Measuring driver cognitive distraction through lips and eyebrows

<span>Cognitive distraction is one of the several contributory factors in road accidents. A number of cognitive distraction detection methods have been developed. One of the most popular methods is based on physiological measurement. Head orientation, gaze rotation, blinking and pupil diameter are among popular physiological parameters that are measured for driver cognitive distraction. In this paper, lips and eyebrows are studied. These new features on human facial expression are obvious and can be easily measured when a person is in cognitive distraction. There are several types of movement on lips and eyebrows that can be captured to indicate cognitive distraction. Correlation and classification techniques are used in this paper for performance measurement and comparison. Real time driving experiment was setup and faceAPI was installed in the car to capture driver’s facial expression. Linear regression, support vector machine (SVM), static Bayesian network (SBN) and logistic regression (LR) are used in this study. Results showed that lips and eyebrows are strongly correlated and have a significant role in improving cognitive distraction detection. Dynamic Bayesian network (DBN) with different confidence of levels was also used in this study to classify whether a driver is distracted or not.</span>

Where is emotional feeling felt in the body? An integrative review

Contemporary research on “embodied emotion” emphasizes the role of the body in emotional feeling. The evidence base on interoception, arguably the most prominent strand of embodied emotion research, places emphasis on the cardiac, respiratory and gastrointestinal systems. In turn, interoception has evidence-based links with improved emotion regulation. Despite the focus on separate bodily systems, it is unclear whether particular interoceptive locations play a greater role in emotional feeling and emotion regulation. Further, according to Gross’ “process model”, the sooner that regulation of an emotion occurs, the better; hence, it is additionally important to identify the first body areas to activate. These issues are investigated in a two-stage integrative review. The first stage was preliminary, giving an overview of the evidence base to highlight the distribution of measured body areas. This indicated that 86% of publications (n = 88) measured cardiac activity, 26% measured the respiratory system, and six percent the gastrointestinal system. Given the emphasis placed on all three systems in interoception theory and research on emotion, this suggests a dearth of comprehensive findings pertaining to feeling locations. The second stage investigated the core issues of where emotional feelings are felt in the body and time-related implications for regulation. This was based on ten texts, which together suggested that the head, throat and chest are the most consistently detected locations across and within numerous emotional contexts. Caution is required, however, since–among other reasons discussed–measurement was not time-restricted in these latter publications, and direct physiological measurement was found in only a minority of cases.

Implementation of Thermal Camera for Non-Contact Physiological Measurement: A Systematic Review

Non-contact physiological measurements based on image sensors have developed rapidly in recent years. Among them, thermal cameras have the advantage of measuring temperature in the environment without light and have potential to develop physiological measurement applications. Various studies have used thermal camera to measure the physiological signals such as respiratory rate, heart rate, and body temperature. In this paper, we provided a general overview of the existing studies by examining the physiological signals of measurement, the used platforms, the thermal camera models and specifications, the use of camera fusion, the image and signal processing step (including the algorithms and tools used), and the performance evaluation. The advantages and challenges of thermal camera-based physiological measurement were also discussed. Several suggestions and prospects such as healthcare applications, machine learning, multi-parameter, and image fusion, have been proposed to improve the physiological measurement of thermal camera in the future.

The Decisive Role of Non-Decision Time for Interpreting the Parameters of Decision Making Models

Computational models of decision making are becoming increasingly popular to interpret reaction time and choice data in terms of decision and non-decision related processes. But current evidence remains scarce as to whether parameters of a mathematical model such as the Drift Diffusion Model (DDM) can recover genuine latent psychological processes. In this study, we combine an experimental approach using a decision making task with a physiological decomposition of each reaction time into a motor and pre-motor time using electro-myography. The aim is to test whether the non-decision time parameter of a DDM, assumed to contain encoding and motor processes, varies according to both psychophysical predictions of stimulus encoding and the physiological measurement of motor processes. Our results show that 1) the encoding time is accounted by a DDM only in the case of instructions emphasizing speed over accuracy and 2) that the onset of muscular activity does not sign the end of the accumulation of evidence. This questions the ability of DDM to account for how participants achieve speed-accuracy tradeoff as well as the interpretability of its parameters in terms of decision and non-decision processes.

