Relevant bibliographies by topics / Human behavior modelling

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Human behavior modelling'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Human behavior modelling"

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Wei, Lan, Chao Yang Zhao, and Wei Feng Yuan. "Modelling of the Human Behavior in Evacuation." Applied Mechanics and Materials 580-583 (July 2014): 2663–66. http://dx.doi.org/10.4028/www.scientific.net/amm.580-583.2663.

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Emergency evacuation is an important issue in fire safety. In this study, a cellular automaton (CA) model in which the human behaviour termed ‘flow with the stream’ is considered is proposed to simulate the procedure of emergency evacuation. Based on the CA model, the influence of the number of guiders to the evacuation from a large compartment is analyzed through numerical test. The result shows that the proposed CA model is a promising tool that may be used in fire safety design.
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Ивашов, А., A. Ivashov, Ю. Мандра, J. Mandra, Д. Зайцев, and D. Zaytsev. "Modelling of human teeth deformation behaviour after restoration." Actual problems in dentistry 12, no. 2 (July 26, 2016): 19–23. http://dx.doi.org/10.18481/2077-7566-2016-12-2-19-23.

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<p class="p1"><span class="s1">In the work the ef cacy of the adhesive dental restoration with different composites at monoaxial compression is tested. Deformation behavior of composite materials (SDR, Filtek P60, Filtek Ultimate Flowable, Filtek Ultimate) is compared in adhesive recording to a dentine at monoaxial compression. It is shown that mechanical properties of the composite materials are different because of their properties, but it is possible to predict their behavior in teeth after composite adhesive restoration. The highlled composites (to Filtek Ultimate and Filtek P60) are closer to dentine mechanical properties whereas the behavior of the lowlled composite materials (SDR and Filtek Ultimate Flowable) signi cantly differs from deformation behavior of a dentine. </span></p>
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Bourantas, Dimitris, and Irene I. Nicandrou. "Modelling post‐acquisition employee behavior: typology and determining factors." Employee Relations 20, no. 1 (February 1, 1998): 73–91. http://dx.doi.org/10.1108/01425459810369841.

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Understanding employee reactions to acquisitions is important in assessing the dynamics of acquisitions and their possible success or failure. Proposes a typology of employee behaviors in acquisitions. Moreover, describes the general framework for studying employee responses, by showing the relationship between the factors contributing to the formation of attitudes which can lead to a certain behavior. Finally, discusses directions for future research regarding human resource issues.
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Sundas Munir. "Cognitive Modelling for User Interface Design in HCI: A Comparative Analysis on Cognitive Models." Lahore Garrison University Research Journal of Computer Science and Information Technology 4, no. 2 (June 25, 2020): 117–29. http://dx.doi.org/10.54692/lgurjcsit.2020.0402138.

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This research aims to elaborate the cognition in the field of human-computer interaction, also acknowledges the cognitive modeling and human behavior processes. Cognitive modeling is a field of Human-Computer Interaction (HCI) which is used to design more efficient human interactive systems. It is used to model the interactive system in such a way that analysts can determine methods that users will interact with the system and also be used to understand the different processes of cognitive human behaviors. Hierarchal task analysis is a task to goal-based model, in which analyst selects the actions and tasks to perform. GOMS is a cognitive knowledge of the human information processing model in HCI that describes the user's cognitive architecture based on four components. The linguistic and grammatical model is a syntactical model in which languages and syntax are designed for the user for system communication in an interactive system. Cognitive human behavior processes are also described to understand the mutual coordination of cognition processes and cognitivemodels in designing an interactive system. Problem-solving is a cognitive process of the human mind to search for a problem and explore the possible solutions for that problem. Decision making is also a cognitive process of human behaviour in which human chooses an action from other alternatives based on certain criteria.
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Supriyanto, Achmad Sani, Vivin Maharani Ekowati, and Sofyan Aldi. "Predicting Modelling of Factors Influencing Innovative Work Behavior." Technium Social Sciences Journal 10 (August 3, 2020): 465–77. http://dx.doi.org/10.47577/tssj.v10i1.1349.

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The hospital success depends on the human resources. Hospital operational system can run well because of human resources support. Therefore, the hospital needs to empower human resources to achieve the organizational goals. One of the solutions is to initiate the Innovative Work Behavior. This study aims to examine the direct effect of work environment on Innovative Work Behavior and the effect of work environment on Innovative Work Behavior through work motivation. This study uses quantitative research. Data analysis uses the PLS (Partial Least Square). The population was all 200 employees or Malang Public Hospital. The sampling technique uses Simple Random Sampling, so the number of samples used is 133 employees. Data was collected by questionnaire. The study results indicated that work environment has a direct effect on Innovative Work Behavior and work environment has a indirect effect on Innovative Work Behavior through work motivation.
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Alrashed, Mohammed, and Jeff Shamma. "Agent Based Modelling and Simulation of Pedestrian Crowds in Panic Situations." Collective Dynamics 5 (August 12, 2020): A100. http://dx.doi.org/10.17815/cd.2020.100.

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The increasing occurrence of panic stampedes during mass events has motivated studying the impact of panic on crowd dynamics. Understanding the collective behaviors of panic stampedes is essential to reducing the risk of deadly crowd disasters. In this work, we use an agent-based formulation to model the collective human behavior in such crowd dynamics. We investigate the impact of panic behavior on crowd dynamics, as a specific form of collective behavior, by introducing a contagious panic parameter. The proposed model describes the intensity and spread of panic through the crowd. The corresponding panic parameter impacts each individual to represent a different variety of behaviors that can be associated with panic situations such as escaping danger, clustering, and pushing. Simulation results show contagious panic and pushing behavior, resulting in a more realistic crowd dynamics model.
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Nasrudin, Na’asah, Hafiszah Ismail, Yusfida Ayu Abdullah, and Nurul Shakila Khalid. "Pedestrian Crossing Behavior Model Based on Human Factor. Case Study: Shah Alam City, Malaysia." Environment-Behaviour Proceedings Journal 5, no. 13 (March 24, 2020): 329–35. http://dx.doi.org/10.21834/e-bpj.v5i13.2109.

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The study aimed to develop models for pedestrian crossings behaviour based on road traffic and human factors. A questionnaire distributed to 663 Shah Alam pedestrians. Respondents were asked to fill out a questionnaire on their perceptions of risks and attitudes concerning walking and road crossings. The modelling analysis showed that there is a significant relationship between the Human Factor and the Crossing Behavior, this study identified two components of the Human Factor that influenced the behaviour of the pedestrian crossing, namely the "risk-taker" and the "rule-follower." Analysis of pedestrian crossings behaviour useful to evaluate the implementation of new pedestrian crossing environments.
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Gridnev, S., Sergey Kurgalin, and Yaroslav Turovskiy. "Artificial neural networks abilities in human behavior and error modelling research." Актуальные направления научных исследований XXI века: теория и практика 3, no. 5 (November 24, 2015): 249–53. http://dx.doi.org/10.12737/16017.

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Takada, Yuji, Taku Naito, and Tetsuo Sawaragi. "Cognitive Causality Modelling for Human Behavior: The Method and Its Applications." IFAC Proceedings Volumes 47, no. 3 (2014): 9864–69. http://dx.doi.org/10.3182/20140824-6-za-1003.02117.

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Zhao, Lu, Nadir Farhi, Yeltsin Valero, and Zoi Christoforou. "Long Short-Time Memory Neural Networks for Human Driving Behavior Modelling." Transportation Research Procedia 72 (2023): 2589–96. http://dx.doi.org/10.1016/j.trpro.2023.11.786.

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Dissertations / Theses on the topic "Human behavior modelling"

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Poletti, Piero. "Human Behavior in Epidemic Modelling." Doctoral thesis, Università degli studi di Trento, 2010. https://hdl.handle.net/11572/367834.

