Driving Big Data – Integration and Synchronization of Data Sources for Artificial Intelligence Applications with the Example of Truck Driver Work Stress and Strain Analysis

This paper contributes to the issue of big data analysis and data quality with the specific field of time synchronization. As a highly relevant use case, big data analysis of work stress and strain factors for driving professions is outlined. Drivers experience work stress and strain due to trends like traffic congestion, time pressure or worsening work conditions. Although a large professional group with 2.5 million (US) and 3.5 million (EU) truck drivers, scientific analysis of work stress and strain factors is scarce. Driver shortage is growing into a large-scale economic and societal challenge, especially for small businesses. Empirical investigations require big data approaches with sources like physiological and truck, traffic, weather, planning or accident data. For such challenges, accurate data is required, especially regarding time synchronization. Awareness among researchers and practitioners is key and first solution approaches are provided, connecting to many further Machine Learning and big data applications

Augmented reality and portable devices to increase safety in container terminals: the testing of A4S project in the port of Genoa

Port container terminals are intrinsically complex environments and the human factor is often the main cause of accidents. Industry 4.0 technologies enable to dispose of enormous quantity of data, process them with advanced algorithms also allowing predictivity, and provide virtual/augmented reality tools to interact with human operators. Promising solutions are spreading that use the loT paradigm to acquire data and apply Big Data techniques to manage them. The objective of “Awareness for Safey-A4S” project, is to test a complete solution that allows field operators to be equipped with intelligent wearable devices, allowing "conscious" interaction in complex environments. This solution provides for the visualization of environment information in real time through Augmented Reality devices. These devices themselves represent a "sensor" providing information to the general system. Such information, integrated with environmental data and gathered through a specific I-IoT cloud platform and customized field devices, can improve safety and effectiveness of operations. Further support for operator safety is provided by a route tracking system aimed at directing operators, walking in the terminal, on the shortest and safest path. Such system can consider in real time the risks due to the movements of terminal equipment. The current paper presents this solution and the first field tests at PSA SECH container terminal in the Italian port of Genoa port to demonstrate the effectiveness of the proposed solution in increasing safety in complex and dangerous environments

An Adaptive Game-Based Learning Strategy for Children Road Safety Education and Practice in Virtual Space

Virtual reality (VR) has been widely used as a tool to assist people by letting them learn and simulate situations that are too dangerous and risky to practice in real life, and one of these is road safety training for children. Traditional video- and presentation-based road safety training has average output results as it lacks physical practice and the involvement of children during training, without any practical testing examination to check the learned abilities of a child before their exposure to real-world environments. Therefore, in this paper, we propose a 3D realistic open-ended VR and Kinect sensor-based training setup using the Unity game engine, wherein children are educated and involved in road safety exercises. The proposed system applies the concepts of VR in a game-like setting to let the children learn about traffic rules and practice them in their homes without any risk of being exposed to the outside environment. Thus, with our interactive and immersive training environment, we aim to minimize road accidents involving children and contribute to the generic domain of healthcare. Furthermore, the proposed framework evaluates the overall performance of the students in a virtual environment (VE) to develop their road-awareness skills. To ensure safety, the proposed system has an extra examination layer for children’s abilities evaluation, whereby a child is considered fit for real-world practice in cases where they fulfil certain criteria by achieving set scores. To show the robustness and stability of the proposed system, we conduct four types of subjective activities by involving a group of ten students with average grades in their classes. The experimental results show the positive effect of the proposed system in improving the road crossing behavior of the children

Coupling Mobile Technology, Position Data Mining, and Attitude toward Risk to Improve Construction Site Safety

Construction sites comprise constantly moving heterogeneous resources that interact in close proximity of each other. The sporadic nature of such interactions creates an accident prone physical space surrounding workers. Despite efforts to improve site safety using location-aware proximity sensing techniques, major scientific gaps still remain in reliably forecasting impending hazardous scenarios before they occur. In the research documented in this thesis, spatiotemporal data of workers and site hazards are fused with a quantifiable model of an individual\u27s attitude toward risk to generate proximity-based safety alerts in real time. In particular, two trajectory prediction models, namely polynomial regression (PR) and hidden Markov model (HMM) are investigated and their effectiveness in predicting a worker\u27s position given his or her past movement trajectory is evaluated. Next, HMM prediction is further improved and calibrated by factoring in a worker\u27s risk profile, a measure of his affinity for or aversion to risky behavior near hazards. Finally, a mobile application is designed and tested in a series of field experiments involving trajectories of different shape and complexity to verify the applicability and value of the designed methodology in addressing construction safety-related problems. Results demonstrate that the developed risk-calibrated HMM-based motion trajectory prediction can reliably detect unsafe movements and impending collision events

