Characterization and Identification of Distraction During Naturalistic Driving Using Wearable Non-Intrusive Physiological Measure of Galvanic Skin Responses

Fatalities due to road accidents are mainly caused by distracted driving. Driving demands continuous attention of the driver. Certain levels of distraction while driving can cause the driver to lose his/her attention which might lead to a fatal accident. Thus, early detection of distraction will help reduce the number of accidents. Several researches have been conducted for automatic detection of driver distraction. Many previous approaches have employed camera-based techniques. However, these methods might detect the distraction rather late to warn the drivers. Although neurophysiological signals using Electroencephalography (EEG) have shown to be another reliable indicator of distraction, EEG signals are very complex, and the technology is intrusive to the drivers, which creates serious doubt for its implementation. In this thesis we investigate a non-intrusive physiological measure-Galvanic Skin Responses (GSR) using a wrist band wearable and conduct an empirical characterization of driver GSR signals during a naturalistic driving experiment. The proposed method is used to evaluate and extract statistical, frequency and time domain features to identify distraction. Also, several data mining techniques such as feature selection, feature-ranking, dimensionality reduction and feature space analysis are performed to generate discriminative bases that reduce the computational complexity for efficient identification of distraction using supervised learning. A signal processing technique: continuous decomposition analysis, exclusive for skin conductance signal was investigated to better understand the behavior of raw signal during cognitive and visual over load from secondary tasks while driving. The proposed driver monitoring and identification system on the edge provided evident results using GSR as a reliable indicator of driver distraction while meeting the requirement of early notification of distraction state to driver.Master of ScienceComputer and Information Science, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/143521/1/Vikas Final Text Embedded.pdfDescription of Vikas Final Text Embedded.pdf : Thesi

Driving Fatigue Recognition with Functional Connectivity Based on Phase Synchronization

Accumulating evidences showed that the optimal brain network topology was altered with the progression of fatigue during car driving. However, the extent of discriminative power of functional connectivity that contribute to the driving fatigue detection is still unclear. In this study, we extracted two types of features (network properties and critical connections) to explore their usefulness in driving fatigue detection. EEG data were recorded twice from twenty healthy subjects during a simulated driving experiment. Multi-band functional connectivity matrices were established using phase lag index, which serve as input for the following graph theoretical analysis and critical connections determination between the most vigilant and fatigued states. We found a reorganisation of brain network towards less efficient architecture in fatigue state across all frequency bands. Further interrogations showed that the discriminative connections were mainly connected to frontal areas, i.e., most of the increased connections are from frontal pole to parietal or occipital regions. Moreover, we achieved a satisfactory classification accuracy (96.76%) using the discriminative connection features in β band. Our study demonstrated that graph theoretical properties and critical connections are of discriminative power for manifesting fatigue alterations and the critical connection is an efficient feature for driving fatigue detection

Intelligent Biosignal Analysis Methods

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others

Trends in Electrodermal Activity, Heart Rate and Temperature during Distracted Driving among Young Novice Drivers.

Driver distraction, defined as the scattering of attention from critical activities for safe driving, is among the key globally recognized contributing factors to road crashes. The trend keeps increasing with in-vehicle information systems and hand-held devices, leading to inattention. Of people in all age groups, young novice teenagers are prone to the risk of road crashes and are also more likely to exhibit risky and unsafe driving behavior. Data shows that the involvement of distracted drivers in fatal & injury collisions is higher for people aged between 16 -34, which is about 55%. Therefore, young drivers are of great concern for the research about driving and evaluation of safe driving conditions, which is vital in upcoming advancements in autonomous vehicles. Several research studies have explored the effects of distracted driving using face tracking and eye glance monitoring. Previous research [50] did not consider much about the effect of distraction on physiological factors and their impact during driving. The current study used data collected from a previous thesis work titled “Detection of Driver Cognitive Distraction Using Machine Learning Methods” by Apurva Misra and conducted new data analysis focusing on new research questions. The main objective of this thesis is to study, identify and discuss the effects on physiological factors like heart rate (HR), electrodermal activity (EDA), body temperature, and motion sickness during distracted driving among young drivers. The data was collected from a driving simulator study comprising 42 participants aged 16 – 23 under normal and distracted driving conditions. Their driving experience ranges from 0 to a maximum of 5 years. Each participant navigated six scenarios, three with distraction and the rest without distraction. Each scenario has a hidden, latent hazard depending on the surrounding; for example, in the work zone scenario, a worker is hidden behind the bulldozer in the work zone. The distraction task is a spoken task for which the driver has to respond verbally, which exerts a workload similar to that observed in conversations using a hands-free mobile phone. The physiological data collected through the Empatica4 wristband was analyzed and compared against age, gender, driver experience, and another parameter like motion sickness score (MSS) obtained from a questionnaire the participants completed after the experiment. Of the physiological factors stated above, it was found that HR and EDA play a significant role while studying distraction. Data analysis showed that HR and EDA increase more during distraction than baseline events. Nearly 80% of drivers with 0 or 1 year of experience tend to have a higher range of HR and EDA, which reveals that they are more distracted than their peers with more experience. From the results of the Load index questionnaire and Motion Sickness susceptibility questionnaire, it is inferred that when MSS increases, there is an increase in HR and EDA. These findings will provide insights into physiological factors for developing distraction mitigation systems or in-vehicle warning systems for distracted drivers

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

Multimodal approach for pilot mental state detection based on EEG

The safety of flight operations depends on the cognitive abilities of pilots. In recent years, there has been growing concern about potential accidents caused by the declining mental states of pilots. We have developed a novel multimodal approach for mental state detection in pilots using electroencephalography (EEG) signals. Our approach includes an advanced automated preprocessing pipeline to remove artefacts from the EEG data, a feature extraction method based on Riemannian geometry analysis of the cleaned EEG data, and a hybrid ensemble learning technique that combines the results of several machine learning classifiers. The proposed approach provides improved accuracy compared to existing methods, achieving an accuracy of 86% when tested on cleaned EEG data. The EEG dataset was collected from 18 pilots who participated in flight experiments and publicly released at NASA’s open portal. This study presents a reliable and efficient solution for detecting mental states in pilots and highlights the potential of EEG signals and ensemble learning algorithms in developing cognitive cockpit systems. The use of an automated preprocessing pipeline, feature extraction method based on Riemannian geometry analysis, and hybrid ensemble learning technique set this work apart from previous efforts in the field and demonstrates the innovative nature of the proposed approach