Psychophysiological models of hypovigilance detection: A scoping review

Hypovigilance represents a major contributor to accidents. In operational contexts, the burden of monitoring/managing vigilance often rests on operators. Recent advances in sensing technologies allow for the development of psychophysiology‐based (hypo)vigilance prediction models. Still, these models remain scarcely applied to operational situations and need better understanding. The current scoping review provides a state of knowledge regarding psychophysiological models of hypovigilance detection. Records evaluating vigilance measuring tools with gold standard comparisons and hypovigilance prediction performances were extracted from MEDLINE, PsychInfo, and Inspec. Exclusion criteria comprised aspects related to language, non‐empirical papers, and sleep studies. The Quality Assessment tool for Diagnostic Accuracy Studies (QUADAS) and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) were used for bias evaluation. Twenty‐one records were reviewed. They were mainly characterized by participant selection and analysis biases. Papers predominantly focused on driving and employed several common psychophysiological techniques. Yet, prediction methods and gold standards varied widely. Overall, we outline the main strategies used to assess hypovigilance, their principal limitations, and we discuss applications of these models

The effect of electronic word of mouth communication on purchase intention moderate by trust: a case online consumer of Bahawalpur Pakistan

The aim of this study is concerned with improving the previous research finding complete filling the research gaps and introducing the e-WOM on purchase intention and brand trust as a moderator between the e-WOM, and purchase intention an online user in Bahawalpur city Pakistan, therefore this study was a focus at linking the research gap of previous literature of past study based on individual awareness from the real-life experience. we collected data from the online user of the Bahawalpur Pakistan. In this study convenience sampling has been used to collect data and instruments of this study adopted from the previous study. The quantitative research methodology used to collect data, survey method was used to assemble data for this study, 300 questionnaire were distributed in Bahawalpur City due to the ease, reliability, and simplicity, effective recovery rate of 67% as a result 202 valid response was obtained for the effect of e-WOM on purchase intention and moderator analysis has been performed. Hypotheses of this research are analyzed by using Structural Equation Modeling (SEM) based on Partial Least Square (PLS). The result of this research is e-WOM significantly positive effect on purchase intention and moderator role of trust significantly affects the relationship between e-WOM, and purchase intention. The addition of brand trust in the model has contributed to the explanatory power, some studied was conduct on brand trust as a moderator and this study has contributed to the literature in this favor. significantly this study focused on current marketing research. Unlike past studies focused on western context, this study has extended the regional literature on e-WOM, and purchase intention to be intergrading in Bahawalpur Pakistan context. Lastly, future studies are recommended to examine the effect of trust in other countries allow for the comparison of the findings

An eeg based study of unintentional sleep onset

Ph.DDOCTOR OF PHILOSOPH

Improving automatic detection of driver fatigue and distraction using machine learning

Changes and advances in information technology have played an important role in the development of intelligent vehicle systems in recent years. Driver fatigue and distracted driving are important factors in traffic accidents. Thus, onboard monitoring of driving behavior has become a crucial component of advanced driver assistance systems for intelligent vehicles. In this article, we present techniques for simultaneously detecting fatigue and distracted driving behaviors using vision-based and machine learning-based approaches. In driving fatigue detection, we use facial alignment networks to identify facial feature points in the images, and calculate the distance of the facial feature points to detect the opening and closing of the eyes and mouth. Furthermore, we use a convolutional neural network (CNN) based on the MobileNet architecture to identify various distracted driving behaviors. Experiments are performed on a PC based setup with a webcam and results are demonstrated using public datasets as well as custom datasets created for training and testing. Compared to previous approaches, we build our own datasets and provide better results in terms of accuracy and computation time.Comment: Master's thesis, 55 page

Facial expression of pain: an evolutionary account.

This paper proposes that human expression of pain in the presence or absence of caregivers, and the detection of pain by observers, arises from evolved propensities. The function of pain is to demand attention and prioritise escape, recovery, and healing; where others can help achieve these goals, effective communication of pain is required. Evidence is reviewed of a distinct and specific facial expression of pain from infancy to old age, consistent across stimuli, and recognizable as pain by observers. Voluntary control over amplitude is incomplete, and observers can better detect pain that the individual attempts to suppress rather than amplify or simulate. In many clinical and experimental settings, the facial expression of pain is incorporated with verbal and nonverbal vocal activity, posture, and movement in an overall category of pain behaviour. This is assumed by clinicians to be under operant control of social contingencies such as sympathy, caregiving, and practical help; thus, strong facial expression is presumed to constitute and attempt to manipulate these contingencies by amplification of the normal expression. Operant formulations support skepticism about the presence or extent of pain, judgments of malingering, and sometimes the withholding of caregiving and help. To the extent that pain expression is influenced by environmental contingencies, however, "amplification" could equally plausibly constitute the release of suppression according to evolved contingent propensities that guide behaviour. Pain has been largely neglected in the evolutionary literature and the literature on expression of emotion, but an evolutionary account can generate improved assessment of pain and reactions to it

Methods and techniques for analyzing human factors facets on drivers

Mención Internacional en el título de doctorWith millions of cars moving daily, driving is the most performed activity worldwide. Unfortunately, according to the World Health Organization (WHO), every year, around 1.35 million people worldwide die from road traffic accidents and, in addition, between 20 and 50 million people are injured, placing road traffic accidents as the second leading cause of death among people between the ages of 5 and 29. According to WHO, human errors, such as speeding, driving under the influence of drugs, fatigue, or distractions at the wheel, are the underlying cause of most road accidents. Global reports on road safety such as "Road safety in the European Union. Trends, statistics, and main challenges" prepared by the European Commission in 2018 presented a statistical analysis that related road accident mortality rates and periods segmented by hours and days of the week. This report revealed that the highest incidence of mortality occurs regularly in the afternoons during working days, coinciding with the period when the volume of traffic increases and when any human error is much more likely to cause a traffic accident. Accordingly, mitigating human errors in driving is a challenge, and there is currently a growing trend in the proposal for technological solutions intended to integrate driver information into advanced driving systems to improve driver performance and ergonomics. The study of human factors in the field of driving is a multidisciplinary field in which several areas of knowledge converge, among which stand out psychology, physiology, instrumentation, signal treatment, machine learning, the integration of information and communication technologies (ICTs), and the design of human-machine communication interfaces. The main objective of this thesis is to exploit knowledge related to the different facets of human factors in the field of driving. Specific objectives include identifying tasks related to driving, the detection of unfavorable cognitive states in the driver, such as stress, and, transversely, the proposal for an architecture for the integration and coordination of driver monitoring systems with other active safety systems. It should be noted that the specific objectives address the critical aspects in each of the issues to be addressed. Identifying driving-related tasks is one of the primary aspects of the conceptual framework of driver modeling. Identifying maneuvers that a driver performs requires training beforehand a model with examples of each maneuver to be identified. To this end, a methodology was established to form a data set in which a relationship is established between the handling of the driving controls (steering wheel, pedals, gear lever, and turn indicators) and a series of adequately identified maneuvers. This methodology consisted of designing different driving scenarios in a realistic driving simulator for each type of maneuver, including stop, overtaking, turns, and specific maneuvers such as U-turn and three-point turn. From the perspective of detecting unfavorable cognitive states in the driver, stress can damage cognitive faculties, causing failures in the decision-making process. Physiological signals such as measurements derived from the heart rhythm or the change of electrical properties of the skin are reliable indicators when assessing whether a person is going through an episode of acute stress. However, the detection of stress patterns is still an open problem. Despite advances in sensor design for the non-invasive collection of physiological signals, certain factors prevent reaching models capable of detecting stress patterns in any subject. This thesis addresses two aspects of stress detection: the collection of physiological values during stress elicitation through laboratory techniques such as the Stroop effect and driving tests; and the detection of stress by designing a process flow based on unsupervised learning techniques, delving into the problems associated with the variability of intra- and inter-individual physiological measures that prevent the achievement of generalist models. Finally, in addition to developing models that address the different aspects of monitoring, the orchestration of monitoring systems and active safety systems is a transversal and essential aspect in improving safety, ergonomics, and driving experience. Both from the perspective of integration into test platforms and integration into final systems, the problem of deploying multiple active safety systems lies in the adoption of monolithic models where the system-specific functionality is run in isolation, without considering aspects such as cooperation and interoperability with other safety systems. This thesis addresses the problem of the development of more complex systems where monitoring systems condition the operability of multiple active safety systems. To this end, a mediation architecture is proposed to coordinate the reception and delivery of data flows generated by the various systems involved, including external sensors (lasers, external cameras), cabin sensors (cameras, smartwatches), detection models, deliberative models, delivery systems and machine-human communication interfaces. Ontology-based data modeling plays a crucial role in structuring all this information and consolidating the semantic representation of the driving scene, thus allowing the development of models based on data fusion.I would like to thank the Ministry of Economy and Competitiveness for granting me the predoctoral fellowship BES-2016-078143 corresponding to the project TRA2015-63708-R, which provided me the opportunity of conducting all my Ph. D activities, including completing an international internship.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José María Armingol Moreno.- Secretario: Felipe Jiménez Alonso.- Vocal: Luis Mart

