Microsleep accident prevention for SMART vehicle via image processing integrated with artificial intelligent

Number of accidents caused by microsleep increases rapidly each day. This is due to the current trend of life, for example high workload, long working hours, traffic jams, having too much caffeine, drinking alcohol, age factor, and many others. This microsleep can lead to major accidents, higher number of deaths, injuries, demolition of property and permanent disability. The creation of SMART Vehicles in the Internet of Things (IoT) increases the technology capabilities in transportation sectors, in addition to reduce the number of crashes on the roads. An integration with Artificial Intelligent (AI) can be a perfect combination on development of a microsleep detection and prevention. While the image processing will be used as the method of detecting the face changes from normal to microsleep symptoms on detecting the eye degree, the head motion and the mouth yawning. This work presented a review of current research that supported the integration of IoT and AI. The analysis and discussion on the best solution and method to prevent microsleep accidents was shown. Lastly, recommendation on development of real sensors for SMART Vehicles will be discussed. A preliminary result on this work also will be shown

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

Distractive driver behaviour detection with spiked neural networks

Spiking neural networks are biologically plausible neural networks, capable of utilizing the concept of time throughout their simulations. They offer unique opportunities for the automotive industry because once they are deployed they require lower computational power and thus have decreased power consumption compared to other artificial neural networks. Theoretically, spiking neural network models can be just as powerful as the current state-of-the-art models, but in practice the technology is not matured enough to over-perform standard neural networks in terms of accuracy. The thesis examines a possible application of spiking neural networks to a real-life image classification problem, distractive drive behaviour detection. Detecting the distractive behaviours of drivers could prevent many accidents, thus saving lives. As the automotive industry moves towards even more automation to increase driving safety the need for low-power neural networks will significantly increase in the near future

Sleep loss and fatigue among commercial airline pilots

Today’s flight operations work on a pressurised 24/7 timetable as a result of the unrelenting escalation in international long-haul, short-haul, regional and overnight flights within commercial aviation. Air traffic around the world has doubled every 10 years for the last 30 years. Whilst there have been considerable advancements in aviation technology and operational demands, the human operators need for sleep remains. Commercial airline pilots are presently highly suspectible to sleep loss and fatigue due to these demanding, round-the- clock requirements. Whilst flight and duty time limitation regulations are in place to prevent pilot fatigue, they are not based on sound scientific evidence regarding their ability to do so. Furthermore, various European-based investigations have reported very high levels of sleep disruption and fatigue in European cockpits. Therefore, this study aimed to examine the influence of sleep deprivation and associated fatigue on incidents in flight and mental health and to investigate its effects on performance in commercial airline pilots. Firstly, this research found a critical pathway from duty hours through to self-reported incidents in flight with sleep disruption and feelings of fatigue in the cockpit found to be key factors contributing to this pathway (Study 1). This research also found very high incidences of self-reported sleep disturbance, feelings of fatigue in the cockpit, and consequential errors and incidents in flight as a result of fatigue. Further to this, self-reported sleep disruption and feelings of fatigue were also found to significantly influence pilots’ self-reported perceived depression or anxiety with those who reported higher incidences of sleep disturbance and fatigue being more likely to report feeling depressed or anxious (Study 2). Additionally, 24 hours’ sleep deprivation and subsequent fatigue was found to significantly impair mood and airline pilot core competencies, specifically cognitive flexibility, hand-eye coordination, multi-tasking ability, sustained attention, problem-solving, situation awareness and perceived workload with significant impairments becoming evident following 20 hours of continuous wakefulness (Study 3 & 4). Flying performance was not significantly impaired. Sleep disruption and fatigue is a highly serious and prevalent problem in European cockpits. It negatively impacts flight safety and pilot mental health and well-being. Further investigation in to the current flight and duty time limitation regulations as well as in to potential measures which could act as early detection and warning indicators of declining performance, as a result of sleep loss and fatigue, would enhance flight safety and promote pilot mental health and well-being

