Que ce soit dans un contexte industriel ou quotidien, les robots deviennent de plus en plus présents dans notre environnement et sont désormais capables d'interagir avec des humains. Dans les milieux industriels, des robots viennent notamment assister les opérateurs des chaînes d'assemblage pour des tâches fatigantes et dangereuses. Robots et opérateurs sont alors amenés à partager le même espace physique (coprésence) et à effectuer des tâches en commun (collaboration). Alors que la sécurité des humains à proximité des robots doit être garantie à tout instant, il convient également de déterminer si le travail collaboratif est accepté par les opérateurs, en termes d'utilisabilité et d'utilité.La première problématique de la thèse consiste à déterminer quelles sont les composantes importantes rentrant en jeu dans l'acceptabilité de la collaboration homme-robot, du point de vue des opérateurs. Différents facteurs peuvent influencer cette acceptabilité : l'apparence des robots et leurs mouvements, la distance de sécurité ou encore le mode d'interaction avec le robot.Afin d'étudier le maximum de facteurs, nous proposons d'utiliser la réalité virtuelle pour mener des tests utilisateurs en environnement virtuel. Nous utilisons des questionnaires pour recueillir les impressions subjectives des opérateurs et des mesures physiologiques pour estimer leur état affectif (stress, effort). La deuxième problématique de la thèse consiste à déterminer si une méthodologie utilisant la réalité virtuelle est pertinente pour cette évaluation : les résultats issus des tests en environnement virtuel rendent-ils bien compte de la situation réelle ?Pour répondre aux problématiques de la thèse, trois cas d'étude ont été mis en place et quatre expérimentations ont été menées. Deux de ces expérimentations ont été reproduites à la fois en environnements réel et virtuel afin d'évaluer la pertinence des résultats issus de la situation virtuelle par rapport à la situation réelle. / Either in the context of the industry or of the everyday life, robots are becoming more and more present in our environment and are nowadays able to interact with humans. In industrial environments, robots now assist operators on the assembly lines for difficult and dangerous tasks. Then, robots and operators need to share the same physical space (copresence) and to manage common tasks (collaboration). On the one side, the safety of humans working near robots has to be guaranteed at all time. On the other hand, it is necessary to determine if such a collaborative work is accepted by the operators, in terms of usability and utility.The first problematic of the thesis consists in determining the important criteria that play a role in the acceptability, from the operators' point of view. Different factors can influence this acceptability: robot appearance, robot movements, safety distance or interaction modes with the robot.In order to study as many factors as possible, we intend to use virtual reality to perform user studies in virtual environments. We are using questionnaires to gather subjective impressions from operators and physiological measures to estimate their affective states (stress, effort). The second problematic of the thesis consists in determining if a methodology using virtual reality is relevant for this evaluation: can the results from studies in virtual environments be reproducible in equivalent physical situations?In order to answer the problematics of the thesis, three use cases have been implemented and four studies have been performed. Two of those studies rely on a physical situation and its virtual reality counterpart in order to evaluate the relevance of the results of the virtual situation compared to the real situation.

In the traffic domain it is commonly known that sleepiness is a highlycontributing factor in traffic accidents. Research has shown that sleepinessamong drivers is present in about 16-23 % of all car accidents. In the aviation andrailway industry a method or framework with some shared influences from theHuman Factors approach, called Fatigue Risk Management (FRM) has been usedto investigate how social and organisational factors affect the personnel’s level ofsleepiness. The overall aims of this study are to investigate how truck drivers’experience, fight and counteract sleepiness in their daily work environment. Theresults show that drivers face a wide variety of sleep contributing factors,stemming from both organisational factors and individual behaviour. Possibleways of counteracting truck driver sleepiness, concerning both the individual andthe organisation, are also suggested.

