Tesis: "Human intention and behavior analysis"

1

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 piétons
Pedestrian 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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2

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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3

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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4

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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5

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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6

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 comportements
The 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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7

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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8

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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9

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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10

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.

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11

Linnell, Don C. "Developing insight into human purposefulness as a crucial element to effective biblical counseling". Theological Research Exchange Network (TREN), 1993. http://www.tren.com.

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12

Mak, Hio Wa. "The application of the theory of planned behavior in help-seeking intention in Macao". Thesis, University of Macau, 2012. http://umaclib3.umac.mo/record=b2589386.

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13

Emelianchik-Key, Kelly, Guardia Amanda C. La y Rebekah Byrd. "Adolescent Self-Injurious Behavior: Analysis of the Youth Risk Behavior Survey Trends". Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etsu-works/924.

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Currently, most adolescent self injurious behavior research is focused on adolescent White females, indicating that self-injury may be more prevalent among females and those of Caucasian decent (Whitlock, 2010). This session will present data from a current study indicating that perhaps males and other ethnic groups are also engaging in this destructive coping mechanism, perhaps in differing ways (self hitting and/or physically aggressive behaviors resulting in injury) than are being focused on by current conceptual and empirical works.

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14

Young, Margaret H. "Parenting Style and Child Behavior Problems: A Longitudinal Analysis". DigitalCommons@USU, 1993. https://digitalcommons.usu.edu/etd/2807.

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Data from the National Survey of Children were used to study the relationships between children's perceptions of parental support and control and measures of self-esteem and behavior problems over time. Data were collected in 1976 , when the children were aged 7-11; 1981, when the children were in their early to mid teens (age 12 to 16); and 1987, when the children were in their late teens and early 20s (age 17 to 22). Parenting measures , based on children's reports, were developed for each wave from items included in the data; constructed variables measuring self -esteem and internal and external expressions of behavior problems were also comprised of individual items drawn from each wave of data. Preliminary analyses showed that parental support was positively correlated to children's self-esteem and negatively related to behavior problems. Although the parental control measures had little effect on the outcome variables , the effect that was present showed that parental coerciveness, rejection, and permissiveness were negatively correlated with children's self-esteem, while being positively associated with both internal and external behavior problems. The preliminary results also showed that the parental effects of mothers and fathers differed for daughters and sons. LISREL analyses were done in an effort to more fully investigate the interactive effects between the constructed variables of interest. Generally, the measures were related as expected , although the relationships were not as strong as anticipated. Of the parenting measures, parental support showed the strongest effects on child outcomes; parental control measures had very little effect on children's self-esteem or behavior problems . In 1987, parenting style had virtually no effect on youths' self-esteem or on behavior problems. For this wave, self-esteem was the strongest predictor of behavior problems in young adults.

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15

Ridgeway, Kristi. "Using the health belief model to enhance understanding of female college students' intention to receive the human papillomavirus vaccination". Pullman, Wash. : Washington State University, 2009. http://www.dissertations.wsu.edu/Thesis/Summer2009/k_ridgeway_061609.pdf.

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16

Liu, Kai. "Detecting stochastic motifs in network and sequence data for human behavior analysis". HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/60.