Methods for enhanced learning using wearable technologies. A study of the maritime sector

Maritime safety is a critical concern due to the potential for serious consequences or accidents for the crew, passengers, environment, and assets resulting from navigation errors or unsafe acts. Traditional training methods face challenges in the rapidly evolving maritime industry, and innovative training methods are being explored. This study explores the use of wearable sensors with biosignal data collection to improve training performance in the maritime sector. Three experiments were conducted progressively to investigate the relationship between navigators' experience levels and biosignal data results, the effects of different training methods on cognitive workload, trainees' stress levels, and their decision-making skills, and the classification of scenario complexity and the biosignal data obtained by the trainees. questionnaire data on stress levels, workload, and user satisfaction of auxiliary training equipment; performance evaluation data on navigational abilities, decision-making skills, and ship-handling abilities; and biosignal data, including electrodermal activity (EDA), body temperature, blood volume pulse (BVP), inter-beat interval (IBI), and heart rate (HR). Several statistical methods and machine-learning algorithms were used in the data analysis. The present dissertation contributes to the advancement of the field of maritime education and training by exploring methods for enhancing learning in complex situations. The use of biosignal data provides insights into the interplay between stress levels and training outcomes in the maritime industry. The proposed conceptual training model underscores the relationship between trainees' stress and safety factors and offers a framework for the development and evaluation of advanced biosignal data-based training systems

Virtual reality environments for the study of decision-making processes in risky contexts through the use of physiological measures and behavioural responses