This simulator study aims to investigate if there is a relationship between self-awareness of driving ability and traffic safety behaviour. Self-awareness in this study is accurate self-evaluation of one’s abilities. By letting 97 participants (55-75 years old) drive the simulator and answering the Driver Skill Inventory (DSI; Warner et al., 2013) as well as the Multidimensional locus of control (T-loc; Özkan & Lajunen, 2005). A measure of self-awareness was computed using the residuals from regression line. Furthermore, this measure could show if a participant over-estimated or under-estimated their ability. Four self-awareness measures were made. The self-awareness measures were compared to traffic safety behaviour. Three different traffic safety measures were computed using specific events in the simulator scenario. The self-awareness measures were grouped into three groups; under-estimators, good self-awareness and over-estimators. These groups were then compared to each other with respect to traffic safety. A multivariate ANOVA was made to test for differences between the self-awareness groups but no significant main difference was found. The results showed no difference in traffic safety behaviour given the different levels of self-awareness. Furthermore, this could be a result of the old age of the sample group as self-awareness may only be relevant in a learning context. The conclusion of the study is that the analysis shows that there is no difference between over-estimators and under-estimators of driving ability, at least not in experienced older drivers.

The field of social network analysis has expanded from the field of social science to the fields of human factors and ergonomics. There is a theory that suggest that one can use the social network methods and create an information network which describes the network from an information sharing perspective and and there are also theories which describes how social network analysis can be used study cognitive maps (mental models). This thesis touches both of these subjects in an attempt to investigate how social network analysis can be used together with real-time information as a data source to investigate the cognitive maps of individuals and comparing these maps with an organisations expected structure based on protocols. The study conducted showed that it was indeed possible to change the social network analysis method into an information based network which explains the origin of a mental model and to study information be- haviour, in a network, but there are still variables which needs to be studied further (e.g. failed information sharing and temporal aspects of information sharing).

Doctorat en sciences psychologiques / info:eu-repo/semantics/nonPublished

L’interaction Humain-Robot est un domaine de recherche en pleine expansion parmi la communauté robotique. De par sa nature il réunit des chercheurs venant de domaines variés, tels que psychologie, sociologie et, bien entendu, robotique. Ensemble, ils définissent et dessinent les robots avec lesquels nous interagirons dans notre quotidien.Comme humains et robots commencent à travailler en environnement partagés, la diversité des tâches qu’ils peuvent accomplir augmente drastiquement. Cela créé de nombreux défis et questions qu’il nous faut adresser, en terme de sécurité et d’acceptation des systèmes robotiques.L’être humain a des besoins et attentes bien spécifiques qui ne peuvent être occultés lors de la conception des interactions robotiques. D’une certaine manière, il existe un besoin fort pour l’émergence d’une véritable interaction humain-robot ergonomique.Au cours de cette thèse, nous avons mis en place des méthodes pour inclure des critères ergonomiques et humains dans les algorithmes de prise de décisions, afin d’automatiser le processus de génération d’une interaction ergonomique. Les solutions que nous proposons se basent sur l’utilisation de fonctions de coût encapsulant les besoins humains et permettent d’optimiser les mouvements du robot et le choix des actions. Nous avons ensuite appliqué cette méthode à deux problèmes courants d’interaction humain-robot.Dans un premier temps, nous avons proposé une technique pour améliorer la lisibilité des mouvements du robot afin d’arriver à une meilleure compréhension des ses intentions. Notre approche ne requiert pas de modéliser le concept de lisibilité de mouvements mais pénalise les trajectoires qui amènent à une interprétation erronée ou tardive des intentions du robot durant l’accomplissement d’une tâche partagée. Au cours de plusieurs études utilisateurs nous avons observé un gain substantiel en terme de temps de prédiction et une réduction des erreurs d’interprétation.Puis, nous nous sommes attelés au problème du choix des actions et des mouvements qui vont maximiser l’ergonomie physique du partenaire humain. En utilisant une mesure d’ergonomie des postures humaines, nous simulons les actions et mouvements du robot et de l’humain pour accomplir une tâche donnée, tout en évitant les situations où l’humain serait dans une posture de travail à risque. Les études utilisateurs menées montrent que notre méthode conduit à des postures de travail plus sûr et à une interaction perçue comme étant meilleure. / Human-Robot Interaction (HRI) is a growing field in the robotic community. By its very nature it brings together researchers from various domains including psychology, sociology and obviously robotics who are shaping and designing the robots people will interact with ona daily basis. As human and robots starts working in a shared environment, the diversity of tasks theycan accomplish together is rapidly increasing. This creates challenges and raises concerns tobe addressed in terms of safety and acceptance of the robotic systems. Human beings havespecific needs and expectations that have to be taken into account when designing robotic interactions. In a sense, there is a strong need for a truly ergonomic human-robot interaction.In this thesis, we propose methods to include ergonomics and human factors in the motions and decisions planning algorithms, to automatize this process of generating an ergonomicinteraction. The solutions we propose make use of cost functions that encapsulate the humanneeds and enable the optimization of the robot’s motions and choices of actions. We haveapplied our method to two common problems of human-robot interaction.First, we propose a method to increase the legibility of the robot motions to achieve abetter understanding of its intentions. Our approach does not require modeling the conceptof legible motions but penalizes the trajectories that leads to late or mispredictions of therobot’s intentions during a live execution of a shared task. In several user studies we achievesubstantial gains in terms of prediction time and reduced interpretation errors.Second, we tackle the problem of choosing actions and planning motions that maximize thephysical ergonomics on the human side. Using a well-accepted ergonomic evaluation functionof human postures, we simulate the actions and motions of both the human and the robot,to accomplish a specific task, while avoiding situations where the human could be at risk interms of working posture. The conducted user studies show that our method leads to saferworking postures and a better perceived interaction.