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With the recent advent of Web 2.0, mobile computing, and pervasive sensing technologies, human activities can readily be logged, leaving digital traces of di.erent forms. For instance, human communication activities recorded in online social networks allow user interactions to be represented as “network” data. Also, human daily activities can be tracked in a smart house, where the log of sensor triggering events can be represented as “sequence” data. This thesis research aims to develop computational data mining algorithms using the generative modeling approach to extract salient patterns (motifs) embedded in such network and sequence data, and to apply them for human behavior analysis. Motifs are de.ned as the recurrent over-represented patterns embedded in the data, and have been known to be e.ective for characterizing complex networks. Many motif extraction methods found in the literature assume that a motif is either present or absent. In real practice, such salient patterns can appear partially due to their stochastic nature and/or the presence of noise. Thus, the probabilistic approach is adopted in this thesis to model motifs. For network data, we use a probability matrix to represent a network motif and propose a mixture model to extract network motifs. A component-wise EM algorithm is adopted where the optimal number of stochastic motifs is automatically determined with the help of a minimum message length criterion. Considering also the edge occurrence ordering within a motif, we model a motif as a mixture of .rst-order Markov chains for the extraction. Using a probabilistic approach similar to the one for network motif, an optimal set of stochastic temporal network motifs are extracted. We carried out rigorous experiments to evaluate the performance of the proposed motif extraction algorithms using both synthetic data sets and real-world social network data sets and mobile phone usage data sets, and obtained promising results. Also, we found that some of the results can be interpreted using the social balance and social status theories which are well-known in social network analysis. To evaluate the e.ectiveness of adopting stochastic temporal network motifs for not only characterizing human behaviors, we incorporate stochastic temporal network motifs as local structural features into a factor graph model for followee recommendation prediction (essentially a link prediction problem) in online social networks. The proposed motif-based factor graph model is found to outperform signi.cantly the existing state-of-the-art methods for the prediction task. For extract motifs from sequence data, the probabilistic framework proposed for the stochastic temporal network motif extraction is also applicable. One possible way is to make use of the edit distance in the probabilistic framework so that the subsequences with minor ordering variations can .rst be grouped to form the initial set of motif candidates. A mixture model can then be used to determine the optimal set of temporal motifs. We applied this approach to extract sequence motifs from a smart home data set which contains sensor triggering events corresponding to some activities performed by residents in the smart home. The unique behavior extracted for each resident based on the detected motifs is also discussed. Keywords: Stochastic network motifs, .nite mixture models, expectation maximization algorithms, social networks, stochastic temporal network motifs, mixture of Markov chains, human behavior analysis, followee recommendation, signed social networks, activity of daily living, smart environments

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17

Varnon, Christopher A. "A Stimulus Control Analysis of Imprinting in a Human-Reared Pigeon". Thesis, University of North Texas, 2011. https://digital.library.unt.edu/ark:/67531/metadc84297/.

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Events that occur early in the life of birds greatly influence social and sexual preferences throughout the course of life. Traditionally, this is explained by a learning process known as imprinting. Young birds are thought to imprint to early stimuli, causing the development of permanent preferences for those stimuli. In the present study, imprinting is examined with respect to behaviors of an adult human-reared pigeon in several conditions. The subject was either presented with no stimulus, a conspecific stimulus, a novel stimulus, a human stimulus, or the human and novel stimuli simultaneously. Several phases within these conditions were employed to pinpoint the variables that produced the most social and sexual behavior. The results showed that while some conditions produced unclear behavior, other conditions produced very clear indications of sexual preference for humans and fear of conspecifics. The results suggest that the concept of imprinting may not be needed to explain the sexual preference of the subject, and that operant contingencies may play a large role in sexual behavior.

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18

Kim, Sojung. "Dynamic Learning and Human Interactions under the Extended Belief-Desire-Intention Framework for Transportation Systems". Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/578837.

