Literatura académica sobre el tema "Human intention and behavior analysis"
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Artículos de revistas sobre el tema "Human intention and behavior analysis"
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Takenaka, Kazuhito, Yasuo Nagasaka, Sayaka Hihara, Hiroyuki Nakahara, Atsushi Iriki, Yasuo Kuniyoshi y Naotaka Fujii. "Linear Discrimination Analysis of Monkey Behavior in an Alternative Free Choice Task". Journal of Robotics and Mechatronics 19, n.º 4 (20 de agosto de 2007): 416–22. http://dx.doi.org/10.20965/jrm.2007.p0416.
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When we observe people, we can often comprehend their intention from their behaviors. The intentions expressed by individuals can be considered as existing in interpersonal space and from a current social context. In our daily activity, choosing socially correct behavior through the observation of such social context is essential. However, it is not known how we can decode intention from another’s behavior. Here, we show how we can retrieve the intention of monkeys through external observation of their behavior patterns while performing alternative free choice tasks. We found that linear discriminant analysis on a monkey’s motion parameters could provide a discriminant score that appears to reflect the internal decision making process. The score showed a clear flexion point that we defined as a moment of outward expression of intention (OEI). This suggests that an alternative decision is made just before an OEI and that intention is expressed in the environment after this OEI in behavior, which in turn suggests that discriminant analysis may be useful in indicating how the brain implements nonverbal social communication. If we could embed the function in a human-machine interfaces, it could enable intuitive, smooth communication between machines and humans.
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Lin, Hsien-I., Fauzy Satrio Wibowo, Nurani Lathifah y Wen-Hui Chen. "Behavior Analysis for Increasing the Efficiency of Human–Robot Collaboration". Machines 10, n.º 11 (8 de noviembre de 2022): 1045. http://dx.doi.org/10.3390/machines10111045.
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In this study, we proposed a behavior analysis for increasing the efficiency of human–robot collaboration in an assembly task. This study was inspired by previous research, in which a set of operator intentions in assembly was translated into an intention graph to formulate a probabilistic decision model for planning robot actions in the presence of operator intention ambiguity and perception uncertainty. Here, we achieved improvement by considering the analysis of human behavior in the form of fatigue and adaptation ability. We also switched the collaboration scheme from cooperative to collaborative, in which both the robot and operator work in parallel, not sequentially. We then tested the proposed method with chair assembly and the results indicated that shortening the assembly duration increased the effectiveness of the assembly process. The results also indicated that the proposed method for assembling 50 chairs was 4.68 s faster than the previous method.
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Liu, Yaqin y Xinxing Luo. "Customer Value Co-Creation Behavior Effects on Online Purchase Intention". Journal of Advanced Computational Intelligence and Intelligent Informatics 23, n.º 2 (20 de marzo de 2019): 253–60. http://dx.doi.org/10.20965/jaciii.2019.p0253.
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Prior marketing literature highlights the customer value co-creation behavior on offline business. This paper focuses on investigating the effects of customer value co-creation behavior on the online purchase intention. Further, the hypothesis is tested via adopting the structural equation model method. The research shows that under the online shopping environment, the behaviors of customer participation and citizenship behavior have significant positive impacts on purchase intention and can be the direct antecedents of purchase intention. Compared with customer participation behavior, customer citizenship behavior has greater impacts on purchase intentions. The analysis outcome of the study has remarkable importance on improving the sales of online retailers.
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Wang, Haibo, Haiqing Si, Yao Li, Ting Pan, Yitong Zong y Naiqi Jiang. "Analysis of Dynamic Characteristics of Pilots Under Different Intentions in Complex Flight Environment". Promet - Traffic&Transportation 32, n.º 1 (13 de febrero de 2020): 153–66. http://dx.doi.org/10.7307/ptt.v32i1.3237.
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Intention is the main embodiment of human cerebral conscious activities, which has an important influence on guiding the realization of human behaviour. It is a vital prerequisite for analysing the dynamic characteristics of pilots with different intentions. Considering the intention law of the generation, transfer and reduction, this paper analyses dynamic characteristics of pilots with different intentions, starting from the factors of effect on the intention. Taking airfield traffic pattern as an example for simulating flight experiments, the pilot’s multi-source dynamic data of human – aircraft – environment system under different intentions and their psycho-physiological-physical characteristics were recorded. Based on Matlab, one-way analysis of variance was used to extract variables with significant changes, and the variables under different intentions were compared and analysed. The results show that the conventional pilots are more conducive to control the aircraft to keep a stable flight attitude. This study is of great significance for perfecting the warning system of flight safety and improving the pilot’s micro-behaviour assessment system.
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Kasmir Kasmir, Jakfar Jakfar y Parwoto Parwoto. "Turnover Intention Analysis In PMA Bogor". ePaper Bisnis : International Journal of Entrepreneurship and Management 1, n.º 2 (30 de junio de 2024): 129–42. http://dx.doi.org/10.61132/epaperbisnis.v1i2.71.
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The superiority of a company really depends on the human resources (HR) it has. Excellence in the field of human resources means that the company can retain, nurture and develop its quality employees from time to time in accordance with the standards they have set. Superior employees are those who have advantages compared to employees in other companies, for example abilities and behavior, so that they are able to achieve targets and even exceed what was previously set as their workload.The management of a company that has superior employees must be good at looking after, nurturing and retaining employees so that they continue to perform well and continue to survive in the company. Maintenance can be done by making employees feel happy and at home working in the company. Then by continuing to develop the competencies and talents they have, so that they have the ability, behavior and high loyalty to the company. If this is not done, it will certainly be a problem for employees to maintain and retain their employees.
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Suzuki, Satoshi y Fumio Harashima. "Estimation Algorithm of Machine Operational Intention by Bayes Filtering with Self-Organizing Map". Advances in Human-Computer Interaction 2012 (2012): 1–20. http://dx.doi.org/10.1155/2012/724587.
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We present an intention estimator algorithm that can deal with dynamic change of the environment in a man-machine system and will be able to be utilized for an autarkical human-assisting system. In the algorithm, state transition relation of intentions is formed using a self-organizing map (SOM) from the measured data of the operation and environmental variables with the reference intention sequence. The operational intention modes are identified by stochastic computation using a Bayesian particle filter with the trainedSOM. This method enables to omit the troublesome process to specify types of information which should be used to build the estimator. Applying the proposed method to the remote operation task, the estimator's behavior was analyzed, the pros and cons of the method were investigated, and ways for the improvement were discussed. As a result, it was confirmed that the estimator can identify the intention modes at 44–94 percent concordance ratios against normal intention modes whose periods can be found by about 70 percent of members of human analysts. On the other hand, it was found that human analysts' discrimination which was used as canonical data for validation differed depending on difference of intention modes. Specifically, an investigation of intentions pattern discriminated by eight analysts showed that the estimator could not identify the same modes that human analysts could not discriminate. And, in the analysis of the multiple different intentions, it was found that the estimator could identify the same type of intention modes to human-discriminated ones as well as 62–73 percent when the first and second dominant intention modes were considered.
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Kim, Siyeon, Sungjoo Hwang y Seok Hwan Hong. "Identifying shoplifting behaviors and inferring behavior intention based on human action detection and sequence analysis". Advanced Engineering Informatics 50 (octubre de 2021): 101399. http://dx.doi.org/10.1016/j.aei.2021.101399.
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Shin, Yeji, Seoyong Kim y Sohee Kim. "Searching for New Human Behavior Model in the Climate Change Age: Analyzing the Impact of Risk Perception and Government Factors on Intention–Action Consistency in Particulate Matter Mitigation". International Journal of Environmental Research and Public Health 19, n.º 17 (4 de septiembre de 2022): 11068. http://dx.doi.org/10.3390/ijerph191711068.
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This study aims to analyze factors influencing citizens’ intentions to take protective action against particulate matter (PM) and their actual actions in response to PM. There were few research on the role of government factors and the issue of intention–action inconsistency in the context of PM mitigation action. Therefore, this study set not only variables in the risk perception paradigm but also ones in government factors as independent variables, while intention and action in response to PM were set as dependent variables. This study’s analysis was based on survey data collected from Korean people. For representativeness of the samples, this study adopted the quota sampling method, considering region, gender, and age. Five hundred respondents finished the survey. To verify the hypotheses, this study used regression and binomial logistic analysis. Analysis showed that (1) negative emotions, trust, knowledge, government competency, policy satisfaction, and policy awareness had significant effects on intention and action in response to PM, and (2) perceived benefits only affected intention, whereas government accountability only affected action. Logistic analysis showed that there were groups in which intentions and actions did not match. Negative emotions and government competence induce intention–action consistency, whereas the perceived benefits and trust in government tend to encourage inconsistency. Knowledge is a variable that induces both consistency and inconsistency in the intention–action relationship. The determinant structures of independent variables affecting the likelihood of belonging to the four groups differed.
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Tandung, Juliana Caesaria. "The Link between HR Attributions and Employees’ Turnover Intentions". Gadjah Mada International Journal of Business 18, n.º 1 (19 de febrero de 2016): 55. http://dx.doi.org/10.22146/gamaijb.9287.
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Human Resources Management (HRM) is part of the organizational functions that contribute to the effectiveness of a firm’s performance, and brings an organization a competitive advantage through the implementation of its Human Resources (HR) practices. HR practices adopted by management are perceived or attributed subjectively by individual employees, and can in turn affect the employees’ attitudes and behavior (e.g. Job satisfaction and turnover intention). The purpose of this study is to contribute to the process-based approach by investigating the effect of HR attributions on turnover intentions, with job satisfaction playing a mediating role. The analysis is on the individual level, with 454 respondents from various organizations within the Netherlands. The results show that HR attributions can affect the turnover intention, through the presence of job satisfaction. Thus, it can be said that it is important to always consider the employees’ attitudes and behavior when examining their perception of HR practices, and in predicting their intention to leave.
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Yang, Kuo-Pin, Hsin-Hua Hsiung y Yu-Jen Chiu. "The comfort zone of the value circumplex for entrepreneurship: a structural analysis". Career Development International 20, n.º 6 (12 de octubre de 2015): 663–83. http://dx.doi.org/10.1108/cdi-07-2014-0087.
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Purpose – The purpose of this paper is to extend the attitudinal approach to entrepreneurial intentions by using a structural analysis to explore overlooked personal values as the antecedents of entrepreneurial attitude. Based on the widely adopted value system proposed by Schwartz, this study argues that while one cluster of personal values is positively correlated with entrepreneurial attitude that leads to entrepreneurial intention, another cluster of personal values is negatively correlated with entrepreneurial attitude. Design/methodology/approach – Questionnaire responses obtained from a sample of 276 MBA were analyzed using structural equation models to examine the influences of values on entrepreneurial intentions via entrepreneurial attitude. Findings – The results of this study demonstrate that personal values of self-direction, stimulation, achievement, and universalism are all positively correlated with entrepreneurial attitude, which together constitute a comfort zone for entrepreneurship, whereas values in the opposite end of the circumplex including benevolence, tradition, conformity, security, and power are negatively correlated with entrepreneurial attitude. The values that discourage the formation of an entrepreneurial attitude also counter the positive effect of entrepreneurial attitude on intention, making the relationship between entrepreneurial attitude and intention contingent upon value conflicts. Originality/value – This study regards entrepreneurship as a career development and contributes to the entrepreneurship study by differentiating the influences of a vital construct, i.e., personal values, which should not be regarded as a universalism. The value circumplex with a comfort and discomfort zone developed by this study can serve as a platform to help build the view on entrepreneurial intentions in terms of personal values.
Tesis sobre el tema "Human intention and behavior analysis"
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Zaier, Mayssa. "Context-aware pedestrian behavior agentification using innovative deep learning methods on video and 3D data". Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Lille Douai, 2024. http://www.theses.fr/2024MTLD0007.
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La prédiction de trajectoires de piétons a suscité une attention croissante en raison de son importance dans des applications telles que les véhicules autonomes et les systèmes de surveillance urbaine. L'objectif de cette thèse est de développer des modèles innovants pour prédire les trajectoires des piétons en se basant principalement sur des données vidéo et 3D acquises notamment dans des zones de conflit, c'est-à-dire des zones où les voies de circulation sont partagées entre piétons et autres usagers. Tout d'abord, nous introduisons un modèle basé sur l'attention qui exploite le contexte dynamique de la scène et un réseau bimodal de transformateurs pour améliorer la modélisation des interactions spatio-temporelles. Pour relever le défi complexe de la prédiction des mouvements humains en 3D, nous proposons un nouveau modèle capable de prédire les poses squelettiques en 3D à partir d'observations en 2D. Cette approche utilise une architecture encodeur-décodeur, combinant des réseaux de transformateurs et des LSTM pour modéliser efficacement le mouvement humain. Nous étendons ensuite notre modèle pour prédire les trajectoires de plusieurs piétons en intégrant des réseaux adversariaux génératifs (GAN) avec des techniques de prédiction de séquences, en incorporant à la fois les interactions sociales et le contexte environnemental. De plus, nous présentons un framework robuste pour représenter les poses des piétons dans l'espace géométrique pour la prédiction des mouvements humains en 3D. Cela implique l'exploration de modèles d'attention géométrique pour fournir des insights plus profonds sur les relations spatiales et la prédiction des mouvements. Nos modèles proposés, le Motion-Lie Transformer et le Kendall Manifold Transformer, utilisent respectivement des représentations d'algèbre de Lie et des techniques d'apprentissage profond sensibles à la géométrie. Des expériences approfondies menées sur plusieurs ensembles de données publiques démontrent que nos modèles surpassent les méthodes traditionnelles de prédiction de trajectoires et d'estimation de poses, même dans des scénarios très denses. De plus, l'approche peut être intégrée dans les systèmes de conduite autonome pour améliorer la sécurité des piétons en améliorant la prédiction des mouvements futurs. Enfin, cette thèse examine les limitations actuelles de la prédiction de trajectoires/mouvement dans les environnements urbains et propose des améliorations potentielles pour aborder des scénarios plus complexes, y compris le comportement imprévisible des piétonsPedestrian trajectory prediction has gained increasing attention due to its importance in applications such as autonomous vehicles and urban surveillance systems. The objective of this thesis is to develop innovative models for predicting pedestrian trajectories relying on mainly video and 3D data acquired in particular on conflict zones, namely zones where traffic lanes are shared between pedestrians and other users. First, we introduce an attention-based model that leverages dynamic scene context and a bimodal transformer network to enhance spatio-temporal interaction modeling. To tackle the complex challenge of 3D human motion prediction, we propose a novel model capable of predicting 3D skeleton poses from 2D observations. This approach utilizes an encoder-decoder architecture, combining transformer networks and LSTMs to effectively model human motion. We then extend our model to predict multi-pedestrian trajectories by integrating Generative Adversarial Networks (GANs) with sequence prediction techniques, incorporating both social interactions and environmental context. Furthermore, we present a robust framework to represent pedestrian poses in geometric space for 3D human motion prediction. This involves exploring geometric attention models to provide deeper insights into spatial relationships and motion prediction. Our proposed models, the Motion-Lie Transformer and Kendall Manifold Transformer, employ Lie algebra representations and geometry-aware deep learning techniques, respectively. Extensive experiments conducted on several public datasets demonstrate that our models outperform traditional trajectory prediction and pose estimation methods, even in highly dense scenarios. Additionally, the approach can be integrated into autonomous driving systems to enhance pedestrian safety by improving the prediction of future movements. Lastly, this thesis examines the current limitations of trajectory/motion prediction in urban environments and proposes potential improvements to address more complex scenarios, including unpredictable pedestrian behavior
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Wang, Wei. "Human Face and Behavior Analysis". Doctoral thesis, Università degli studi di Trento, 2018. https://hdl.handle.net/11572/367945.