Insecure cyber behavior of end users may expose their computers to cyber-attack. A first step to improve their cyber behavior is to identify their tendency toward insecure cyber behavior. Unfortunately, not much work has been done in this area. In particular, the relationship between end users cyber behavior and their personality traits is much less explored. This paper presents a comprehensive review of a newly developed, easily configurable, and flexible software SPICE for psychologist and cognitive scientists to study personality traits and insecure cyber behavior of end users. The software utilizes well-established cognitive methods (such as dot-probe) to identify number of personality traits, and further allows researchers to design and conduct experiments and detailed quantitative study on the cyber behavior of end users. The software collects fine-grained data on users for analysis.

Contemporary product and production development is typically carried out with the support of computer tools where the design of products and workstations are originated and evaluated within virtual environments. Ergonomics addresses factors important to consider in the product and production development process to ensure a good fit between humans and the items being designed. Digital human modelling (DHM) tools enable simulations and analyses of ergonomics in virtual environments. Anthropometry is central when using DHM tools for product and production development to ensure that the design fits the intended proportion of the targeted population from a physical perspective. Several methods have been prescribed to consider the anthropometric diversity that exists within human populations. Still many DHM based simulations in product and production development processes are done with approaches that are poor in representing anthropometric diversity. Hence, there is a need for better tools and methods that would support DHM tool users to more effectively and efficiently consider anthropometric diversity in the design process. In this thesis current methods for anthropometric diversity considerations have been reviewed and new methods and functionality have been developed and implemented in a DHM tool. Mathematical models have been developed to consider three specific parts important to the consideration of anthropometric diversity: generation of suitable test cases, prediction of missing anthropometric data and implementation of more diverse anthropometric variables such as strength and flexibility. Results show that the proposed methods are accurate and advantageous compared to approaches often used in industry today. The mathematical models for generation of suitable test cases and prediction of missing anthropometric data have been implemented in an anthropometric software module. The module has undergone usability testing with industry DHM tools users. The developed anthropometric module is shown to answer to relevant needs of DHM tool users and fit into the work processes related to DHM simulations and ergonomics analyses utilised in industry today.

