Intelligent driver drowsiness detection system using uncorrelated fuzzy locality preserving analysis

One of the leading causes of automobile accidents is related to driving impairment due to drowsiness. A large percentage of these accidents occur due to drivers' unawareness of the degree of impairment. An automatic detection of drowsiness levels could lead to lower accidents and hence lower fatalities. However, the significant fluctuations of the drowsiness state within a short time poses a major challenge in this problem. In response to such a challenge, we present the Uncorrelated Fuzzy Locality Preserving Analysis (UFLPA) feature projection method. The proposed UFLPA utilizes the changes in driver behavior, by means of the corresponding Electroencephalogram (EEG), Electrooculogram (EOG), and Electrocardiogram (ECG) signals to extract a set of features that can highly discriminate between the different drowsiness levels. Unlike existing methods, the proposed UFLPA takes into consideration the fuzzy nature of the input measurements while preserving the local discriminant and manifold structures of the data. Additionally, UFLPA also utilizes Singular Value Decomposition (SVD) to avoid the singularity problem and produce a set of uncorrelated features. Experiments were performed on datasets collected from thirty-one subjects participating in a simulation driving test with practical results indicating the significance of the results achieved by UFLPA of 94%-95% accuracy on average across all subjects. © 2011 IEEE

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data

Uncorrelated fuzzy neighborhood preserving analysis based feature projection for driver drowsiness recognition

Driver drowsiness is reported as one of the main causal factors in many traffic accidents as it progressively impairs the driver's awareness about external events. Drowsiness detection can be approached through monitoring physiological signals while driving to correlate drowsiness with the change in the corresponding patterns of the Electroencephalogram (EEG), Electrooculogram (EOG), and Electrocardiogram (ECG) signals. The main challenge in such an approach is to extract a set of features that can highly discriminate between the different drowsiness levels. This paper proposes a new Fuzzy Neighborhood Preserving Analysis (FNPA) feature projection method that is used to extract the discriminant information relevant to the loss of attention caused by drowsiness. Unlike existing methods, FNPA considers the fuzzy memberships of the input measurements into the different classes while constructing the graph Laplacian. Thus, it is able to identify both the discriminant and the geometrical structure of the input data while accounting for the overlapping nature of the drowsiness patterns. Furthermore, in order to address the singularity problem that occurs in many real world problems, the singular value decomposition (SVD), and later the QR-Decomposition, are utilized to extract a set of statistically uncorrelated features presenting the Uncorrelated FNPA (UFNPA). In the current preliminary study with datasets collected from 31 subjects only, while performing a driving simulation task, the proposed method is capable of accurately classifying the drowsiness levels using a small number of features with an average accuracy of ≈93%. On the other hand, the possibility of developing a subject-independent drowsiness recognition system is also investigated when the problem is converted into a binary classification task, as imposed by the number of drowsiness levels exhibited by the drivers, with accuracies ranging from 82%-to-84%. © 2012 Elsevier B.V

Advances in Computational Intelligence Applications in the Mining Industry

This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners

New radial basis function network based techniques for holistic recognition of facial expressions

Ph.DDOCTOR OF PHILOSOPH

Improving Access and Mental Health for Youth Through Virtual Models of Care

The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic