There is a rich repertoire of methods for stress detection using various physiological signals and algorithms. However, there is still a gap in research efforts moving from laboratory studies to real-world settings. A small number of research has verified when a physiological response is a reaction to an extrinsic stimulus of the participant’s environment in real-world settings. Typically, physiological signals are correlated with the spatial characteristics of the physical environment, supported by video records or interviews. The present research aims to bridge the gap between laboratory settings and real-world field studies by introducing a new algorithm that leverages the capabilities of wearable physiological sensors to detect moments of stress (MOS). We propose a rule-based algorithm based on galvanic skin response and skin temperature, combing empirical findings with expert knowledge to ensure transferability between laboratory settings and real-world field studies. To verify our algorithm, we carried out a laboratory experiment to create a “gold standard” of physiological responses to stressors. We validated the algorithm in real-world field studies using a mixed-method approach by spatially correlating the participant’s perceived stress, geo-located questionnaires, and the corresponding real-world situation from the video. Results show that the algorithm detects MOS with 84% accuracy, showing high correlations between measured (by wearable sensors), reported (by questionnaires and eDiary entries), and recorded (by video) stress events. The urban stressors that were identified in the real-world studies originate from traffic congestion, dangerous driving situations, and crowded areas such as tourist attractions. The presented research can enhance stress detection in real life and may thus foster a better understanding of circumstances that bring about physiological stress in humans.

Today, medical endoscopy is a widely used procedure to inspect the inner cavities of the human body. The advent of endoscopic imaging techniques-allowing the acquisition of images or videos-created the possibility for the development of the whole new branch of computer-aided decision support systems. Such systems aim at helping physicians to identify possibly malignant abnormalities more accurately. At the beginning of this paper, we give a brief introduction to the history of endoscopy, followed by introducing the main types of endoscopes which emerged so far (flexible endoscope, wireless capsule endoscope, and confocal laser endomicroscope). We then give a brief introduction to computer-aided decision support systems specifically targeted at endoscopy in the gastrointestinal tract. Then we present general facts and figures concerning computer-aided decision support systems and summarize work specifically targeted at computer-aided decision support in the gastrointestinal tract. This summary is followed by a discussion of some common issues concerning the approaches reviewed and suggestions of possible ways to resolve them.

These findings emphasize the importance of individual differences for understanding eating behavior in daily life. Experienced time pressure may be an important aspect for future EMA eating studies.

Psychophysiological science employs a large variety of signals from the human body that index the activity of the peripheral nervous system. This allows for studying interactions of psychological and physiological processes that are relevant for understanding cognition, emotion, and psychopathology. The multidimensional nature of the data and the interactions between different physiological signals represent a methodological and computational challenge. Analysis software in this domain is often limited in its coverage of the signals from different physiological systems, and therefore only partially addresses these challenges. ANSLAB (short for Autonomic Nervous System Laboratory) is an integrated software suite that supports data visualization, artifact detection, data reduction, automated processing, and statistical analysis for a large range of autonomic, respiratory, and muscular measures. Analysis modules for cardiovascular (e.g., electrocardiogram, heart rate variability, blood pressure wave, pulse wave, and impedance cardiography), electrodermal (skin conductance level and responses), respiratory (respiratory pattern, timing, and volume variables, as well as capnography), and muscular (eye-blink startle, facial and bodily electromyography) systems are complemented by specialized modules (e.g., body temperature and accelerometry, cross-spectral analysis of respiratory and cardiac measures, signal averaging, and statistical analysis) and productivityenhancing features (batched processing, fully automatized analyses, and data management). ANSLAB also facilitates the analysis of long-term recordings from ambulatory assessment studies. The present article reviews several analysis modules included in ANSLAB and describes how these address some of the current needs and methodological challenges of psychophysiological science.

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Automated classification of duodenal texture patches with histological ground truth in case of pediatric celiac disease is proposed. The classical focus of classification in this context is a two-class problem: mucosa affected by celiac disease and unaffected duodenal tissue. We extend this focus and apply classification according to a modified Marsh scheme into four classes. In addition to other techniques used previously for classification of endoscopic imagery, we apply Local Binary Patterns (LBP) operators and propose two new operator types, one of which adapts to the different properties of Wavelet transform subbands. The achieved results are promising in that operators based on LBP turn out to achieve better results compared to many other texture classification techniques as used in earlier work. Specifically, the proposed wavelet-based LBP scheme achieved the best overall accuracy of all feature extraction techniques considered in the two-class case and was among the best in the four-class scheme. Results also show that a classification into four classes is feasible in principle, however, when compared to the two-class case we note that there is still room for improvement due to various reasons discussed.

We present a system for an automated colon cancer detection based on the pit pattern classification. In contrast to previous work we exploit the visual nature of the underlying classification scheme by extracting features based on detected edges. To focus on the most discriminative subset of features we use a greedy forward feature subset selection. The classification is then carried out using the k-nearest neighbors (k-NN) classifier.The results obtained are very promising and show that an automated classification of the given imagery is feasible by using the proposed method.

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