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Human face and behavior analysis are very important research topics in the field of computer vision and they have broad applications in our everyday life. For instance, face alignment, face aging, face expression analysis and action recognition have been well studied and applied for security and entertainment. With these face analyzing techniques (e.g., face aging), we could enhance the performance of cross-age face verification system which now has been used for banks and electronic devices to recognize their clients. With the help of action recognition system, we could better summarize the user uploaded videos or generate logs for surveillance videos. This could help us retrieve the videos more accurately and easily.
The dictionary learning and neural networks are powerful machine learning models for these research tasks. Initially, we focus on the multi-view action recognition task. First, a class-wise dictionary is pre-trained which encourages the sparse representations of the between-class videos from different views to lie close by. Next, we integrate the classifiers and the dictionary learning model into a unified model to learn the dictionary and classifiers jointly.
For face alignment, we frame the standard cascaded face alignment problem as a recurrent process by using a recurrent neural network. Importantly, by combining a convolutional neural network with a recurrent one we alleviate hand-crafted features to learn task-specific features. For human face aging task, it takes as input a single image and automatically outputs a series of aged faces. Since human face aging is a smooth progression, it is more appropriate to age the face by going through smooth transitional states. In this way, the intermediate aged faces between the age groups can be generated. Towards this target, we employ a recurrent neural network. The hidden units in the RFA are connected autoregressively allowing the framework to age the person by referring to the previous aged faces. For smile video generation, one person may smile in different ways (e.g., closing/opening the eyes or mouth). This is a one-to-many image-to-video generation problem, and we introduce a deep neural architecture named conditional multi-mode network (CMM-Net) to approach it. A multi-mode recurrent generator is trained to induce diversity and generate K different sequences of video frames.
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Wang, Wei. "Human Face and Behavior Analysis". Doctoral thesis, University of Trento, 2018. http://eprints-phd.biblio.unitn.it/2925/1/phd_thesis.pdf.
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Human face and behavior analysis are very important research topics in the field of computer vision and they have broad applications in our everyday life. For instance, face alignment, face aging, face expression analysis and action recognition have been well studied and applied for security and entertainment. With these face analyzing techniques (e.g., face aging), we could enhance the performance of cross-age face verification system which now has been used for banks and electronic devices to recognize their clients. With the help of action recognition system, we could better summarize the user uploaded videos or generate logs for surveillance videos. This could help us retrieve the videos more accurately and easily.
The dictionary learning and neural networks are powerful machine learning models for these research tasks. Initially, we focus on the multi-view action recognition task. First, a class-wise dictionary is pre-trained which encourages the sparse representations of the between-class videos from different views to lie close by. Next, we integrate the classifiers and the dictionary learning model into a unified model to learn the dictionary and classifiers jointly.
For face alignment, we frame the standard cascaded face alignment problem as a recurrent process by using a recurrent neural network. Importantly, by combining a convolutional neural network with a recurrent one we alleviate hand-crafted features to learn task-specific features. For human face aging task, it takes as input a single image and automatically outputs a series of aged faces. Since human face aging is a smooth progression, it is more appropriate to age the face by going through smooth transitional states. In this way, the intermediate aged faces between the age groups can be generated. Towards this target, we employ a recurrent neural network. The hidden units in the RFA are connected autoregressively allowing the framework to age the person by referring to the previous aged faces. For smile video generation, one person may smile in different ways (e.g., closing/opening the eyes or mouth). This is a one-to-many image-to-video generation problem, and we introduce a deep neural architecture named conditional multi-mode network (CMM-Net) to approach it. A multi-mode recurrent generator is trained to induce diversity and generate K different sequences of video frames.
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Lee, Seung Ho. "INTEGRATED HUMAN DECISION BEHAVIOR MODELING UNDER AN EXTENDED BELIEF-DESIRE-INTENTION FRAMEWORK". Diss., The University of Arizona, 2009. http://hdl.handle.net/10150/193788.
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Modeling comprehensive human decision behaviors in a unified and extensible framework is quite challenging. In this research, an integrated Belief-Desire-Intention (BDI) modeling framework is proposed to represent the human decision behavior, whose submodules (Belief, Desire, Decision-Making, and Emotion modules) are based on a Bayesian belief network (BBN), Decision-Field-Theory (DFT), a probabilistic depth first search (PDFS) technique, and a BBN-reinforcement (Q-Learning) hybrid learning algorithm. A key novelty of the proposed model is its ability to represent various human decision behaviors such as decision-making, decision-planning, and learning in a unified framework.To this end, first, we extend DFT (a widely known psychological model for preference evolution) to cope with dynamic environments. The extended DFT (EDFT) updates the subjective evaluation for the alternatives and the attention weights on the attributes via BBN under the dynamic environment. To illustrate and validate the proposed EDFT, a human-in-the-loop experiment is conducted for a virtual stock market. Second, a new approach to represent learning (a dynamic evolution process of underlying modules) in the human decision behavior is proposed under the context of the BDI framework. Our research focuses on how a human adjusts his perception process (involving BBN) dynamically against his performance (depicted via a confidence index) in predicting the environment as part of his decision-planning. To this end, Q-learning is employed and further developed.To mimic realistic human behaviors, attributes of the BDI framework are reverse-engineered from human-in-the-loop experiments conducted in the Cave Automatic Virtual Environment (CAVE). The proposed modeling framework is demonstrated for a human's evacuation behaviors in response to a terrorist bomb attack. The constructed simulation has been used to test the impact of several factors (e.g., demographics, number of police officers, information sharing via speakers) on evacuation performance (e.g., average evacuation time, percentage of casualties).In addition, the proposed human decision behavior model is extended for decisions of many stakeholders that form a complex social network in the community-based development of software systems.To the best of our knowledge, the proposed human decision behavior modeling framework is one of the first efforts to represent various human decision behaviors (e.g., decision-making, decision-planning, dynamic learning) in a unified BDI framework.
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Zhao, Hongyang. "Motion Sensors-Based Human Behavior Recognition And Analysis". W&M ScholarWorks, 2020. https://scholarworks.wm.edu/etd/1593091889.
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Human behavior recognition and analysis have been considered as a core technology that can facilitate a variety of applications. However, accurate detection and recognition of human behavior is still a big challenge that attracts a lot of research efforts. Among all the research works, motion sensors-based human behavior recognition is promising as it is low cost, low power, and easy to carry. In this dissertation, we use motion sensors to study human behaviors. First, we present Ultigesture (UG) wristband, a hardware platform for detecting and analyzing human behavior. The hardware platform integrates an accelerometer, gyroscope, and compass sensor, providing a combination of (1) fully open Application Programming Interface (API) for various application development, (2) appropriate form factor for comfortable daily wear, and (3) affordable cost for large scale adoption. Second, we study the hand gesture recognition problem when a user performs gestures continuously. we propose a novel continuous gesture recognition algorithm. It accurately and automatically separates hand movements into segments, and merges adjacent segments if needed, so that each gesture only exists in one segment. Then, we apply the Hidden Markov Model to classify each segment into one of predefined hand gestures. Experiments with human subjects show that the recognition accuracy is 99.4% when users perform gestures discretely, and 94.6% when users perform gestures continuously. Third, we study the hand gesture recognition problem when a user is moving. We propose a novel mobility-aware hand gesture segmentation algorithm to detect and segment hand gestures. We also propose a Convolutional Neural Network to classify hand gestures with mobility noises. For the leave-one-subject-out cross-validation test, experiments with human subjects show that the proposed segmentation algorithm achieves 94.0% precision, and 91.2% recall when the user is moving. The proposed hand gesture classification algorithm is 16.1%, 15.3%, and 14.4% more accurate than state-of-the-art work when the user is standing, walking, and jogging, respectively. Finally, we present a tennis ball speed estimation system, TennisEye, which uses a racket-mounted motion sensor to estimate ball speed. We divide the tennis shots into three categories: serve, groundstroke, and volley. For a serve, we propose a regression model to estimate the ball speed. In addition, we propose a physical model and a regression model for both groundstroke and volley shots. Under the leave-one-subject-out cross-validation test, evaluation results show that TennisEye is 10.8% more accurate than the state-of-the-art work.
