A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability

Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t-tests to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system’s performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear

Brain Behavior in Learning and Memory Recall Process: A High-Resolution EEG Analysis

Learning is a cognitive process, which leads to create new memory. Today, multimedia contents are common-ly used in classroom for learning. This study investigated brain physiological behavior during learning and memory process using multimedia contents and Electroencephalogram (EEG) method. Fifteen healthy subjects voluntarily participated and performed three experimental tasks: i) Intelligence task, ii) learning task, and iii) recall task. EEG was recorded duration learning and memory recall task using 128 channels Hydro Cel Geodesic Net system (EGI Inc., USA) with recommended specifications. EEG source localization showed that deep brain medial temporal region was highly activated during learning task. EEG theta band in frontal and parietal regions and gamma band at left posterior temporal and frontal regions differentiated successful memory recall. This study provides additional understanding of successful memory recall that complements earlier brain mapping studies

Determining Intermediary Closely Related Languages to Find a Mediator for Intertribal Conflict Resolution

Indonesia has a diverse ethnic and cultural background. However, this diversity sometimes creates social problems, such as intertribal conflict. Because of the large differences among tribal languages, it is often difficult for conflicting parties to dialog for conflict resolution. To address this problem, we aim to find intermediary closely related languages from a language similarity knowledge graph using the best-performing pathfinding algorithms. In this research, we analyze the performances of two pathfinding algorithms, namely, Dijkstra and Yen’s K, by comparing their execution time and the total lexical distances of the intermediary languages (called “the cost”). Our research findings show that even though the Dijkstra and Yen’s K algorithms have equal total cost for all the cases, Yen’s K outperformed Dijkstra at searching for intermediary languages that are closely related, with an average of 160% higher performance on execution time. The selection of native speakers of the obtained intermediary languages as mediators is formalized as an optimization problem with four criteria: language similarity, geographical distance, background, and expected salary. We present a case study where the intermediary closely related languages can be used as a guideline to find mediators who can help resolve the intertribal conflicts among Indonesian tribes. To calculate the first criteria, we implemented the Yen’s K algorithm to calculate the shortest path between target languages and return the path via the intermediary languages. This implementation shows the potential use of the mediator selection model defined in this paper in various other roles such as trader or salesman, politician’s spokesman, reporter or journalist, etc

Optimal power allocation in interference-limited communication networks

© 2010 Dr. Nasreen BadruddinCommunication networks such as wireless networks and Digital Subscriber Line (DSL) systems are plagued by the effects of interference, which degrades the signal to interference and noise ratio (SINR) at the receiver. Increasing the transmit power of one link may boost the SINR to its intended receiver at the expense of causing more interference in other links in the network. Therefore, power control is a balancing act between getting the most out of the individual link rates without degrading the performance of other links in the network. In this thesis, we tackle the problem of finding the optimal power scheme which maximises the overall network sumrate of different models of the interference network. The sumrate function is well-known to be non-concave in general, and so convex optimisation techniques may not be applicable in finding the optimal power solution. We present solutions to the power optimisation problem for various models of the interference network by treating interference as worst-case Gaussian noise. A recurring result with the networks investigated is the optimality of binary power control, where the power policy is simply to either switch them off or on. We also discovered a sufficient condition where binary power control is optimal for sumrate maximisation in a network treating interference as noise. Apart from the actual characterisation of the optimal power solution for these interference networks, our contribution is the various techniques used in arriving at the solutions. These include a method of grouping and performing piecewise comparison of the power vectors, the use of majorisation and Schur-concavity/convexity and dynamic programming. Our results give potential insights on solving other, more complex network models

Maximising sum rate for two interfering wireless links

In this paper, we consider the problem of maximising the sum rate of two interfering links in Gaussian noise. We assume no fading and that interference is treated as worst-case Gaussian noise, and that the optimal power allocation must be time-invariant. We show that either both links must operate at maximum power, or one link operates at maximum power and the other link is switched off. The switching point between one power scheme to another depends on the value of ± which is the SNR of the link, as well as the channel gains at all links.7 page(s

Driver drowsiness detection using EEG power spectrum analysis

Driver drowsiness is considered to be a very critical issue causing many fatal accidents, injuries and property damages. Therefore, it has been an area of intensive research in recent years. In this paper, a driving simulator based study was conducted to observe the significant changes that occur in the EEG power spectrum during monotonous driving. Nine healthy university students voluntarily participated in the experiment. The absolute band power of the EEG signal was computed by taking the FFT of the time series signal and then the power spectral density was computed using Welch method. Our findings conclude that alpha and theta band powers increase significantly (p<;0.05) when a subject moves from alert state to drowsy state. These changes are more dominant in the occipital and parietal regions when compared to the other regions. The findings of this study provide a promising drowsiness indicator which can be used to prevent road accidents caused by driver drowsiness

EEG Brain Connectivity Analysis to Detect Driver Drowsiness Using Coherence

Drowsiness at the wheel is one of the major contributing factors towards road accidents. Therefore, efforts have been made to detect driver drowsiness using electroencephalogram (EEG). The use of EEG as a possible driver drowsiness indicator is commonly accepted. However, in this paper, we have studied brain connectivity measure instead of the traditional spectral power measures. For this purpose, the EEG coherence analysis is performed to examine the functional connectivity between various brain regions during the transitional phase, i.e., from alert state to drowsy state. Data collection is performed in a simulator based environment. Twenty-two healthy subjects voluntarily participated in the study after providing their consent. All possible combinations of inter- and intra-hemispheric coherences are analyzed. Because of the unavailability of common gold standard, video recordings are captured during the experiment to mark the drowsy state. To verify the statistical significance of the proposed features, paired t-test is performed. The analysis revealed significant differences (p0.05) in inter- and intra-hemispheric coherences (brain connectivity analysis) between alert and drowsy state, which shows the potential of coherence analysis in detection drowsiness

A non-invasive approach to detect drowsiness in a monotonous driving environment

Many researchers have found that one of the major contributing factors of road accidents is driver drowsiness. Heart Rate Variability (HRV) is a non-invasive method to observe the influence of autonomic nervous system (ANS) of the human body. The ANS consists of parasympathetic and sympathetic nervous activities and its relation to driver drowsiness is observed by means of HRV analysis. In this study, twenty-two subjects participated in an experiment based on simulated driving environment. The temporal changes for low frequency (LF), high frequency (HF) and LF/HF ratio are observed. LF and HF spectral powers show significant changes from alert to drowsy state. Paired t-test is used to find the statistical significance. The analysis shows that there is a significant (p<;0.01) decrease in the LF/HF ratio when subject is in drowsy state. The observations also conclude with significance that LF decreases (p<;0.001) and HF increases (p<;0.05) from alert to drowsy state. This study shows very encouraging results that can be used to prevent drowsiness related accidents