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Mathematical models represent a powerful tool for investigating the dynamics of human infection diseases, providing useful predictions about the spread of a disease and the effectiveness of possible control measures. One of the central aspects to understand the dynamics of human infection is the heterogeneity in behavioral patters adopted by the host population. Beyond control measures imposed by public authorities, human behavioral changes can be triggered by uncoordinated responses driven by the diffusion of fear in the general population or by the risk perception. In order to assess how and when behavioral changes can affect the spread of an epidemic, spontaneous social distancing - e.g. produced by avoiding crowded environments, using face masks or limiting travels - is investigated. Moreover, in order to assess whether vaccine preventable diseases can be eliminated through not compulsory vaccination programs, vaccination choices are investigated as well. The proposed models are based on an evolutionary game theory framework. Considering dynamical games allows explicitly modeling the coupled dynamics of disease transmission and human behavioral changes. Specifically, the information diffusion is modeled through an imitation process in which the convenience of different behaviors depends on the perceived risk of infection and vaccine side effects. The proposed models allow the investigation of the effects of misperception of risks induced by partial, delayed or incorrect information (either concerning the state of the epidemic or vaccine side effects) as well. The performed investigation highlights that a small reduction in the number of potentially infectious contacts in response to an epidemic and an initial misperception of the risk of infection can remarkably affect the spread of infection. On the other hand, the analysis of vaccination choices showed that concerns about proclaimed risks of vaccine side effects can result in widespread refusal of vaccination which in turn leads to drops in vaccine uptake and suboptimal vaccination coverage.
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Poletti, Piero. "Human Behavior in Epidemic Modelling." Doctoral thesis, University of Trento, 2010. http://eprints-phd.biblio.unitn.it/422/1/tesi.pdf.

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Mathematical models represent a powerful tool for investigating the dynamics of human infection diseases, providing useful predictions about the spread of a disease and the effectiveness of possible control measures. One of the central aspects to understand the dynamics of human infection is the heterogeneity in behavioral patters adopted by the host population. Beyond control measures imposed by public authorities, human behavioral changes can be triggered by uncoordinated responses driven by the diffusion of fear in the general population or by the risk perception. In order to assess how and when behavioral changes can affect the spread of an epidemic, spontaneous social distancing - e.g. produced by avoiding crowded environments, using face masks or limiting travels - is investigated. Moreover, in order to assess whether vaccine preventable diseases can be eliminated through not compulsory vaccination programs, vaccination choices are investigated as well. The proposed models are based on an evolutionary game theory framework. Considering dynamical games allows explicitly modeling the coupled dynamics of disease transmission and human behavioral changes. Specifically, the information diffusion is modeled through an imitation process in which the convenience of different behaviors depends on the perceived risk of infection and vaccine side effects. The proposed models allow the investigation of the effects of misperception of risks induced by partial, delayed or incorrect information (either concerning the state of the epidemic or vaccine side effects) as well. The performed investigation highlights that a small reduction in the number of potentially infectious contacts in response to an epidemic and an initial misperception of the risk of infection can remarkably affect the spread of infection. On the other hand, the analysis of vaccination choices showed that concerns about proclaimed risks of vaccine side effects can result in widespread refusal of vaccination which in turn leads to drops in vaccine uptake and suboptimal vaccination coverage.
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Sun, Chao. "Human behavioural skills modelling and recognition." Electrical, Computer and Telecommunications Engineering - Faculty of Informatics, 2007. http://ro.uow.edu.au/theses/677.

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Human behaviour can be considered as the ensemble of various activities performed by an individual towards performing a particular task. There are many factors influencing human behaviour including culture, attitudes, emotions, values, ethics, and so on. In this work, the concept of 'human behaviour' in the context of human psycho-motor behaviour is studied. This work is primarily concerned with the development of a system to learn, distinguish and recognise various pre-defined human behavioural tasks. As an initial constraint, the challenging goal, subject to the limitation of hardware, is to model various human behaviours with only one integrated inertial sensor. The motions are captured with the sensor and recorded as streams of multi-dimensional sensory data, which are subsequently analysed into certain patterns. Since only one point on the human body can be measured with that sensor at a time, there are not sufficient motion data to enable the generation of new synthetic behaviours (which might be possible with multiple sensors). It is not really possible to develop a comprehensive model of complex behaviours under this condition. Thus, this work has focussed on building a system to model the behaviour of a specific part of the human body, and in turn to recognise and compare these behaviours. The experimental rig consists of an inertial sensor mounted on the subject providing kinematics data in real-time. Through this sensor, the behavioural motions are transformed into continuous streams of signals including Euler angles and accelerations in three spatial dimensions. Unsupervised machine learning algorithms and other techniques are implemented to analyse and build models of human behaviours in this work. An intrinsic classification algorithm called MML (Minimum Message Length encoding), and a popular unsupervised fuzzy clustering algorithm FCM (Fuzzy c-Means) are used to segment the complex data streams respectively, formulating inherent models of the dynamic modes they represent. Subsequent representation and analysis including FSM (Finite State Machines), DTW (Dynamic Time Warping), Kullback-Leibler divergence and Smith-Waterman sequence alignment have proved quite effective in distinguishing between behavioural characteristics that persist across a variety of tasks and multiple candidates. The hypothesis pursued in the thesis has been validated based on two machine learning algorithms for unsupervised learning namely MML and FCM. Each of these methods is capable of producing a range of primitives from the motion training data. However, the outcomes of regular expression and Dynamic Time Warping analysis results indicate that MML provides better results compared with the FCM algorithm in terms of identifying behaviours.
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Ghanipoor, Machiani Sahar. "Modeling Driver Behavior at Signalized Intersections: Decision Dynamics, Human Learning, and Safety Measures of Real-time Control Systems." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/71798.

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Traffic conflicts associated to signalized intersections are one of the major contributing factors to crash occurrences. Driver behavior plays an important role in the safety concerns related to signalized intersections. In this research effort, dynamics of driver behavior in relation to the traffic conflicts occurring at the onset of yellow is investigated. The area ahead of intersections in which drivers encounter a dilemma to pass through or stop when the yellow light commences is called Dilemma Zone (DZ). Several DZ-protection algorithms and advance signal settings have been developed to accommodate the DZ-related safety concerns. The focus of this study is on drivers' decision dynamics, human learning, and choice behavior in DZ, and DZ-related safety measures. First, influential factors to drivers' decision in DZ were determined using a driver behavior survey. This information was applied to design an adaptive experiment in a driving simulator study. Scenarios in the experimental design are aimed at capturing drivers learning process while experiencing safe and unsafe signal settings. The result of the experiment revealed that drivers do learn from some of their experience. However, this learning process led into a higher level of risk aversion behavior. Therefore, DZ-protection algorithms, independent of their approach, should not have any concerns regarding drivers learning effect on their protection procedure. Next, the possibility of predicting drivers' decision in different time frames using different datasets was examined. The results showed a promising prediction model if the data collection period is assumed 3 seconds after yellow. The prediction model serves advance signal protection algorithms to make more intelligent decisions. In the next step, a novel Surrogate Safety Number (SSN) was introduced based on the concept of time to collision. This measure is applicable to evaluate different DZ-protection algorithms regardless of their embedded methodology, and it has the potential to be used in developing new DZ-protection algorithms. Last, an agent-based human learning model was developed integrating machine learning and human learning techniques. An abstracted model of human memory and cognitive structure was used to model agent's behavior and learning. The model was applied to DZ decision making process, and agents were trained using the driver simulator data. The human learning model resulted in lower and faster-merging errors in mimicking drivers' behavior comparing to a pure machine learning technique.
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Slavík, Jiří. "Modelování umělého života." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2007. http://www.nusl.cz/ntk/nusl-412757.

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Artificial life modelling is an extensive subject to exert effort in one piece of work such as this work. For that reason this work is limited to keep the base line of modelling of human behavior. Presented work deal with a creation of an artificial agent whose human-like behavior is implemented according to the PECS reference model. Specification of the PECS reference model is implemented in Java to build a simulation of artificial human being in some environment. Modeled human being has been assigned to imitate human activities, react adequately to sensed events from the environment and change the environment to achieve its goals. Modeled human being is a behavior controled entity acting according to its current role.
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Gasparotto, Thomas. "Modélisation de l’évacuation des personnes en situation d’incendie." Thesis, Université de Lorraine, 2018. http://www.theses.fr/2018LORR0128/document.

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Ce travail, mené conjointement entre CNPP et le Laboratoire d’Énergétique et de Mécanique Théorique et Appliquée, est consacré à la mise en place d’un modèle d’évacuation de personnes, dans l’optique d’une application en Ingénierie de Sécurité Incendie. Le modèle de cheminement de personnes développé dans ce manuscrit est un modèle physique reposant sur une équation de conservation de la densité de personnes. Il est basé sur des hypothèses simples et réalistes résultant de l’observation de mouvements de foule, et utilise une vision macroscopique des personnes caractérisées par une densité moyenne. Ce modèle est mis en œuvre sur des cas de vérification et de comparaison issus de la littérature. Des expériences d’évacuation sont réalisées à échelle réelle afin de récolter des données quantitatives sur le mouvement des personnes et de valider de façon pertinente le modèle de cheminement de personnes. En outre, une stratégie est proposée afin d’intégrer dans la modélisation les contraintes thermiques et optiques liées au feu ainsi que leur impact sur le processus d’évacuation. Enfin, des simulations d’évacuation intégrant les effets du feu sont effectuées sur une configuration à grande échelle
This work was conducted as a collaboration between CNPP and the laboratory LEMTA. It was devoted to the implementation of an emergency egress model offering prospects for use in Fire Safety Engineering. The pedestrian movement model described in this manuscript is a physical model relying on a people density balance equation. This model is based on three fundamental assumptions resulting from pedestrian phenomena commonly observed, especially in crowds. Its mathematical formulation assumes that people are regarded as a mean density in a macroscopic way. The pedestrian model was tested on verification and comparison cases extracted from literature. Evacuation drills were also performed at real scale without fire constraints to collect some quantitative data like egress times or flows, and to validate the people motion model. Furthermore, a mathematical strategy is propounded in order to integrate thermal and optical stresses into the evacuation model and to take into consideration their incidence on evacuation processes. Finally, egress simulations are achieved on a large-scale configuration considering different scenarios involving fires
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Syed, Shazali Syed Tarmizi. "Human behaviour modelling through Human Intelligent Movement Software (HIMs)." Thesis, Loughborough University, 2010. https://dspace.lboro.ac.uk/2134/6382.

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The concept of sustainable urban environments aims to provide urban facilities including transport interchanges that can accommodate a wide spectrum of the human population irrespective of gender, age or disability. A major objective is to reduce levels of social exclusion which arise from inadequacy in infrastructure that strongly affects certain members of society such as the elderly, disabled and poor. This research focuses on the particular aspect of crowded public spaces where it is envisaged that improvements in crowd flow could be achieved by a proper consideration of all the users of the space but particularly the elderly and disabled. The ultimate objective would be design tools that provide architects with the means to achieve inclusivity in design for the elderly and disabled with relative ease and speed. Therefore, this research has developed a methodology and a computing tool to implement aspects of human walking behaviour in public spaces. Human behaviours have been studied using a large-scale video observation involving over 17,000 subjects. The videos have been analysed to determine a number of different behaviours and their relationship to distinguishing characteristics of the subjects such as age, gender and disability. Algorithms for representing these behaviours have been developed and implemented as a simulation tool (HIMs) within commercially available gaming software. Two case studies, within shopping malls and a bus station, have been carried out to illustrate the feasibility of the work and simple examples of small environmental design changes that significantly affect crowd flow are shown.
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Zhao, Ruiqi. "Spatial and temporal modelling for automatic human behavioral analysis." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1480683512602658.

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Toulgoat, Isabelle. "Modélisation du comportement humain dans les simulations de combat naval." Phd thesis, Université du Sud Toulon Var, 2011. http://tel.archives-ouvertes.fr/tel-00626811.

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Cette thèse porte sur la modélisation du comportement humain dans les simulations de combat naval. Au sein de l'entreprise DCNS, les simulations de combat naval permettent d'évaluer les performances opérationnelles des navires militaires, dans un scénario donné. Les simulations actuelles ne permettent pas de prendre en compte l'analyse et la décision d'un opérateur, qui peuvent parfois conduire à des réactions inattendues. Le but de cette thèse est donc de modéliser le comportement d'un opérateur pour les simulations de combats navals.Pour représenter les connaissances, la logique non monotone la plus employée a été utilisée: la logique des défauts. Une prise en compte du temps a été ajoutée à cette logique des défauts. La logique des défauts va permettre de calculer des extensions. Chaque extension correspond à une action possible pour l'opérateur.Une méthode qui permet de choisir une extension a été définie. Cette méthode simule la décision de l'opérateur et elle prend en compte le caractère de l'opérateur.
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Onyango, Stevine Obura. "Behaviour Modelling and System Control with Human in the Loop." Thesis, Paris Est, 2017. http://www.theses.fr/2017PESC1162/document.

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Malgré le progrès en recherche et développement dans le domaine de système autonome, de tels systèmes nécessitent l’intervention humaine pour résoudre les problèmes imprévus durant l’exécution des tâches par l’utilisateur.Il est donc nécessaire, malgré cette autonomie, de tenir compte du comportement du conducteur et il est difficile d’ignorer l’effet de l’intervention humaine dans le cadre de l’évolution continue de l’environnement et des préférences de l’utilisateur. Afin d’exécuter les opérations selon les attentes de l’opérateur, il est nécessaire d’incorporer dans la commande les besoins de l’utilisateur.Dans les travaux présentés dans cette thèse un modèle comportemental de l’utilisateur est développé et intégré dans la boucle de commande afin d’adapter la commande à l’utilisateur. Ceci est appliqué à la commande des fauteuils électrique et assiste dans la navigation du fauteuil dans un milieu encombré.Le développement du modèle comportemental est basé sur la méthode de potentielles orientées et la détection des obstacles et le comportement du conducteur vs de ces obstacles par l’adaptation duL’étude contribue également au développement d’un modèle dynamique du fauteuil utilisable dans des situations normales et exceptionnelles telle que le dérapage. Ce modèle est développé pour un le cas le plus courant des fauteuil avec roues arrière conductrices utilisant le formalisme Euler Lagrange avec les forces gravitationnelles et sur des surfaces inclinées.Dans la formulation de la commande, le modèle du conducteur est introduit dans la boucle de commande. L’optimalité de la performance est assurée par l’utilisation du commande prédictif généralisé pour le système en temps continue. Les résultats de la simulation démontrent l’efficacité de l’approche proposée pour l’adaptation de la commande au comportement du conducteur
Although the progressive research and development of autonomous systems is fairly evident, such systems still require human interventions to solve the unforeseen complexities, and clear the uncertainties encountered in the execution of user-tasks. Thus, in spite of the system's autonomy, it may not be possible to absolutely disregard the operator's role. Human intervention, particularly in the control of auto-mobiles, may as well be hard to ignore because of the constantly changing operational context and the evolving nature of the drivers' needs and preferences. In order to execute the autonomous operations in conformity with the operator's expectations, it may be necessary to incorporate the advancing needs and behaviour of the operator in the design. This thesis formulates an operator behaviour model, and integrates the model in the control loop to adapt the functionality of a human-machine system to the operator's behaviour. The study focuses on a powered wheelchair, and contributes to the advancement of steering performance, through background assistance by modelling, empirical estimation and incorporation of the driver's steering behaviour into the control system. The formulation of the steering behaviour model is based on two fundamentals: the general empirical knowledge of wheelchair steering, and the experimental steering data captured by a standard powered wheelchair, on both virtual and real environments. The study considers a reactive directed potential field (DPF) method in the modelling of drivers' risk detection and avoidance behaviour, and applies the ordinary least square procedure in the identification of best-fitting driver parameters. The study also contributes to the development of a dynamic model of the wheelchair, usable under normal and non-normal conditions, by taking into consideration the conventional differential drive wheelchair structure with two front castor wheels. Derivation of the dynamic model, based on the Euler Lagrange formalism, is carried out in two folds: initially by considering the gravitational forces subjected to the wheelchair on inclined configurations with no slipping situations, and finally by incorporating slipping parameters into the model. Determination of the slipping parameters is approached from the geometric perspective, by considering the non-holonomic motions of the wheelchair in the Euclidean space. In the closed-loop model, the input-output feedback controller is proposed for the tracking of user inputs by torque compensation. The optimality of the resulting minimum-phase closed-loop system is then ensured through the performance index of the non-linear continuous-time generalised predictive control (GPC). Simulation results demonstrate the expected behaviour of the wheelchair dynamic model, the steering behaviour model and the assistive capability of the closed-loop system
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Books on the topic "Human behavior modelling"