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Devanne, Maxime. "3D human behavior understanding by shape analysis of human motion and pose". Thesis, Lille 1, 2015. http://www.theses.fr/2015LIL10138/document.
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L'émergence de capteurs de profondeur capturant la structure 3D de la scène et du corps humain offre de nouvelles possibilités pour l'étude du mouvement et la compréhension des comportements humains. Cependant, la conception et le développement de modules de reconnaissance de comportements à la fois précis et efficaces est une tâche difficile en raison de la variabilité de la posture humaine, la complexité du mouvement et les interactions avec l'environnement. Dans cette thèse, nous nous concentrons d'abord sur le problème de la reconnaissance d'actions en représentant la trajectoire du corps humain au cours du temps, capturant ainsi simultanément la forme du corps et la dynamique du mouvement. Le problème de la reconnaissance d'actions est alors formulé comme le calcul de similitude entre la forme des trajectoires dans un cadre Riemannien. Les expériences menées sur quatre bases de données démontrent le potentiel de la solution en termes de précision/temps de latence de la reconnaissance d'actions. Deuxièmement, nous étendons l'étude aux comportements plus complexes en analysant l'évolution de la forme de la posture pour décomposer la séquence en unités de mouvement. Chaque unité de mouvement est alors caractérisée par la trajectoire de mouvement et l'apparence autour des mains, de manière à décrire le mouvement humain et l'interaction avec les objets. Enfin, la séquence de segments temporels est modélisée par un classifieur Bayésien naïf dynamique. Les expériences menées sur quatre bases de données évaluent le potentiel de l'approche dans différents contextes de reconnaissance et détection en ligne de comportementsThe emergence of RGB-D sensors providing the 3D structure of both the scene and the human body offers new opportunities for studying human motion and understanding human behaviors. However, the design and development of models for behavior recognition that are both accurate and efficient is a challenging task due to the variability of the human pose, the complexity of human motion and possible interactions with the environment. In this thesis, we first focus on the action recognition problem by representing human action as the trajectory of 3D coordinates of human body joints over the time, thus capturing simultaneously the body shape and the dynamics of the motion. The action recognition problem is then formulated as the problem of computing the similarity between shape of trajectories in a Riemannian framework. Experiments carried out on four representative benchmarks demonstrate the potential of the proposed solution in terms of accuracy/latency for a low-latency action recognition. Second, we extend the study to more complex behaviors by analyzing the evolution of the human pose shape to decompose the motion stream into short motion units. Each motion unit is then characterized by the motion trajectory and depth appearance around hand joints, so as to describe the human motion and interaction with objects. Finally, the sequence of temporal segments is modeled through a Dynamic Naive Bayesian Classifier. Experiments on four representative datasets evaluate the potential of the proposed approach in different contexts, including recognition and online detection of behaviors
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Hansen, Elis y Cecilia Holmblad. "Linjechefers intention att fullfölja de förändringar en Human Resource Transformation medfört". Thesis, Högskolan i Borås, Institutionen för Pedagogik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-20497.
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I föreliggande studie har det undersökts vad som påverkat linjechefer att fullfölja sina förändrade arbetsförhållanden. Förändringarna är ett resultat efter att en större omorganisation har genomförts, en så kallad Human Resource Transformation. Studien baserades på Icek Ajzens Theory of planned behavior, vars utgångspunkt är individens intention att utföra ett tänkt beteende. Det antogs, utifrån Ajzen (1991), att de tre faktorerna, attityd, subjektiv norm och upplevd beteendekontroll var av betydelse för intentionen. Sammanlagt svarade 42 linjechefer från Skatteverket på en enkät om attityder, subjektiva normer och upplevd beteendekontroll gentemot de nya arbetsuppgifterna. En kvalitativ informantintervju genomfördes med en HR-strateg vid Skatteverket för att förtydliga syfte och orsak till organisationsförändringen samt för att tydliggöra hur linjechefernas arbete var tänk att se ut efter att HRT implementerats. Resultatet visade att subjektiv norm är den faktor som hade mest betydelse för intentionen att fullfölja HRT. Attityd mot beteendet och upplevd beteendekontroll visade sig inte ha signifikant samband med intentionen.The present study has examined what factors influence line managers intention towards changing employment. The changes are a result after a major organizational development, known as Human Resources Transformation. The study is based on Icek Ajzens Theory of planned behavior who’s main core is the individual’s intention to carry out a planned behavior. Ajzen (1991) assumes that tree factors affect the intention toward behavior. A total of 42 line managers answered on a survey based on questions about attitudes, subjective norms and perceived behavioral control related to their new work situation. A qualitative informantinterview was conducted with an HR-strategist at the Swedish tax agency to clarify the purpose and reason for the reorganization and to clarify HRTs real impact in the business. The result shows that subjective norm is the element with the most impact on the intention to proceed with HRT. Attitude toward behavior and perceived behavioral control was found to have no significant correlation with intention.
Program: Organisations- och personalutvecklare i samhället
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Woods-Groves, Suzanne. "An exploratory factor analysis of the human behavior rating scale". Auburn, Ala., 2007. http://repo.lib.auburn.edu/07M%20Dissertations/WOODS_SUZANNE_12.pdf.
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Ciptadi, Arridhana. "Interactive tracking and action retrieval to support human behavior analysis". Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/54987.
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The goal of this thesis is to develop a set of tools for continuous tracking of behavioral phenomena in videos to support human behavior study. Current standard practices for extracting useful behavioral information from a video are typically difficult to replicate and require a lot of human time. For example, extensive training is typically required for a human coder to reliably code a particular behavior/interaction. Also, manual coding typically takes a lot more time than the actual length of the video (e.g. , it can take up to 6 times the actual length of the video to do human-assisted single object tracking. The time intensive nature of this process (due to the need to train expert and manual coding) puts a strong burden on the research process. In fact, it is not uncommon for an institution that heavily uses videos for behavioral research to have a massive backlog of unprocessed video data. To address this issue, I have developed an efficient behavior retrieval and interactive tracking system. These tools allow behavioral researchers/clinicians to more easily extract relevant behavioral information, and more objectively analyze behavioral data from videos. I have demonstrated that my behavior retrieval system achieves state-of-the-art performance for retrieving stereotypical behaviors of individuals with autism in a real-world video data captured in a classroom setting. I have also demonstrated that my interactive tracking system is able to produce high-precision tracking results with less human effort compared to the state-of-the-art. I further show that by leveraging the tracking results, we can extract an objective measure based on proximity between people that is useful for analyzing certain social interactions. I validated this new measure by showing that we can use it to predict qualitative expert ratings in the Strange Situation (a procedure for studying infant attachment security), a quantity that is difficult to obtain due to the difficulty in training the human expert.
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Zhang, Huiqi. "Socioscope: Human Relationship and Behavior Analysis in Mobile Social Networks". Thesis, University of North Texas, 2010. https://digital.library.unt.edu/ark:/67531/metadc30533/.
