movements, facial occlusion by hands, sunglasses, scarf, beard etc.