Data-driven multivariate and multiscale methods for brain computer interface

This thesis focuses on the development of data-driven multivariate and multiscale methods for brain computer interface (BCI) systems. The electroencephalogram (EEG), the most convenient means to measure neurophysiological activity due to its noninvasive nature, is mainly considered. The nonlinearity and nonstationarity inherent in EEG and its multichannel recording nature require a new set of data-driven multivariate techniques to estimate more accurately features for enhanced BCI operation. Also, a long term goal is to enable an alternative EEG recording strategy for achieving long-term and portable monitoring. Empirical mode decomposition (EMD) and local mean decomposition (LMD), fully data-driven adaptive tools, are considered to decompose the nonlinear and nonstationary EEG signal into a set of components which are highly localised in time and frequency. It is shown that the complex and multivariate extensions of EMD, which can exploit common oscillatory modes within multivariate (multichannel) data, can be used to accurately estimate and compare the amplitude and phase information among multiple sources, a key for the feature extraction of BCI system. A complex extension of local mean decomposition is also introduced and its operation is illustrated on two channel neuronal spike streams. Common spatial pattern (CSP), a standard feature extraction technique for BCI application, is also extended to complex domain using the augmented complex statistics. Depending on the circularity/noncircularity of a complex signal, one of the complex CSP algorithms can be chosen to produce the best classification performance between two different EEG classes. Using these complex and multivariate algorithms, two cognitive brain studies are investigated for more natural and intuitive design of advanced BCI systems. Firstly, a Yarbus-style auditory selective attention experiment is introduced to measure the user attention to a sound source among a mixture of sound stimuli, which is aimed at improving the usefulness of hearing instruments such as hearing aid. Secondly, emotion experiments elicited by taste and taste recall are examined to determine the pleasure and displeasure of a food for the implementation of affective computing. The separation between two emotional responses is examined using real and complex-valued common spatial pattern methods. Finally, we introduce a novel approach to brain monitoring based on EEG recordings from within the ear canal, embedded on a custom made hearing aid earplug. The new platform promises the possibility of both short- and long-term continuous use for standard brain monitoring and interfacing applications

How to improve learning from video, using an eye tracker

The initial trigger of this research about learning from video was the availability of log files from users of video material. Video modality is seen as attractive as it is associated with the relaxed mood of watching TV. The experiments in this research have the goal to gain more insight in viewing patterns of students when viewing video. Students received an awareness instruction about the use of possible alternative viewing behaviors to see whether this would enhance their learning effects. We found that: - the learning effects of students with a narrow viewing repertoire were less than the learning effects of students with a broad viewing repertoire or strategic viewers. - students with some basic knowledge of the topics covered in the videos benefited most from the use of possible alternative viewing behaviors and students with low prior knowledge benefited the least. - the knowledge gain of students with low prior knowledge disappeared after a few weeks; knowledge construction seems worse when doing two things at the same time. - media players could offer more options to help students with their search for the content they want to view again. - there was no correlation between pervasive personality traits and viewing behavior of students. The right use of video in higher education will lead to students and teachers that are more aware of their learning and teaching behavior, to better videos, to enhanced media players, and, finally, to higher learning effects that let users improve their learning from video

Using spontaneously generated online patient experiences to improve healthcare : A case study using Modafinil

Background Acknowledged issues with the RCT focus of EBM and recognition of the value of patient input have created a need for new methods of knowledge generation that can give the depth of qualitative studies but on a much larger scale. Almost half of the global population uses social media regularly, with increasing numbers of people using online spaces as either a first- or second-line health information and exchange resource. Estimates suggest the volume of online health related data grew by 300% between 2017 and 2020. As a data source, this unstructured freeform textual data is a form of patient generated health data, containing a mass of patient centred, contextually grounded detail about the perceptions and health concerns of those who post online. Methods for analysing it are at an early stage of development, but it is seen as having potential to add to clinical understanding, either by augmenting existing knowledge, or in aiding understanding of real-world usage of healthcare interventions and services. Objectives To explore how large-scale analysis of SGOPE can help with understanding patient perspectives of their conditions, symptoms, and self-management behaviours, assess the effectiveness of interventions, contribute to the process of knowledge and evidence creation, and consequently help healthcare systems improve outcomes in the most efficient manner. A secondary aim is to contribute to the development of methods that can be generalised across other interventions or services. Methods Using Modafinil as a case study, a multistage approach was taken. First, an exploratory study, comparing both qualitative and basic NLP techniques was undertaken on a small sample of 260 posts to identify topics, evaluate effectiveness and identify perceived causal text. An umbrella scoping review was then undertaken exploring how and for what purposes SGOPE data is currently being used within healthcare research. Findings from both then guided the main study, which used a variety of unsupervised NLP tools to explore the main dataset of over 69k posts. Individual methods were compared against each other. Results from both studies were compared and for evaluation. Results In contrast to the existing inconclusive systematic review evidence for Modafinil for anything other than narcolepsy, both studies found that Modafinil is seen as by posters as effective in treating fatigue and cognition symptoms in a wide range of conditions. Both identified the topics mentioned in the data, although more work needs to be done to develop the NLP methods to achieve a greater depth of understanding. The first study identified eight themes within the posts: reason for taking, impact of symptoms, acquisition, dosage, side-effects, comparison with other interventions, effectiveness, and quality of life outcomes. Effectiveness of Modafinil was found to be 68% positive, 12% mixed and 18% negative. Expressions of causal belief were identified. In the main study, effectiveness was measured with sentiment analysis, with all methods showing strong positive sentiment. Topic modelling identified groups of themes. Linguistic techniques extracted phrases indicating causality. Various analysis methods were compared to develop a method that could be generalised across other health topics

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 400)

This bibliography lists 397 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during April 1995. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance