<p>Emerging technologies such as real-time travel information systems and automated vehicles (AVs) have profound impacts on driver decision-making behavior. While they generally have positive impacts by enabling drivers to make more informed decisions or by reducing their driving effort, there are several concerns related to inadequate consideration of cognitive and psychological aspects in their design. In this context, this dissertation analyzes different aspects of driver cognition and psychology that arise from drivers’ interactions with these technologies using physiological data collected in two sets of driving simulator experiments.</p> <p>This research analyzes the latent cognitive and psychological effects of real-time travel information using electroencephalogram (EEG) data measured in the first set of driving simulator experiments. Using insights from the previous analysis, a hybrid route choice modeling framework is proposed that incorporates the impacts of the latent information-induced cognitive and psychological effects along with other explanatory variables that can be measured directly (i.e., route characteristics, information characteristics, driver attributes, and situational factors) on drivers’ route choice decisions. EEG data is analyzed to extract two latent cognitive variables that capture the driver’s cognitive effort during and immediately after the information provision, and cognitive inattention before implementing the route choice decision. </p> <p>Several safety concerns emerge for the transition of control from the automated driving system to a human driver after the vehicle issues a takeover warning under conditional vehicle automation (SAE Level 3). In this context, this study investigates the impacts of driver’s pre-warning cognitive state on takeover performance (i.e., driving performance while resuming manual control) using EEG data measured in the second set of driving simulator experiments. However, there is no comprehensive metric available in the literature that could be used to benchmark the role of driver’s pre-warning cognitive state on takeover performance, as most existing studies ignore the interdependencies between the associated driving performance indicators by analyzing them independently. This study proposes a novel comprehensive takeover performance metric, Takeover Performance Index (TOPI), that combines multiple driving performance indicators representing different aspects of takeover performance. </p> <p>Acknowledging the practical limitations of EEG data to have real-world applications, this dissertation evaluates the driver’s situational awareness (SA) and mental stress using eye-tracking and heart rate measures, respectively, that can be obtained from in-vehicle driver monitoring systems in real-time. The differences in SA and mental stress over time, their correlations, and their impacts on the TOPI are analyzed to evaluate the efficacy of using eye-tracking and heart rate measures for estimating the overall takeover performance in conditionally AVs.</p> The study findings can assist information service providers and auto manufacturers to incorporate driver cognition and psychology in designing safer real-time information and their delivery systems. They can also aid traffic operators to incorporate cognitive aspects while devising strategies for designing and disseminating real-time travel information to influence drivers’ route choices. Further, the study findings provide valuable insights to design operating and licensing strategies, and regulations for conditionally automated vehicles. They can also assist auto manufacturers in designing integrated in-vehicle driver monitoring and warning systems that enhance road safety and user experience.

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Contexte : Chaque année de multiples enfants guérissent des cancers pédiatriques, principalement de la leucémie lymphoblastique aiguë (LLA), le cancer le plus important chez les enfants. Ces enfants sont à risque de plusieurs comorbidités à la suite des traitements contre le cancer qui peuvent nuire à leur qualité de vie. La cardiotoxicité est la comorbidité la plus fréquente. Les maladies cardiaques découlant de cette cardiotoxicité sont la cause de décès non cancéreuse la plus fréquente chez les enfants survivants de cancer. La cardiotoxicité est une maladie hétérogène avec de multiples présentations cliniques et des mécanismes physiopathologiques qui ne sont pas encore totalement compris. L’exposition du cœur et des tissus adjacents à la radiothérapie et à la chimiothérapie peut induire des lésions cardiaques en entraînant une dégénérescence des cardiomyocytes qui mène éventuellement à la mort des cardiomyocytes. La perte progressive des cardiomyocytes fonctionnels entraîne un remodelage ventriculaire pathologique, dont les symptômes peuvent ne pas se manifester immédiatement. À un certain stade, les cardiomyocytes restants ne peuvent plus compenser pour les demandes cardiaques, ce qui cause l’apparition initiale de la dysfonction diastolique et de l’apparition ultérieure de la dysfonction systolique et de l’insuffisance cardiaque. La détection précoce des lésions cardiaques est donc devenue une nécessité pour réduire le risque et le fardeau de la cardiotoxicité chez les survivants de LLA pédiatrique. Il est possible que les paramètres de dépistage actuels sous-estiment le risque chez les survivants du cancer et qu'une évaluation cardiaque plus large soit nécessaire. L’électrocardiogramme est un test utilisé pour évaluer la fréquence et le rythme cardiaques et permet de montrer des signes précoces de dommages cardiaques sous cliniques. Il peut montrer des arythmies, des changements plus subtiles qui précèdent les arythmies ou des changements ischémiques. L'évaluation cardiaque en situation de stress peut également révéler un dysfonctionnement myocardique. Ce qui nous mène à nous poser les questions suivantes : Quelle est la prévalence des anomalies à l’électrocardiogramme à la suite des traitements contre le cancer chez les survivants de LLA pédiatrique ? Est-ce que la cardiotoxicité entraîne des anomalies de la fréquence cardiaque à l’effort ? Est-ce que l’électrocardiogramme à l’effort permet d’identifier des anomalies de la repolarisation ventriculaire qui n’apparaissent pas au repos ? L’objectif est de détecter les anomalies à l’électrocardiogramme au repos et à l’effort pour améliorer la détection précoce de la cardiotoxicité. Résultats principaux : Nous avons confirmé l'apparition d'anomalies de l’électrocardiogramme, notamment des anomalies de la fréquence cardiaque, des intervalles QTc prolongés, des modifications de l'onde ST-T, des ondes Q pathologiques, des arythmies, des troubles de la conduction et des voltage QRS bas, qui étaient associés au traitement de la LLA pédiatrique. Les anomalies de la fréquence cardiaque pendant l’effort et à l’effort maximal sont fréquentes dans notre cohorte de 251 survivants de LLA pédiatrique. Nous avons montré que la majorité des survivants de LLA pédiatrique ont une fréquence cardiaque anormale au maximum de leur effort. Certains participants présentaient aussi de l’incompétence chronotrope, ce qui est une condition anormale pour de jeunes adultes. Nous avons identifié des allongements de l’intervalle QT au repos, mais davantage à l’effort. Nous avons également comparé l’utilisation de différentes approches pour la correction de l’intervalle QT afin d’avoir une évaluation précise de l’intervalle QTc. Conclusions : L’épreuve d’effort avec un électrocardiogramme est un moyen non invasif et rentable d'évaluer l'activité électrique cardiaque, permettant de détecter des changements subtils qui peuvent indiquer une cardiotoxicité sous-jacente. Cette recherche est cruciale pour s'assurer que les survivants de LLA pédiatrique vivent non seulement plus longtemps, mais qu'ils mènent également une vie saine et épanouie. / Backgrounds: Every year, many children recover from pediatric cancers, especially acute lymphoblastic leukemia (ALL), the most common childhood cancer. These children are at risk of several comorbidities following cancer treatment, which can adversely affect their quality of life. Cardiotoxicity is the most frequent comorbidity. Heart disease is the most common non-cancerous cause of death in childhood cancer survivors. Cardiotoxicity is a heterogeneous disease with multiple clinical presentations and often poorly understood pathophysiological mechanisms. It can lead to electrophysiological dysfunction and morphological and functional alterations. Exposure of the heart and surrounding tissues to radiotherapy and chemotherapy can induce cardiac lesions, leading to cardiomyocyte degeneration and eventually leading to cardiomyocyte death. Continued loss of functional cardiomyocytes leads to pathological ventricular remodeling, the symptoms of which may not be immediately apparent. At a certain stage, the remaining cardiomyocytes can no longer compensate for cardiac demands. This leads to the initial onset of diastolic dysfunction and the subsequent development of systolic dysfunction and heart failure. Early detection of cardiac damage has therefore become a key to reducing the risk and burden of cardiotoxicity in pediatric ALL survivors. It is possible that current screening parameters underestimate risk in pediatric cancer survivors and that a broader cardiac evaluation is required. An electrocardiogram is a test used to assess heart rate and rhythm and may show early signs of subclinical cardiac damage in pediatric cancer survivors who have received cardiotoxic treatments. The electrocardiogram can show cardiotoxicity through arrhythmias, the more subtle changes that precede arrhythmias, or ischemic changes. Stress cardiac evaluation can also reveal myocardial dysfunction. This leads us to ask the following questions: What is the prevalence of electrocardiogram abnormalities following cancer treatment in pediatric ALL survivors? Does cardiotoxicity lead to heart rate abnormalities during exercise? Does the exercise electrocardiogram identify ventricular repolarization abnormalities caused by cardiotoxicity that do not appear at rest? The aim is to detect electrocardiogram abnormalities at rest and during exercise to improve early detection of cardiotoxicity. Main results: We confirmed the occurrence of electrocardiogram abnormalities, including heart rate abnormalities, prolonged QTc intervals, ST-T wave changes, pathological Q waves, arrhythmias, conduction disturbances and low QRS voltage, which were associated with treatment of pediatric ALL. Heart rate abnormalities during exercise and at maximal effort are common in our cohort of 251 pediatric ALL survivors. We showed that the majority of pediatric ALL survivors have an abnormal heart rate at peak exertion. Some participants also exhibited chronotropic incompetence, which is an abnormal condition for young adults. We identified QT interval prolongations at rest, but more so during exercise. We also compared the use of different approaches to QT correction in order to obtain an accurate assessment of the QTc interval. Conclusions: Exercise testing with an electrocardiogram is a non-invasive and cost-effective means of assessing cardiac electrical activity, allowing detection of subtle changes that may indicate underlying cardiotoxicity. This research is crucial to ensuring that pediatric ALL survivors not only live longer, but also lead healthy, fulfilling lives.