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The widely used mobile phone, as well as its related technologies had opened opportunities for a complete change on how people interact and build relationship across geographic and time considerations. The convenience of instant communication by mobile phones that broke the barrier of space and time is evidently the key motivational point on why such technologies so important in people's life and daily activities. Mobile phones have become the most popular communication tools. Mobile phone technology is apparently changing our relationship to each other in our work and lives. The impact of new technologies on people's lives in social spaces gives us the chance to rethink the possibilities of technologies in social interaction. Accordingly, mobile phones are basically changing social relations in ways that are intricate to measure with any precision. In this dissertation I propose a socioscope model for social network, relationship and human behavior analysis based on mobile phone call detail records. Because of the diversities and complexities of human social behavior, one technique cannot detect different features of human social behaviors. Therefore I use multiple probability and statistical methods for quantifying social groups, relationships and communication patterns, for predicting social tie strengths and for detecting human behavior changes and unusual consumption events. I propose a new reciprocity index to measure the level of reciprocity between users and their communication partners. The experimental results show that this approach is effective. Among other applications, this work is useful for homeland security, detection of unwanted calls (e.g., spam), telecommunication presence, and marketing. In my future work I plan to analyze and study the social network dynamics and evolution.
Libros sobre el tema "Human intention and behavior analysis"
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Salah, Albert Ali y Theo Gevers, eds. Computer Analysis of Human Behavior. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-994-9.
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Ahad, Md Atiqur Rahman, Sozo Inoue, Guillaume Lopez y Tahera Hossain. Human Activity and Behavior Analysis. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781032636054.
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Ahad, Md Atiqur Rahman, Sozo Inoue, Guillaume Lopez y Tahera Hossain. Human Activity and Behavior Analysis. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003371540.
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Theo, Gevers y SpringerLink (Online service), eds. Computer Analysis of Human Behavior. London: Springer-Verlag London Limited, 2011.
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Yu, Zhiwen y Zhu Wang. Human Behavior Analysis: Sensing and Understanding. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2109-6.
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Beasley, Melanie M. y Andrew D. Somerville, eds. Exploring Human Behavior Through Isotope Analysis. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32268-6.
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Cooper, John O. Applied behavior analysis. Columbus: Merrill Pub. Co., 1987.
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C, Leslie Julian y Blackman Derek E, eds. Experimental and applied analysis of human behavior. Reno: Context Press, 2000.
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C, Troutman Anne, ed. Applied behavior analysis for teachers. 9a ed. Upper Saddle River, N.J: Merrill, 2013.
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Lundin, Robert W. Personality: A behavioral analysis. 2a ed. Malabar, Fla: R.E. Krieger Pub. Co., 1986.
Buscar texto completoCapítulos de libros sobre el tema "Human intention and behavior analysis"
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Spehr, Jens. "Human Behavior Analysis". En Studies in Systems, Decision and Control, 135–59. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11325-8_7.
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Salah, Albert Ali, Ben J. A. Kröse y Diane J. Cook. "Behavior Analysis for Elderly". En Human Behavior Understanding, 1–10. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24195-1_1.
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Jalali, Laleh, Hyungik Oh, Ramin Moazeni y Ramesh Jain. "Human Behavior Analysis from Smartphone Data Streams". En Human Behavior Understanding, 68–85. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46843-3_5.
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Conigliaro, Davide, Francesco Setti, Chiara Bassetti, Roberta Ferrario y Marco Cristani. "ATTENTO: ATTENTion Observed for Automated Spectator Crowd Analysis". En Human Behavior Understanding, 102–11. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02714-2_9.
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Ozkan, Derya y Louis-Philippe Morency. "Concensus of Self-features for Nonverbal Behavior Analysis". En Human Behavior Understanding, 75–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14715-9_8.
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Baur, Tobias, Ionut Damian, Florian Lingenfelser, Johannes Wagner y Elisabeth André. "NovA: Automated Analysis of Nonverbal Signals in Social Interactions". En Human Behavior Understanding, 160–71. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02714-2_14.
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Wei, Haolin, David S. Monaghan, Noel E. O’Connor y Patricia Scanlon. "A New Multi-modal Dataset for Human Affect Analysis". En Human Behavior Understanding, 42–51. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11839-0_4.
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Ullah, Habib, Mohib Ullah y Nicola Conci. "Dominant Motion Analysis in Regular and Irregular Crowd Scenes". En Human Behavior Understanding, 62–72. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11839-0_6.
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Yu, Zhiwen y Zhu Wang. "Individual Behavior Recognition". En Human Behavior Analysis: Sensing and Understanding, 37–137. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2109-6_5.
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Yu, Zhiwen y Zhu Wang. "Group Behavior Recognition". En Human Behavior Analysis: Sensing and Understanding, 139–218. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2109-6_6.
Texto completoActas de conferencias sobre el tema "Human intention and behavior analysis"
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Wang, Longfeng, Yutong Liu, Mingyan Wang, Qinghua Chen, Fengting Wang y Jiawei Chen. "Human Travel Behavior Analysis Using Private Car Data". En 2024 5th International Conference on Artificial Intelligence and Electromechanical Automation (AIEA), 410–14. IEEE, 2024. http://dx.doi.org/10.1109/aiea62095.2024.10692794.
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Feit, Andrew y Berenice Mettler. "Information-Based Analysis of Visual Cues in Human Guidance". En Vertical Flight Society 72nd Annual Forum & Technology Display, 1–13. The Vertical Flight Society, 2016. http://dx.doi.org/10.4050/f-0072-2016-11578.
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Perception plays a central role in such humans' motion guidance skills as precision rotorcraft landing or driving a car. This paper investigates the information available in visible cues, relative to a first-person motion guidance task, in terms of sensory-motor guidance primitives. Human subjects performed a motion guidance task in a 3D simulation system, using only visual information. Guidance primitive patterns were identified from recorded subject motion and perception behavior. Information transfer between visual measurements and vehicle motion is quantified from the resulting data to identify both model-based and nonrepresentational guidance strategies. Results show that subjects use separate guidance primitive modes during distinct phases of a trajectory. This understanding of perceptual and guidance primitive elements in human behavior has applications ranging from improved augmented cuing for human pilots to advancing artificial perception in autonomous vehicle systems.
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Chen, Wen-Chi y Chih-Fu Wu. "Rehabilitation behavior intention of upper extremity stroke patients by IMVT". En 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003870.
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In recent years, studies have shown that mirror therapy can improve the rehabilitation effect of upper extremity stroke patients. On the other hand, with the advancement of virtual reality technology, relevant rehabilitation medical researchers have widely introduced virtual reality technology into the rehabilitation medical system. This pilot study investigates the effect of the integration of mirror therapy and virtual reality technology (IMVT) with somatosensory game elements on upper extremity stroke patients. Based on research ethics, patients with upper extremity stroke were first consulted about their willingness to participate in the experiment, and the experiment was carried out with the patients’ consents. In this preliminary study, a total of 38 upper limb stroke patients were willing to participate in this ethical research investigation. Thirty-eight patients included 21 males and 17 females, with an average age of 53.8 and 55.4, respectively, are shown a demonstration video, made by the physical health personnel, of how to operate the equipment integrating mirror therapy and virtual reality before participating the experiment. The upper limb stroke patients who agreed to participate in the experiment after browsing the video are given a questionnaire survey on the rehabilitation behavior intention of the patients. The questionnaire used in this research is based on the literature survey of extending the unified theory of acceptance and use of technology II(UTAUT2) proposed by Venkatesh et al. (2012), and the questionnaire items are selected through interviews with rehabilitation medical experts from accredited institutions. The content of the questionnaire included five exogenous variables, namely, "performance expectation", "effort expectation", "social influence", "hedonic motivation" and "game setting", one endogenous variable of "behavioral intention" as well as twenty indicators. The statistical tool PLS-smart was used to perform PLS-SEM (Structural Equation Modeling) analysis on the data. The analysis results found that "hedonic motivation" has a significant impact on "behavior intention" in the SEM model. According to patient interviews and SEM analysis, it can be inferred that the increase in content of hedonic motivation can improve patients’ rehabilitation behavior intentions. In contrast, since upper limb stroke patients still question this novel rehabilitation method, performance expectations, effort expectations, social influence, as well as, game setting, are insignificant in behavioral intention.