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Williams, Terry. Modelling complex projects. Chichester: Wiley, 2002.

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Williams, Terry. Modelling complex projects. Chichester: Wiley, 2002.

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Williams, Terry. Modelling complex projects. Chichester: Wiley, 2002.

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Modelling complex projects. Chichester: Wiley, 2002.

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C, Cacciabue Pietro, ed. Modelling driver behaviour in automotive environments: Critical issues in driver interactions with intelligent transport systems. London: Springer, 2007.

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Colloquium of Theoretical and Quantitative Geography (7th 1991 Stockholm). Modelling space and networks: Progress in theoretical and quantitative geography : proceedings from the 7th Colloquium of Theoretical and Quantitative Geography, Stockholm, September 1991. Umeå: Umeå universitet, 1997.

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Nakoinz, Oliver, and Daniel Knitter. Modelling Human Behaviour in Landscapes. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29538-1.

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Bernd, Schmidt. The modelling of human behaviour. Ghent: SCS Europe Publishing House, 2000.

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Cacciabue, Pietro Carlo. Modelling and Simulation of Human Behaviour in System Control. London: Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-1567-0.

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Cacciabue, Pietro Carlo. Modelling and Simulation of Human Behaviour in System Control. London: Springer London, 1998.

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Book chapters on the topic "Human behavior modelling"

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Lehmann, Hagen, and Pier Giuseppe Rossi. "Gestures in Educational Behavior Coordination. Grounding an Enactive Robot-Assisted Approach to Didactics." In Modelling Human Motion, 315–34. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46732-6_15.

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Liu, Andrew M. "Modeling Differences in Behavior Within and Between Drivers." In Human Modelling in Assisted Transportation, 15–22. Milano: Springer Milan, 2011. http://dx.doi.org/10.1007/978-88-470-1821-1_3.

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Bota, Florentin, and Dana Simian. "Embedding Human Behavior Using Multidimensional Economic Agents." In Modelling and Development of Intelligent Systems, 3–19. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68527-0_1.

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Kiss, Istvan Z. "Incorporating Human Behaviour in Epidemic Dynamics: A Modelling Perspective." In Modeling the Interplay Between Human Behavior and the Spread of Infectious Diseases, 125–37. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5474-8_8.

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Funk, Sebastian, and Vincent A. A. Jansen. "The Talk of the Town: Modelling the Spread of Information and Changes in Behaviour." In Modeling the Interplay Between Human Behavior and the Spread of Infectious Diseases, 93–102. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5474-8_6.

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Jayaswal, Bishrut. "Redesigning an Excavator Operator’s Seat and Controls Using Digital Human Modelling in RAMSIS." In Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Anthropometry, Human Behavior, and Communication, 45–58. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05890-5_4.