Tesis por compendio[ES] Comprender el comportamiento humano en situaciones de riesgo, cómo los factores individuales y externos influyen en nuestras decisiones y en qué medida es posible influir y modificar nuestros comportamientos, constituye un desafío tanto para los científicos como para la sociedad en general. Desde la perspectiva de la Seguridad y Salud en el Trabajo (SST), así como en numerosos campos como la sociología y las finanzas, este tema tiene importantes implicaciones ya que las situaciones de riesgo son un aspecto común en diversos ámbitos de nuestra vida. La toma de riesgos (TR) es parte del proceso de toma de decisiones en situaciones de incertidumbre, en las que se conoce de antemano la probabilidad de cada consecuencia positiva o negativa. Si bien el concepto de la TR está bien definido en la literatura, se ha abordado desde diferentes perspectivas, por lo que los factores que se han propuesto para explicar o moderar la TR también son muy diversos. Centrándonos en los factores individuales (procesos cognitivos y emocionales) que influyen en la TR, estos pueden afectar la forma en que se abordan las situaciones peligrosas de dos maneras diferentes. En primer lugar, pueden sesgar la percepción de una situación, de modo que no se lleve a cabo una evaluación adecuada y por tanto esto lleve a conductas sesgadas. En segundo lugar, estos factores configuran una cierta propensión general al riesgo en los seres humanos, de modo que pueden o no sentirse atraídos por situaciones potencialmente peligrosas. De la misma manera que la definición de la TR se ha abordado desde diferentes perspectivas, su evaluación también se ha tratado desde diferentes puntos de vista y constituye hoy en día un desafío para investigadores y profesionales, por lo que no se ha encontrado un consenso claro en cuanto a la existencia de una medida validada para la TR. La evaluación de la TR se ha realizado tradicionalmente mediante cuestionarios; sin embargo, se ha demostrado que estas medidas presentan diversas limitaciones que pueden conducir a resultados alterados. Las tareas comportamentales surgen como una solución alternativa capaz de superar algunas de estas barreras. En cambio, su capacidad de transferencia a situaciones de la vida real parece ser limitada. La realidad virtual (RV) permite recrear situaciones reales simuladas para realizar evaluaciones basadas en el desempeño. La RV presenta numerosas ventajas que pueden aportar beneficios para la evaluación de los comportamientos humanos, ya que aporta una mayor inmersión, fidelidad y un mayor nivel de implicación que los métodos de evaluación tradicionales, y numerosos trabajos en el campo de la psicología aplicada y la neurociencia organizacional han avalado su uso para evaluación humana. En esta investigación, proponemos la RV como tecnología capaz de facilitar el estudio de los procesos de la TR, aprovechando sus numerosas posibilidades, que se pueden resumir como: simulación de situaciones de riesgo realistas, interacciones naturales con el entorno virtual, inclusión de medidas implícitas para evaluación oculta y medición fisiológica en tiempo real. Esta tesis proporciona aportaciones a la definición de la TR, particularmente en la identificación de qué factores constituyen este complejo proceso. Además, investiga el uso de la RV inmersiva en la investigación del comportamiento humano, específicamente para la evaluación de la TR, proporcionando premisas de diseño de entornos virtuales para la evaluación de los constructos psicológicos identificados como determinantes para definir la TR. Finalmente, analiza la validez de la RV en combinación con medidas fisiológicas para la evaluación de la TR de forma implícita.[CA] Comprendre el comportament humà en situacions de risc, com els factors individuals i externs influeixen en les nostres decisions i en quina mesura és possible influir i modificar els nostres comportaments, constitueix un desafiament tant per als científics com per a la societat en general. Des de la perspectiva de la Seguretat i Salut en el Treball (SST), així com en nombrosos camps com la sociologia i les finances, aquest tema té importants implicacions ja que les situacions de risc són un aspecte comú en diversos àmbits de la nostra vida. La presa de riscos (PR) és part del procés de presa de decisions en situacions d'incertesa, en les quals es coneix per endavant la probabilitat de cada conseqüència positiva o negativa. Si bé el concepte de la PR està ben definit en la literatura, s'ha abordat des de diferents perspectives, per la qual cosa els factors que s'han proposat per a explicar o moderar la PR també són molt diversos. Centrant-nos en els factors individuals (processos cognitius i emocionals) que influeixen en la PR, aquests poden afectar la forma en què s'aborden les situacions perilloses de dues maneres diferents. En primer lloc, poden esbiaixar la percepció d'una situació, de manera que no es duga a terme una avaluació adequada i per tant això porte a conductes esbiaixades. En segon lloc, aquests factors configuren una certa propensió general al risc en els éssers humans, de manera que poden o no sentir-se atrets per situacions potencialment perilloses. De la mateixa manera que la definició de la PR s'ha abordat des de diferents perspectives, la seua avaluació també s'ha tractat des de diferents punts de vista i constitueix hui dia un desafiament per a investigadors i professionals, per la qual cosa no s'ha trobat un consens clar quant a l'existència d'una mesura validada per a la PR. L'avaluació de la PR s'ha realitzat tradicionalment mitjançant qüestionaris; no obstant això, s'ha demostrat que aquestes mesures presenten diverses limitacions que poden conduir a resultats alterats. Les tasques comportamentals sorgeixen com una solució alternativa capaç de superar algunes d'aquestes barreres. En canvi, la seua capacitat de transferència a situacions de la vida real sembla ser limitada. La realitat virtual (RV) permet recrear situacions reals simulades per a realitzar avaluacions basades en l'acompliment. La RV presenta nombrosos avantatges que poden aportar beneficis per a l'avaluació dels comportaments humans, ja que aporta una major immersió, fidelitat i un major nivell d'implicació que els mètodes d'avaluació tradicionals, i nombrosos treballs en el camp de la psicologia aplicada i la neurociència organitzacional han avalat el seu ús per a avaluació humana. En aquesta investigació, proposem la RV com a tecnologia capaç de facilitar l'estudi dels processos de la PR, aprofitant les seues nombroses possibilitats, que es poden resumir com: simulació de situacions de risc realistes, interaccions naturals amb l'entorn virtual, inclusió de mesures implícites per a avaluació oculta i mesurament fisiològic en temps real. Aquesta tesi proporciona aportacions a la definició de la PR, particularment en la identificació de quins factors constitueixen aquest complex procés. A més, investiga l'ús de la RV immersiva en la investigació del comportament humà, específicament per a l'avaluació de la PR, proporcionant premisses de disseny d'entorns virtuals per a l'avaluació dels constructes psicològics identificats com a determinants per a definir la PR. Finalment, analitza la validesa de la RV en combinació amb mesures fisiològiques per a l'avaluació de la PR de manera implícita.[EN] Understanding human behaviour in risk situations, how individual and external factors influence our decisions and to what extent it is possible to influence and modify our behaviours, constitutes a challenge both for scientists and for society in general. From the perspective of Occupational Safety and Health (OSH), as well as in numerous fields such as sociology of finance, this topic has important implications since risk situations are a common aspect in various domains of our lives. Risk taking (RT) is part of the decision-making process in uncertain situations, in which the probability of each positive or negative consequence is known in advance. Although the concept of RT is well defined in the literature, it has been approached from different perspectives, so that the factors that have been proposed to explain or moderate RT are also very diverse. Focusing on the individual factors - cognitive and emotional processes - that influence RT, these may affect how hazardous situations are addressed in two different ways. First, they can skew the perception of a situation, so that an adequate evaluation is not carried out and therefore this leads to biased behaviors. Second, these factors shape a certain general propensity towards risk in humans, so that they may or may not be attracted to potentially dangerous situations. In the same way that the definition of RT has been approached from different perspectives, the evaluation of RT has also been treated from different points of view and nowadays constitutes a challenge for researchers and practitioners, so that a clear consensus has not been found regarding the existence of a validated measure for RT. RT evaluation has traditionally been carried out using questionnaires; however, it has been demonstrated that these measures present various limitations that can lead to altered results. Behavioural tasks emerge as an alternative solution capable of overcoming some of these boundaries. Instead, their ability to transference to real life situations appears to be limited. Virtual reality (VR) enables recreating real-simulated situations to carry out performance-based assessments. VR presents numerous advantages that can provide benefits for the evaluation of human behaviours, since it provides greater immersion, fidelity and a higher level of involvement than traditional evaluation methods, and numerous works in the field of applied psychology and organizational neuroscience have endorsed its use for human assessment. In this investigation, we propose VR as technology capable of facilitating the study of RT processes, taking advantage of its numerous possibilities, which can be resumed as: simulation of realistic risk situations, natural interactions with the virtual environment, inclusion of implicit measures for stealth assessment and physiological real-time measurement. This thesis provides novel contributions to the definition of RT, particularly in the identification of which factors constitute this complex process. Moreover, it investigates the use of immersive VR in human behaviour research, specifically for RT assessment, providing design premises of virtual environments for the evaluation of the psychological constructs identified as determinants to define RT. Finally, it analyses the validity of VR in combination with physiological measures for the evaluation of RT in an implicit way.Contrato predoctoral FPI (BES-2017-079857). Ministerio de Economía, Industria y Competitividad (Madrid, Spain).Juan Ripoll, CD. (2021). Virtual reality environments for the study of decision-making processes in risky contexts through the use of physiological measures and behavioural responses [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/171236TESISCompendi