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Liu, Charles Z., Hasan Aliamani y Manolya Kavakli. "Behavior-intention analysis and human-aware computing: Case study and discussion". En 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2017. http://dx.doi.org/10.1109/iciea.2017.8282899.
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Zhang, Xinyao, Sibo Tian, Xiao Liang, Minghui Zheng y Sara Behdad. "Multi-Task Learning for Intention and Trajectory Prediction in Human-Robot Collaborative Disassembly Tasks". En ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/detc2024-143753.
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Abstract Human-robot collaboration (HRC) has become an integral element of many industries, including manufacturing. A fundamental requirement for safe HRC is to understand and predict human intentions and trajectories, especially when humans and robots operate in close proximity. However, predicting both human intention and trajectory components simultaneously remains a research gap. In this paper, we have developed a multi-task learning (MTL) framework designed for HRC, which processes motion data from both human and robot trajectories. The first task predicts human trajectories, focusing on reconstructing the motion sequences. The second task employs supervised learning, specifically a Support Vector Machine (SVM), to predict human intention based on the latent representation. In addition, an unsupervised learning method, Hidden Markov Model (HMM), is utilized for human intention prediction that offers a different approach to decoding the latent features. The proposed framework uses MTL to understand human behavior in complex manufacturing environments. The novelty of the work includes the use of a latent representation to capture temporal dynamics in human motion sequences and a comparative analysis of various encoder architectures. We validate our framework through a case study focused on a HRC disassembly desktop task. The findings confirm the system’s capability to accurately predict both human intentions and trajectories.
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Alcain, Jesselyn, Charlie Marquez, Lara Camille Beriña, Luke Samuel Bulaon, Jane Andrea Cheng y Gennes Erika Cruz. "Predictive Model Establishment for The Online Shopping Experience Factors Affecting Repurchase Intention Using Stepwise Linear Regression". En 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1002553.
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The COVID-19 pandemic has brought drastic changes in the way people purchase goods/products. The behavior of people shifted to online shopping and an increase in electronic transactions. In the present, the e-commerce industry in the Philippines continues to grow; however, it is still not as established as developed countries. For online businesses to succeed in the highly competitive online environment, a better understanding of the different factors affecting consumer behavior is vital. This study investigated the e-commerce sector wherein the focus is the online shopping factors that affect the repurchase intention of consumers from multiple generations, namely Baby Boomers, Generation X, Generation Y, and Generation Z. Seven online shopping experience factors were determined in the study affecting consumers' repurchase intention. The study used stepwise linear regression and Cronbach's alpha for data analysis, and the predictive models were validated. The results show that Customer Satisfaction and Perceived Usefulness affect the online repurchase intention for Baby Boomers. At the same time, Perceived Security, Transactionality, and Website Quality are the factors that affect Generation X's repurchase intention. Furthermore, Availability, Perceived Usefulness, and Website Quality affect repurchase intention for Generation Y. Lastly, Perceived Ease of Use, Perceived Security and Customer Satisfaction affects the repurchase intention for Generation Z. Aside from repurchase intention, these factors also significantly affect online store revisit. These results will be helpful for e-commerce company owners in improving the features of their platforms based on consumers' preferences. Recommendations include for e-commerce sites to improve cybersecurity, and user-friendly interface, convenient payment methods, clear refund or return policies, and consistent monitoring and updating of product stocks.
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Klostermann, Marina y Lina Kluy. "Privacy Concerns in Recommender Systems for Personalized Learning at the Workplace: The Mediating Role of Perceived Trustworthiness". En 13th International Conference on Human Interaction & Emerging Technologies: Artificial Intelligence & Future Applications. AHFE International, 2025. https://doi.org/10.54941/ahfe1005906.
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Artificial intelligence (AI) is capable of reconfiguring activities in Human Resource Management (HRM), including talent acquisition, performance management and learning and development (Minbaeva, 2021). The integration of AI into HRM systems can optimize processes, such as comprehensive needs assessments for learning and development, which would otherwise be lengthy and time-consuming. Moreover, the integration of AI in HRM has the potential to enhance decision-making processes and employee experience (Strohmeier, 2020). The use of big data and personal information in AI-based HRM systems to provide employees with personalized learning recommendations gives rise to privacy concerns. These concerns must be addressed in order to guarantee a responsible and calibrated use of these technologies. In the event that users express concerns regarding the adequate protection of their personal information by the system, they may perceive the system as untrustworthy and, consequently, refrain from using the system. In the context of privacy concerns, trust(worthiness) is assumed to be one of the most crucial predictors of behavior (e.g., intention to use a system). However, the explicit role of perceived trustworthiness in the relationship between privacy concerns and the intention to use an AI-based system has yet to be demonstrated. The aim of the present study was to investigate whether there exists a mediating effect of perceived trustworthiness on the relation between privacy concerns and the intention to use an AI-based recommender system for workplace learning. An online experiment was developed to simulate such a system. The analysis of this study is based on data of 69 participants (employees, 29 female, age M = 33.28 years, SD = 10.49) from one of the two experimental conditions, in which they were permitted to determine which personal information to provide for a personalized learning recommendation. The mean interaction time with the recommender system was 43.23 minutes (SD = 18.64). The participants completed questionnaires addressing a range of different constructs, including perceived trustworthiness, privacy concerns and intention to use. Contrary to previous studies postulating privacy concerns as a predictor of privacy behavior, the analysis showed no direct effect of privacy concerns on intention to use the system (B = -0.001, p > .05). However, the results indicated that privacy concerns significantly predicted perceived trustworthiness (B = -0.170, p < .05), which in turn significantly predicted the intention to use the system (B = 0.936, p < .01). Therefore, privacy concerns exert an indirect influence on the intention to use the system through perceived trustworthiness. The results underscore the significance of perceived trustworthiness in the context of privacy concerns and the intention to use an AI-based recommender system for workplace learning. This study represents a preliminary step towards addressing the research gap on the role of trust(worthiness) in the context of privacy concerns, as proposed by previous studies. Implications can be derived for the design of human-centered recommender systems for workplace learning, taking into account increasing perceived trustworthiness and reducing privacy concerns. Future research should continue to investigate additional factors in the relationship of privacy concerns, attitudes and behavior, for instance, perceived control over personal information.
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Barbarini, Luiz Henrique Maiorino y Bernardo Luis Rodrigues de Andrade. "Considerations About Human Factors in Risk Analysis for Ships With Alarm System". En ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-38128.
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In complex systems, such as vessels and commercial ships, the ability to understand and influence human behavior is essential to ensure safety and reliability. According to statistics of classification societies (Rothblum, 2000; ALERT!, 2003), humans are largely responsible for accidents on board and, therefore, are considered a major component of the dependability of vessels. This paper presents a proposal on how to address the human and organizational factors in risk analysis for ships, focusing on the scenarios where the crew interacts with the alarm and monitoring system. In order to illustrate the steps and assumptions to be done by an analyst applying the proposed model, the accident of the vessel Maersk Doha occurred in October of 2006 in the United States, is analyzed. The report on the investigation of this accident is public and accessible via the Internet site of the Marine Accident Investigation Branch – MAIB. This approach has the intention to be a proactive tool to anticipate and prevent accidents on board by means of the correct design and use of the ship’s alarm system.
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Zhang, Hui, Xinjie Liang y Yingping Cao. "The Influence of Background Music Popularity on Consumer Online Shopping Behavior: A Model Analysis". En 15th International Conference on Applied Human Factors and Ergonomics (AHFE 2024). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1005414.