In order to investigate the etiology of and evaluate interventions for work-related back injuries, researchers need efficient, accurate occupational exposure assessment methods suitable for large samples. The chapters in this thesis examine critical decisions using electromyography (EMG): How should exposure be measured? For what duration? Who should be measured, and how many times? Low-back EMG, or muscle activity data, was collected during 138 full-shift field measurements over 30 different job titles at 50 different worksites in 5 heavy industries: forestry, transportation, wood products, construction, and warehousing. Observations and self-reports of posture, manual materials handling (MMH), and driving exposures were collected concurrently. 1) Variability of EMG calibration measurements was investigated on right/left sides, multiple trials, 4 positions, and pre/post-shift. Position accounts for the majority of explained variability; there is little to gain by measuring multiple trials or pre- and post-shift, but measuring both sides and multiple positions is worthwhile. 2) Observation and self-report data were easier to collect and cheaper than the EMG direct measure. Costs and successful field performance need to be weighed against the added data detail when making choices about exposure assessment techniques for epidemiological studies. 3) Observed and self-reported exposures were used to predict EMG exposure metrics using mixed multiple linear regression models. Regression models using observed variables predicted 43-50% of the variability in the EMG metrics, while self-reported variables predicted 21%-36%. The observation exposure model provides a low-cost alternative to direct measurement. The self-reported exposure model should be considered with more caution. 4) Full-shift EMG data was resampled for 4, 2, and 1 hour, and for 10 and 2 minute durations to determine the optimal sampling duration. Bias was consistently low, but shorter durations had higher absolute error, percentage error, and limits of agreement. Durations of 4 and 2 hours may be acceptable but those less than 1 hour had large errors. 5) Components of EMG variance were calculated between- and within-subject, and between- industry, company, job, and post hoc grouping. Resolution, contrast, and exposure-response relationship attenuation were calculated for each grouping scheme. The post hoc scheme had the highest contrast and lowest resolution. / Medicine, Faculty of / Population and Public Health (SPPH), School of / Graduate

Introduction: Falls are the leading cause of death due to injury among the elderly. Every 24 minutes an older adult dies from a fall related injury. Studies using 3 different methods were performed at a large urban, academic medical center in the US. Aim #1: Understand the advantages and disadvantages of QI methodologies (Lean and Six Sigma) and HFE when applied to fall prevention in the acute care setting: o Evaluate the contribution of QI and HFE to fall prevention with a focus on reducing falls with serious injury. o Use studies with different methodologies (Lean, Six Sigma) to develop and implement an intervention with the goal of decreasing total falls and falls with injury. o Compare methodologies (Lean, Six Sigma and HFE) to understand their benefits and limitations. Aim #2: Develop recommendations for fall prevention: o Investigate interventions and assess success of fall prevention. o Develop an understanding of interventions that prevent falls resulting in injury. Methodology and Results: Study #1 (Method = Lean, Intervention = Standard Work): Study #1 used Lean techniques such as standard work to improve fall risk assessment and intervention selection. Total falls decreased by 22%. At first glance this appears successful but a deeper evaluation of the serious injuries revealed more improvement is needed. There were still 15 falls with serious injuries that occurred among the three oncology divisions. These rare but serious injuries result in a longer hospital stay and increased cost of treatment that is not reimbursed. Due to a climate of increasing financial pressure further reduction of serious injury was desired. Study #2 (Method = Six Sigma, Intervention = Patient Partnering: Study #2 used Six Sigma tools to investigate root causes of falls. An intervention called Patient Partnering was developed to encourage patients to call for help and participate in preventing their own falls. There were no falls with serious injury for over 14 consecutive months. However, the intervention was difficult to sustain due to resistance from nurses and patients. Falls with injuries resumed as the intervention ceased. Study #3 (Method = Qualitative HFE, Intervention = Patient Interview): Study #3 was a qualitative study based on Human Factors principles to understand patient s perception of fall risk. It was found that patients did not think they would fall and felt particularly safe and protected while in hospital. They found it difficult to get around with IV tubes and crowed spaces. They wanted information and assistance when they need it, in the format they prefer (customized for each individual patient). Impact on society: Falls prevention interventions need to be designed for all the stakeholders (patients and staff). Patients think nurses will keep them safe and are willing to participate with fall prevention if they feel it is tailored to their needs. Until all perspectives are taken into account it is unlikely that there will be sustained and embedded improvements. Key message: Falls with injury are rare events with complex root causes that require agile solutions with constant revision to align with rapidly changing conditions and interactions. Reducing injury will take a balance between safe environment, organization, processes, tasks and behaviors from staff and patients.