Assisting Designers in the Anticipation of Future Product Use

En ligne sur le site de l'éditeur : http://www.aijstpme.kmutnb.ac.th/index.php?option=com_jdownloads&view=viewcategories&Itemid=1In this paper, we present some theories sover past decades describing interactions between designers and users, and a state of the art of methods and tools to support these interactions in user-centred design. We discuss related methodological issues as a first step toward the introduction of new methods to assist usercentred design, to avoid uses of the product which might have undesirable consequences, while leaving margins allowing users to adapt to the situation and potentially introduce further innovations within the product. Lastly, we discuss the concept of unforeseen use and introduce creativity methods to help designers anticipate these uses

Evaluating a VR System for Collecting Safety-Critical Vehicle-Pedestrian Interactions

Autonomous vehicles (AVs) require comprehensive and reliable pedestrian trajectory data to ensure safe operation. However, obtaining data of safety-critical scenarios such as jaywalking and near-collisions, or uncommon agents such as children, disabled pedestrians, and vulnerable road users poses logistical and ethical challenges. This paper evaluates a Virtual Reality (VR) system designed to collect pedestrian trajectory and body pose data in a controlled, low-risk environment. We substantiate the usefulness of such a system through semi-structured interviews with professionals in the AV field, and validate the effectiveness of the system through two empirical studies: a first-person user evaluation involving 62 participants, and a third-person evaluative survey involving 290 respondents. Our findings demonstrate that the VR-based data collection system elicits realistic responses for capturing pedestrian data in safety-critical or uncommon vehicle-pedestrian interaction scenarios.Comment: In submission to CHI 202

An Overview of Self-Adaptive Technologies Within Virtual Reality Training

This overview presents the current state-of-the-art of self-adaptive technologies within virtual reality (VR) training. Virtual reality training and assessment is increasingly used for five key areas: medical, industrial & commercial training, serious games, rehabilitation and remote training such as Massive Open Online Courses (MOOCs). Adaptation can be applied to five core technologies of VR including haptic devices, stereo graphics, adaptive content, assessment and autonomous agents. Automation of VR training can contribute to automation of actual procedures including remote and robotic assisted surgery which reduces injury and improves accuracy of the procedure. Automated haptic interaction can enable tele-presence and virtual artefact tactile interaction from either remote or simulated environments. Automation, machine learning and data driven features play an important role in providing trainee-specific individual adaptive training content. Data from trainee assessment can form an input to autonomous systems for customised training and automated difficulty levels to match individual requirements. Self-adaptive technology has been developed previously within individual technologies of VR training. One of the conclusions of this research is that while it does not exist, an enhanced portable framework is needed and it would be beneficial to combine automation of core technologies, producing a reusable automation framework for VR training