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With the popularity of online shopping, online e-commerce is paying more attention to the impact of sensory marketing on consumer behavior. Previous studies suggest that music plays a significant role in enhancing the online shopping experience for consumers. However, the specific patterns of consumer impulse that music induces are yet to be fully discovered. Therefore, the study aims to explore the influence of music popularity on online shopping behavior in reference to the familiarity on emotion regulation. The results can create a better auditory environment for users' shopping experience and give a guidance for merchants to choose music that fits their brand positioning.MethodThirty participants (15 females) between the ages of 19 and 33 (M = 23.8, SD = 3.2) were recruited and all had years of online shopping experience. Participants were evenly assigned to three groups(no-music group, non-pop music group, and pop music group). The task scenario of participants is to buy a pair of sports shoes, so we simulated the current mainstream e-commerce shopping website to make online shopping pages of sports shoes. We first got 50 popular and 50 non-popular songs based on popularity on the charts of various music platforms. Then 10 of each the most popular and the least popular songs were selected from each of the two groups based on user feedback. Participants were asked to browse eight goods while listening to music in a quiet indoor environment. In terms of objective data collection, eye tracker was used to record participants' browsing time, browsing track and focusing position. After that, we used a questionnaire to collect the subjective evaluation. The questionnaire mainly includes three parts: recording emotions before shopping, recording behaviors during shopping (direct purchase, add shopping cart, share and collection), and recording experiences after shopping (ranking of purchase intention, emotions and music feelings). Finally, we conducted a qualitative analysis of semi-structured interviews to improve the experimental design.ResultsA t-test of the experimental data revealed that the duration of participant browsing markedly increased when shopping with music, in comparison to shopping without music (p<0.05). Compared with non-pop music, the subjects were more familiar with pop music and listened to it for a longer time. During the experience with popular music, the emotional arousal of the participants significantly increased in the shopping context (p<0.05). More precisely, the heightened shopping excitement contributed to an elevated rate of adding items to the cart and sharing these selections.ConclusionOur research shows that music popularity significantly influences online consumer behavior. Specifically, in terms of the connection between music types and shopping experience, pop music first increases users' attention to goods. In addition, familiar music enhanced the arousal dimension of mood, thus promoting the arousal of desire to buy. These insights enhance our comprehension of the strategic use of background music in e-commerce shopping contexts. Based on the conclusions of the research, a better auditory experience can be created for users in the shopping scene in the future. In terms of business application and marketing strategy, it can also guide merchants to strategically use music to influence mood changes and promote product consumption.
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Waithaka, Eva Njambi. "Potential for Behaviour Change among Kenyan Type 2 Diabetes Service Users: An Exploratory Factor Analysis of the Health Belief Model". En 3rd International Nutrition and Dietetics Scientific Conference. KENYA NUTRITIONISTS AND DIETICIANS INSTITUTE, 2023. http://dx.doi.org/10.57039/jnd-conf-abt-2023-f.s.d.h.l-17.
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Behavioural factors associated to eating habits, increased physical activity and reduction in behaviours that are sedentary are important for preventing, managing and influencing the risk of developing type 2 diabetes mellitus (T2DM). Since human behaviour is complex, theoretical frameworks have been developed to help improve the success of different interventions that purpose to change behaviours. This study assessed the use of the health belief model to describe potential for behaviour change among Kenyan T2DM service users. A cross sectional research design was adopted. A quantitative methodology was used. The location of the study was Thika Level 5 Hospital. The target population of the study was T2DM service users, aged 20-70 years, and attending Thika level 5 hospital diabetes clinic. The sample size for the study was 346 service users. Purposive sampling was used in selecting the sample size. A questionnaire was used in data collection. Data collected was cleaned, coded and entered into SPSS version 26.0 for analysis. Items related to the patients’ demographic information were assessed through frequencies and percentages. Items testing behaviour change based on the health belief model were subjected to exploratory factor analysis (EFA). Results for the study were presented in form of tables. Through EFA, the study found that behaviour change among Kenyan T2DM service users attending Thika Level 5 Hospital diabetes clinic is based on four constructs of the health belief model: susceptibility to heart attack or stroke, intention to exercise, perceived benefits of exercise and healthy eating and healthy eating intentions (cues of action). The study therefore concludes that behaviour change is perceived differently among T2DM service users. Therefore, it is recommended that health awareness messages and counseling for T2DM patients should utilize the health belief model constructs to come up with effective interventions for promoting behaviour change. A “one size fits all” approach is not recommended when coming up with interventions towards behaviour change. Key Words: EFA, HBM, behavior change, T2DM
Informes sobre el tema "Human intention and behavior analysis"
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Hillestad, Torgeir Martin. The Metapsychology of Evil: Main Theoretical Perspectives Causes, Consequences and Critique. University of Stavanger, 2014. http://dx.doi.org/10.31265/usps.224.
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The purpose of this text or dissertation is to throw some basic light on a fundamental problem concerning manhood, namely the question of evil, its main sources, dynamics and importance for human attitudes and behaviour. The perspective behind the analysis itself is that of psychology. Somebody, or many, may feel at bit nervous by the word “evil” itself. It may very well be seen as too connected to religion, myth and even superstition. Yet those who are motivated to lose oneself in the subject retain a deep interest in human destructiveness, malevolence and hate, significant themes pointing at threatening prospects for mankind. The text is organized or divided into four main ordinary chapters, the three first of them organized or divided into continuous and numbered sections. A crucial point or question is of cause how to define evil itself. It can of cause be done both intentional, instrumental and by consequence. Other theorists however have stated that the concept of evil exclusively rests on a myth originated in the Judean-Christian conception of Satan and ultimate evil. This last argument presupposes evil itself as non-existent in the real rational world. It seems however a fact that most people attach certain basic meaning to the concept, mainly that it represents ultimately bad and terrible actions and behaviour directed toward common people for the purpose of bringing upon them ultimate pain and suffer. However, there is no room for essentialism here, meaning that we simply can look “inside” some original matter to get to know what it “really” is. Rather, a phenomenon gets its identity from the constituted meaning operating within a certain human communities and contexts loaded with intentionality and inter-subjective meaning. As mentioned above, the concept of evil can be interpreted both instrumental and intentional, the first being the broadest of them. Here evil stands for behaviour and human deeds having terrifying or fatal consequences for subjects and people or in general, regardless of the intentions behind. The intentional interpretation however, links the concept to certain predispositions, characteristics and even strong motives in subjects, groups and sometimes political systems and nations. I will keep in mind and clear the way for both these perspectives for the discussion in prospect. This essay represents a psychological perspective on evil, but makes it clear that a more or less complete account of such a psychological view also should include a thorough understanding or integration of some basic social and even biological assumptions. However, I consider a social psychological position of significant importance, especially because in my opinion it represents some sort of coordination of knowledge and theoretical perspectives inherent in the subject or problem itself, the main task here being to integrate perspectives of a psychological as well as social and biological kind. Since humans are essential social creatures, the way itself to present knowledge concerning the human condition, must be social of some sort and kind, however not referring to some kind of reductionism where social models of explanation possess or holds monopoly. Social and social psychological perspectives itself represents parts of the whole matter regarding understanding and explanation of human evil. The fact that humans present, or has to represent themselves as humans among other humans, means that basically a social language is required both to explain and describe human manners and ways of being. This then truly represents its own way or, more correctly, level or standard of explanation, which makes social psychology some sort of significant, though not sufficient. More substantial, the vision itself of integrating different ontological and theoretical levels and objects of science for the purpose of manifesting or make real a full-fledged psychological perspective on evil, should be considered or characterized a meta-psychological perspective. The text is partially constructed as a review of existing theories and theorists concerning the matter of evil and logically associated themes such as violence, mass murder, genocide, antisocial behaviour in general, aggression, hate and cruelty. However, the demands of making a theoretical distinction between these themes, although connected, is stressed. Above all, an integral perspective combining different scientific disciplines is aimed at.