Digitally connected work and its consequences for strain – a systematic review

Background Evolving digitization has an impact not only on the organization of work, but also on the health of employees. Dealing with new technologies, integrating new processes and requirements into work, and restructuring tasks among others are demands that can be stressful and impair health. Objectives Our aim was to identify (clusters of) working conditions associated with digitally connected work and to analyze their relations with strain, that is, health and well-being outcomes. Methods Between May and October 2019, a search string was used to systematically search six databases (EMBASE, Medline, PSYNDEX, PsycInfo, SocIndex, WISO) for German and English texts according to the PEO scheme. The methodological quality was assessed using the Quality Assessment Tool for Studies with Diverse Design. Results 14 studies were identified. Despite the search string containing latest technologies, we identified mostly studies from the 1980s/90s. To aggregate findings, a categorization of work factors (cognitive demands, social factors, organizational factors, environmental factors) and health factors (motivation/satisfaction, reduced well-being/affective symptoms, physiological parameters/somatic complaints) is introduced. The most frequently identified work factors belong to the category of cognitive demands. For health factors, motivation/satisfaction was identified most often. 475 associations were found in total. Conclusions This systematic review provides an overview of work and health factors that have been studied between 1981 and 2019. Recent texts frequently study individualized health factors (e.g., life satisfaction) whereas objective physiological measurement data and objective survey methods such as workplace analysis are not used. This latter approach was predominantly found in the older studies. In order to obtain a comprehensive picture, however, it is worthwhile to use a combination of these subjective and objective approaches for future studies in this field.

Prediction of COVID-19 Patient using Supervised Machine Learning Algorithm

One of the most symptomatic diseases is COVID-19. Early and precise physiological measurement-based prediction of breathing will minimize the risk of COVID-19 by a reasonable distance from anyone; wearing a mask, cleanliness, medication, balanced diet, and if not well stay safe at home. To evaluate the collected datasets of COVID-19 prediction, five machine learning classifiers were used: Nave Bayes, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbour (KNN), and Decision Tree. COVID-19 datasets from the Repository were combined and re-examined to remove incomplete entries, and a total of 2500 cases were utilized in this study. Features of fever, body pain, runny nose, difficulty in breathing, shore throat, and nasal congestion, are considered to be the most important differences between patients who have COVID-19s and those who do not. We exhibit the prediction functionality of five machine learning classifiers. A publicly available data set was used to train and assess the model. With an overall accuracy of 99.88 percent, the ensemble model is performed commendably. When compared to the existing methods and studies, the proposed model is performed better. As a result, the model presented is trustworthy and can be used to screen COVID-19 patients timely, efficiently.

Phosphoproteomics of Cold Stress-Responsive Mechanisms in Rhododendron Chrysanthum

As an alpine plant，Rhododendron chrysanthum (R. chrysanthum) has evolved cold resistance mechanisms and become a valuable plant resource with the responsive mechanism of cold stress. In my study, we adopt the phosphoproteomic and proteomic analysis combining with physiological measurement to illustrate the responsive mechanism of R. chrysanthum seedling under cold (4℃) stress. After chilling for 12 h, 350 significantly changed proteins and 274 significantly changed phosphoproteins were detected. Clusters of Orthologous Groups（COG）analysis showed that significantly changed proteins and phosphoproteins were mainly involved in signal transduction and energy production and conversion under cold stress. The results indicated photosynthesis was inhibited under cold stress, but cold induced calcium-mediated signaling, reactive oxygen species (ROS) homeostasis and other transcription regulation factors could protect plants from the destruction caused by cold stress. These results provide a detailed insight into the cold stress response and defense mechanisms of R. chrysanthum leaves at the phosphoproteome level.