Multimodal Features for Detection of Driver Stress and Fatigue: Review

Driver fatigue and stress significantly contribute to higher number of car accidents worldwide. Although, different detection approaches have been already commercialized and used by car producers (and third party companies), research activities in this field are still needed in order to increase the reliability of these alert systems. Also, in the context of automated driving, the driver mental state assessment will be an important part of cars in future. This paper presents state-of-the-art review of different approaches for driver fatigue and stress detection and evaluation. We describe in details various signals (biological, car and video) and derived features used for these tasks and we discuss their relevance and advantages. In order to make this review complete, we also describe different datasets, acquisition systems and experiment scenarios

Improving passive driver fatigue, sitting health risk factors and user experience in automobiles. Conception, development and evaluation of a novel interactive seating system

Lange Fahrten in Automobilen werden oftmals von monotoniebedingter Ermüdung durch mangelnde Stimulation sowie körperlichen Gesundheitsrisiken durch langanhaltendes Sitzen begleitet. In der Literatur werden kognitive Zusatzaufgaben als Maßnahme gegen monotoniebedingte Ermüdung vorgeschlagen. Der Stand der Technik um gesundheitliche Risiken durch langanhaltendes Sitzen im Fahrzeug zu reduzieren, beschränkt sich hauptsächlich auf Sitzsysteme, welche die Passagiere passiv mobilisieren. Mit dem Ziel die Fahrsicherheit zu erhöhen und gleichzeitig Gesundheitsrisiken zu reduzieren, wurde ein neuartiges interaktives Sitzsystem (IASS) konzipiert, entwickelt und evaluiert. Das IASS soll den Fahrer/die Fahrerin oder die Passagiere dazu motivieren mit dem Sitz zu interagieren und dabei aktive Bewegungen gegen die Sitzfläche auszuführen. Im Gegensatz zu aktuellen interaktiven Sitzsystemen erfolgt die grundlegende Interaktion nicht über ein Display. Stattdessen werden auditive Einsprachen, Luftblasen in der Lehne, Vibrationsmotoren sowie die Ambientebeleuchtung genutzt. Ein Display kommt lediglich bei der Einführung in die Nutzung des Systems oder ergänzend für die Passagiere zum Einsatz. Das ermöglicht, dass der Fahrer/die Fahrerin den Blick und damit auch die Aufmerksamkeit auf der Straße behalten kann. Dadurch soll das System, im Gegensatz zum Stand der Technik, nicht nur für die Passagiere, sondern auch für den Fahrer/die Fahrerin geeignet sein. Das wiederum eröffnet die Möglichkeit das IASS als Zusatzaufgabe neben dem Fahren zu nutzen, um einer momotoniebedingten Reduktion der Aufmerksamkeit entgegenzuwirken. Dabei ist anzumerken, dass die erhöhte Fahrsicherheit durch eine verbesserte Aufmerksamkeit nicht nur den Insassen, sondern auch den anderen Verkehrsteilnehmern dient. Gleichzeitig haben alle Passagiere die Möglichkeit, durch die physiologisch sinnvollen Bewegungen gegen den Sitz, die Gesundheitsrisiken durch langanhaltendes Sitzen zu reduzieren. Insgesamt wurden drei Probandenstudien durchgeführt, um das IASS zu entwickeln und zu evaluieren. Die erste