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Oliver, Nuria. "Urban Computing and Smart Cities: Opportunities and Challenges in Modelling Large-Scale Aggregated Human Behavior." In Lecture Notes in Computer Science, 16–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25446-8_2.

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Duca, Gabriella, and Antonella Frisiello. "Human Behavior Modelling in Socio-technical System Simulation: A Case Study on Air Traffic Control." In Springer Series in Design and Innovation, 140–47. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28390-1_15.

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Belavadi, Poornima, Laura Burbach, Martina Ziefle, and André Calero Valdez. "Finding a Structure: Evaluating Different Modelling Languages Regarding Their Suitability of Designing Agent-Based Models." In Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Human Body, Motion and Behavior, 201–19. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77817-0_16.

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Raywood-Burke, George, Laura M. Bishop, Phoebe M. Asquith, and Phillip L. Morgan. "Human Individual Difference Predictors in Cyber-Security: Exploring an Alternative Scale Method and Data Resolution to Modelling Cyber Secure Behavior." In HCI for Cybersecurity, Privacy and Trust, 226–40. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77392-2_15.

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Conference papers on the topic "Human behavior modelling"

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Simou, Ioulia, and Andreas Komninos. "Modelling human behavior during text entry." In MobileHCI '23: 25th International Conference on Mobile Human-Computer Interaction. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3565066.3609739.

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Li Wang, Zhi-qiu Lei, and Shao-cheng Qu. "The statistical study of human behavior in exercise mode." In 2011 International Conference on Modelling, Identification and Control. IEEE, 2011. http://dx.doi.org/10.1109/icmic.2011.5973715.

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Shi, Peng, Fei Liu, Ming Yang, and Zicai Wang. "A Fuzzy Rules-Based Approach to Analyzing Human Behavior Models." In 2009 11th International Conference on Computer Modelling and Simulation. IEEE, 2009. http://dx.doi.org/10.1109/uksim.2009.79.

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Chen, Yuxiao, Nauman Sohani, and Huei Peng. "Modelling of uncertain reactive human driving behavior: a classification approach." In 2018 IEEE Conference on Decision and Control (CDC). IEEE, 2018. http://dx.doi.org/10.1109/cdc.2018.8619568.

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Koutsomanis, Demetrios. "Artificial Intelligence for Ethology: Neural Networks in the Prediction of Human Behavior." In 2014 UKSim-AMSS 16th International Conference on Modelling and Simulation (UKSim). IEEE, 2014. http://dx.doi.org/10.1109/uksim.2014.106.

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Chaudhari, Kalpesh, Su Piao Sen Fabian, Nandha Kumar Kandasamy, Abhisek Ukil, and H. B. Gooi. "Agent-based modelling of EV energy storage systems considering human crowd behavior." In IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2017. http://dx.doi.org/10.1109/iecon.2017.8216909.

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Innocenti, M., A. Balluchi, and A. Balestrino. "New results on human operator modelling during nonlinear behavior in the control loop." In Proceedings of 16th American CONTROL Conference. IEEE, 1997. http://dx.doi.org/10.1109/acc.1997.609258.

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Yi, Ran, Jie Lin, and Huai-Ning Wu. "Adaptive Learning of Human Driving Behavior for Nonlinear Vehicle Systems via Fuzzy Modelling." In 2023 China Automation Congress (CAC). IEEE, 2023. http://dx.doi.org/10.1109/cac59555.2023.10451327.

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Balicki, Jerzy, Michal Beringer, Waldemar Korlub, Piotr Przybylek, Maciej Tyszka, and Marcin Zadroga. "Collective citizens' behavior modelling with support of the Internet of Things and Big Data." In 2015 8th International Conference on Human System Interactions (HSI). IEEE, 2015. http://dx.doi.org/10.1109/hsi.2015.7170644.

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Dijkstra, J. "An Agent Architecture for Visualizing Simulated Human Behavior to Support the Assessment of Design Performance." In 2008 International Conference on Computational Intelligence for Modelling Control & Automation. IEEE, 2008. http://dx.doi.org/10.1109/cimca.2008.58.

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