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Atrial fibrillation is one of the most common cardiac rhythm disorders characterized by ever-increasing prevalence and incidence in the Czech Republic and abroad. The incidence of atrial fibrillation is reported at 2-4 % of the population, but due to the often asymptomatic course, the real prevalence is even higher. The aim of this work is to design an algorithm for automatic detection of atrial fibrillation in the ECG record. In the practical part of this work, an algorithm for the detection of atrial fibrillation is proposed. For the detection itself, the k-nearest neighbor method, the support vector method and the multilayer neural network were used to classify ECG signals using features indicating the variability of RR intervals and the presence of the P wave in the ECG recordings. The best detection was achieved by a model using a multilayer neural network classification with two hidden layers. Results of success indicators: Sensitivity 91.23 %, Specificity 99.20 %, PPV 91.23 %, F-measure 91.23 % and Accuracy 98.53 %.

The thesis deals with one of the non-linear methods for signal processing - dynamic time warping (DTW). The method observes shape changes, which should be used in biomedical signals processing. The thesis involves the method description and consecution for finding DTW optimal way. The method is applied on the number series in the edutainment program, on the group of simulated signals and real electrocardiograms (ECG). ECG recordings were gained by performing experiments on the Masaryk University and their aim was clarifying the influence of voltage-sensitive dye on the heart tissue. One-lead ECG was described in time domain, frequency domain, time-frequency domain and subsequently remitted to DTW algorithm. The method outcomes evaluates the diversity rate of ECG signals obtained in each experiment stages. During the data evaluation were followed up the changes in process of the tension-sensible paint application and the stage of scouring toward control. The difference of elaborating signals group was verified in the time domain (37,5 %), in the frequency domain (75 %) and in the time-frequency domain (25 %). However, due to the small data group was not possible to explicitly approve the activity of voltage-sensitive dye on the heart tissue and to determinate limiting value of minimum algorithm way DTW for each heart round electrocardiogram classification. In the more data group analysis it is supposed to manifest the trend of growth heart round ECG differences in the stage of staining toward the stage of scouring.

This thesis deals with analysis of T-wave Alternans (TWA), periodical changes of T wave in ECG signal. Presence of these alternans may predict higher risk of sudden cardiac death. From the several possible ways of TWA classification, the training algorithms of self organizing maps are used in this thesis. Result of the thesis is a program, which in the first step detects QRS complexes in the signal. Then, in the next step, gained reference points are used for T-waves detection. Detected waves are represented by a vector of significant points, which is used as an input for artificial neural network. Final output of the program is a decision about presence of TWA in the signal and its rate.

This thesis discusses the utilization of the artificial neural networks (ANN) for detection of coronary artery disease (CAD) in frequency area. The first part of this thesis is orientated towards the theoretical knowledge. Describes the issue of ECG pathological changes. ECQ are converted to frequency area. Described statistical methods and methods for automatic detection of CAD and MI. Explained the issue of the perceptron and ANN. The second deals with use of Neural Network Toolbox MATLAB®. This part focuses on counting and finding suitable parameters and making connection of band. At the end of the thesis UNS is used to detect ischemic parameters and the results are discussed. Average values for the best settings are 100% accuracy.

Measures of workload have been developed on the basis of the various definitions, some are designed to capture the multi-dimensional aspects of a unitary resource pool (Kahneman, 1973) while others are developed on the basis of multiple resource theory (Wickens, 2002). Although many theory based workload measures exist, others have often been constructed to serve the purpose of specific experimental tasks. As a result, it is likely that not every workload measure is reliable and valid for all tasks, much less each domain. To date, no single measure, systematically tested across experimental tasks, domains, and other measures is considered a universal measure of workload. Most researchers would argue that multiple measures from various categories should be applied to a given task to comprehensively assess workload. The goal for Study 1 to establish task load manipulations for two theoretically different tasks that induce distinct levels of workload assessed by both subjective and performance measures was successful. The results of the subjective responses support standardization and validation of the tasks and demands of that task for investigating workload. After investigating the use of subjective and objective measures of workload to identify a universal and comprehensive measure or set of measures, based on Study 2, it can only be concluded that not one or a set of measures exists. Arguably, it is not to say that one will never be conceived and developed, but at this time, one does not reside in the psychometric catalog. Instead, it appears that a more suitable approach is to customize a set of workload measures based on the task. The novel approach of assessing the sensitivity and comprehensive ability of conjointly utilizing subjective, performance, and physiological workload measures for theoretically different tasks within the same domain contributes to the theory by laying the foundation for improving methodology for researching workload. The applicable contribution of this project is a stepping-stone towards developing complex profiles of workload for use in closed-loop systems, such as human-robot team iv interaction. Identifying the best combination of workload measures enables human factors practitioners, trainers, and task designers to improve methodology and evaluation of system designs, training requirements, and personnel selection