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Bier, Asmeret Brooke. Sensitivity analysis techniques for models of human behavior. Office of Scientific and Technical Information (OSTI), septiembre de 2010. http://dx.doi.org/10.2172/1008119.
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Martinez, Kelley. Examining Human Behavior and Tool Use through Experimental Replications and a Technological Analysis of Ground Stone in the Lower Columbia. Portland State University Library, enero de 2000. http://dx.doi.org/10.15760/etd.6958.
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Cueto, Santiago. Additional Analysis of Nutritional Indicators and Children's Development. Inter-American Development Bank, mayo de 2007. http://dx.doi.org/10.18235/0006820.
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This presentation deals with the relationship between malnutrition and behavior patterns. As examples on how nutrition affects human social development it presents a series of crossed indicators such as neonatal iron status and temperament and height and weight as predictors of achievement, grade repetition and dropout in rural Peru. This document was created for the PAHO/IDB Workshop Seminar "On Health, Human Development Potential and the Quality of Life: Towards Biological-Based Index of Human Development Potential for Assessing the Quality of Life" in May, 2007.
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Ricca, Bernard. Introduction to Nonlinear Dynamical Systems and Analysis. Instats Inc., 2024. http://dx.doi.org/10.61700/j16tr1vnie1lu1801.
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This seminar introduces nonlinear dynamical systems analyses tailored for researchers across the social, health, and physical sciences, providing a framework to model complex human behavior and societal interactions. Participants will gain hands-on expertise in using R for nonlinear dynamical systems modeling, exploring concepts such as fixed points, stability, and attractors, with practical applications to enhance their research.
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Davis, Austin. Overview of a rapid discrete infrared acquisition system and method for automated behavioral analysis of multiple emissive objects. Engineer Research and Development Center (U.S.), febrero de 2025. https://doi.org/10.21079/11681/49564.
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Many animal species form congregations on the landscape. These concentrations of animals provide an opportunity for biologists to conduct efficient population monitoring efforts. While general use of these sites is easy to document, continual monitoring is often problematic due to limited resources (time, expertise, etc.), potential for human disturbance on animal population and behavior, and an inability to determine an accurate assessment of counts. To allow for accurate and efficient assessment of animal numbers and usage of an area, an automated technology has been developed to monitor and characterize large animal concentrations. This automated technology provides information on population size, movement behavior characteristics, and other behavioral aspects of the target species.
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Alhaji, Mohammed M., Nyaga Robert y Patrick Forscher. All responses are local. How behavioral systems can enhance global management of the Mpox outbreak. Busara, octubre de 2024. http://dx.doi.org/10.62372/yeup3422.
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Mpox represents a dire threat to public health. We believe that behavioral science can play a crucial role in understanding and managing the Mpox threat as effective management of public health requires understanding and managing human behavior. However, behavioral science alone may not be enough: pandemics spread through complex networks of human behavior that we call behavioral systems (Diaz del Valle, Wendel, & Jang, 2023); responding effectively to Mpox may therefore require an interdisciplinary toolkit that integrates qualitative and quantitative methods from across psychology, behavioral economics, anthropology, political science, systems analysis, and computational methods to understand how individual behaviors fit within larger systems, and how these systems can, in turn, be changed through effective targeting of behavioral interventions within them.
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bell, Matthew, Marcel P. Huijser y David Kack. Exploring Apex Predator Effects on Wildlife-Vehicle Collisions: A Case Study on Wolf Reintroductions in Yellowstone. Western Transportation Institute, septiembre de 2024. http://dx.doi.org/10.15788/1727735675.
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This study investigates the impact of wolf reintroduction on wildlife-vehicle collisions (WVCs) along a segment of US-191 bordering Yellowstone National Park. Wolves were reintroduced in 1995–1996, and subsequent wolf pack establishment may have influenced the behavior and population dynamics of prey species, potentially altering WVC patterns. Using carcass data collected from 1989 to 2021, the analysis was divided into two primary phases: before wolves (1989–1996) and after wolves (1997–2021). A series of linear mixed-effects models were developed to assess changes in WVCs across these time periods. Predictor variables included average annual daily traffic (AADT), elk population estimates, and wolf counts. Results showed that WVCs significantly declined in the post-wolf period, suggesting that the presence of wolves may reduce WVCs directly by modifying prey behavior and movement patterns, or indirectly by reducing prey population densities. Further analysis revealed that while elk populations were a significant predictor of WVCs before wolves were reintroduced, this relationship weakened post-reintroduction. Traffic volume did not significantly influence WVC patterns in either period, nor did it interact significantly with wolf presence. The inclusion of wolf counts as a continuous variable showed a negative relationship with WVCs, indicating that higher wolf densities may contribute to a further reduction in collisions over time. These findings suggest that apex predators can play a role in mitigating human-wildlife conflicts, such as WVCs, by influencing prey species’ behavior and distribution. The study provides valuable insights for wildlife managers and transportation planners, highlighting the potential benefits of predator conservation for road safety and ecosystem health.
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Nyaga, Robert G. y Stephen Wendel. What is rigorous qualitative research in behavioral science? Busara, mayo de 2024. http://dx.doi.org/10.62372/ldgw4731.
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Rigorous qualitative research in behavioral science guides the understanding of the intricacies of human behavior, motivations, and experiences. It transcends mere observation, and delves into the depths of individual experiences and socio-cultural nuances. Through replicable research design, transparent data collection processes, and systematic analysis, rigorous qualitative research endeavors to unearth rich insights that quantitative and experimental approaches alone cannot capture. It offers a profound understanding of our world, contributing to the advancement of theory and practice within behavioral science. When applied in behavioral science, qualitative research provides a meaningful richness of insights, an approach we call qualitative behavioral science. Qualitative behavioral science synthesizes the long and deep traditions of qualitative research with the specific insights and goals of applied behavioral science: to understand and make meaningful, measurable changes in people’s lives.
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Gibbons, Ronald, Rajaram Bhagavathula, Brian Williams, Andrew Kassing, Johann Hamen y Daniel Elkins. Evaluation of Roadway High-Mast Tower Lighting. Illinois Center for Transportation, octubre de 2023. http://dx.doi.org/10.36501/0197-9191/23-021.
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This study conducted an evaluation of high-mast roadway lighting, which included a review of Illinois Department of Transportation’s high-mast lighting specification and a field experiment. The lighting specification was reviewed for potential cost-saving measures. Recommended changes included the use of an external drive mechanism, a design-by-application mounting ring, and using the industry standard handhole design. The field experiment consisted of two human factors evaluations. In one evaluation, participants were driven along I-57 through five interchanges with various lighting designs and answered questionnaires about the lighting. Results showed that interchanges that were fully lit outperformed partially lit interchanges for perceptions of comfort, safety, and visibility regardless of whether the interchange used conventional lighting or high-mast lighting. When comparing participants’ responses between high-mast lighting at full intensity or at a 50% dim level, there was no change in perceptions of comfort, safety, or visibility. In the second evaluation, radar sensors were placed at key locations at each interchange and the speed behavior of traffic was recorded and analyzed. Speed analysis showed that lighting designs using only high-mast lighting had a potential ±safety benefit via reductions in vehicle speed variation. Comparing high-mast lighting at full intensity and at a 50% dim level, the dimmed lighting resulted in a significant reduction in speed for exiting traffic. Furthermore, dimming improved safe speed behavior on ramp segments while not significantly affecting the speed behavior for vehicles traveling through the interchange. Recommendations based on the results include using full interchange lighting designs, using a single light type (conventional or high mast, but not both), using a lower light level, and dimming lighting during times of low traffic volume.