Probandenstudie diente der Entwicklung des IASS. Um die Bewegungen des Passagiers/der Passagierin bei der Interaktion zu detektieren, wurden Drucksensoren in der Sitzlehne integriert. Zur Gewährleistung einer zuverlässigen Bewegungserkennung von Passagieren mit unterschiedlichen anthropometrischen Merkmalen, war es entscheidend, die Sensoren an optimalen Positionen in der Lehne zu platzieren. Daher war der erste Schritt im Entwicklungsprozess des IASS die Durchführung einer Probandenstudie, um diese optimalen Positionen festzulegen. Im Rahmen der Studie wurden Sitzdruckverteilungsbilder erfasst und anschließend mit einer Methode, dem Statistical Parametric Mapping ausgewertet, die für gewöhnlich bei der funktionellen Magnetresonanztomographie eingesetzt wird. In der vorliegenden Arbeit wurde diese Methode erstmalig zur Auswertung von Sitzdruckverteilungen herangezogen. Der Vorteil gegenüber herkömmlichen Methoden ist die hohe örtliche Auflösung. Damit war es möglich, die Sensoranbringungspunkte sehr präzise zu definieren. Anschließend wurde das IASS auf Basis der in der ersten Studie gewonnenen Information aufgebaut und anschließend in zwei weiteren Probandenstudien mit einem aktuellen Sitzmassagesystem (MS) hinsichtlich Wirksamkeit verglichen. In der zweiten Probandestudie sollte das Potential des IASS für die Fahrsicherheit bewertet werden. Die im Fahrsimulator durchgeführte Studie zeigte, dass das IASS die monotoniebedingte Ermüdung reduzierte, während dieser Effekt bei Nutzung des MS nicht auftrat. Das IASS wurde ebenfalls bezüglich des Nutzererlebnisses sowie der emotionalen Wahrnehmung gegenüber dem MS bevorzugt. Außerdem bewerteten die Probanden und Probandinnen, dass das IASS im Vergleich zum MS, sowohl Komfort wesentlich stärker erhöhte als auch Diskomfort stärker reduzierte. In der dritten und abschließenden Probandenstudie wurde untersucht, ob das IASS den körperlichen Gesundheitsrisiken durch langanhaltendes Sitzen besser entgegen wirkt als das MS. Hierfür wurden beide Sitzsysteme in einem Serienfahrzeug verbaut. Um die Effekte beider Sitzsysteme zu vergleichen, wurden gesundheitsrelevante Parameter erfasst. Damit fahrtbedingte Signalstörungen ausgeschlossen werden konnten, wurde die Studie im stehenden Fahrzeug durchgeführt. Das Elektrokardiogramm zeigte, dass ausschließlich das IASS die Herzfrequenz erhöhte. Elektromyographische Messungen zeigten außerdem, dass das IASS die Aktivität in den sechs erfassten Muskeln erhöhte, während beim MS lediglich ein Muskel eine Tendenz zur Aktivitätserhöhung zeigte. Entsprechend sind die gesundheitsfördernden Effekte des IASS im Vergleich zum MS als wesentlich stärker einzustufen. Nach dem Kenntnisstand des Autors, ist dies die erste wissenschaftliche Publikation welche eine motorische Zusatzaufgabe als Maßnahme gegen monotoniebedingte Ermüdung im Fahrzeug untersucht. Darüber hinaus wurde erstmalig direkt verglichen, ob ein automobiles Sitzsystem welches zu aktiven Bewegungen animiert, einem System das die Passagiere passiv bewegt, hinsichtlich der Reduktion der negativen körperlichen Effekte des Sitzens überlegen ist

アニメーション映画のキャラクター表情と情動表現に関する研究

筑波大学修士(情報学)学位論文・平成31年3月25日授与(41272号