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In recent years, multi-agent traffic simulation has been widely used to accurately evaluate the performance of a road network considering individual and dynamic movements of vehicles under a virtual roadway environment. Given initial traffic demands and road conditions, the simulation is executed with multiple iterations and provides users with converged roadway conditions for the performance evaluation. For an accurate traffic simulation model, the driver's learning behavior is one of the major components to be concerned, as it affects road conditions (e.g., traffic flows) at each iteration as well as performance (e.g., accuracy and computational efficiency) of the traffic simulation. The goal of this study is to propose a realistic learning behavior model of drivers concerning their uncertain perception and interactions with other drivers. The proposed learning model is based on the Extended Belief-Desire-Intention (E-BDI) framework and two major decisions arising in the field of transportation (i.e., route planning and decision-making at an intersection). More specifically, the learning behavior is modeled via a dynamic evolution of a Bayesian network (BN) structure. The proposed dynamic learning approach considers three underlying assumptions: 1) the limited memory of a driver, 2) learning with incomplete observations on the road conditions, and 3) non-stationary road conditions. Thus, the dynamic learning approach allows driver agents to understand real-time road conditions and estimate future road conditions based on their past knowledge. In addition, interaction behaviors are also incorporated in the E-BDI framework to address influences of interactions on the driver's learning behavior. In this dissertation work, five major human interactions adopted from a social science literature are considered: 1) accommodation, 2) collaboration, 3) compromise, 4) avoidance, and 5) competition. The first three interaction types help to mimic information exchange behaviors between drivers (e.g., finding a route using a navigation system) while the last two interaction types are relevant with behaviors involving non-information exchange behaviors (e.g., finding a route based on a driver's own experiences). To calibrate the proposed learning behavior model and evaluate its performance in terms of inference accuracy and computational efficiency, drivers' decision data at intersections are collected via a human-in-the-loop experiment involving a driving simulator. Moreover, the proposed model is used to test and demonstrate the impact of five interactions on drivers' learning behavior under an en route planning scenario with real traffic data of Albany, New York, and Phoenix, Arizona. In this dissertation work, two major traffic simulation platforms, AnyLogic® and DynusT®, are used for the demonstration purposes. The experimental results reveal that the proposed model is effective in modeling realistic learning behaviors of drivers in conduction with interactions with other drivers.

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19

Crowl, Justin. "An analysis of music and its influence on adult lifestyle choices and behavioral tendencies /". Connect to resource online, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1254324107.

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CARVALHO, CLAUDIO RIBEIRO. "NUMERICAL AND EXPERIMENTAL ANALYSIS OF THE BEHAVIOR OF THE HUMAN SKIN UNDER EXPANSION". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2004. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=5519@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
A modelagem de materiais biológicos para o entendimento de diversas práticas cirúrgicas na medicina, tem se mostrado a cada dia mais importante. Na cirurgia plástica, reparadora ou estética, o conhecimento do comportamento da pele humana, in vivo, é de fundamental importância nos processos cirúrgicos. Nos últimos anos, mais precisamente desde 1957, uma técnica realizada pela cirurgia plástica, conhecida como expansão de pele, vem sendo cada vez mais utilizada. Esta técnica tem como objetivo a obtenção de uma quantidade adicional de pele, para a utilização na recuperação de queimados, reconstrução mamária, retirada de pele com aparência indesejada e outras aplicações. Este trabalho tem como objetivo fornecer aos médicos o conhecimento do comportamento da pele, através de uma análise numérica e experimental, quando submetida ao processo de expansão. Durante o desenvolvimento deste trabalho, estaremos mostrando a metodologia adotada para estudar a pele humana e os resultados alcançados com o acompanhamento de pacientes submetidos a este tipo de processo cirúrgico. Para atingir o objetivo, foram estudadas diversas equações constitutivas, procurando as melhores constantes viscoelásticas, que descrevam o comportamento da pele sob expansão. No estudo realizado, avaliamos ainda as mudanças das constantes viscoelásticas durante a evolução da expansão e o comportamento da pele em diferentes regiões do corpo humano. Nos resultados apresentados, encontramos erros máximos para as constantes da equação constitutiva adotada, na ordem de 28 por cento. Estudos recentes envolvendo a pele humana apresentam erros na ordem de 75 por cento, para as constantes elásticas do material. Com a análise dos resultados, mostramos o comportamento da pele humana, em seres vivos, em diferentes regiões do corpo. O conhecimento de uma equação constitutiva, ou seja, da relação entre as tensões e deformações que ocorrem na pele durante o processo de expansão, proporcionará aos médicos a possibilidade de avaliar as diversas técnicas utilizadas na cirurgia plástica em geral : a expansão de pele, a reconstrução mamária, análise de suturas, análise de cicatrizes, comportamento da pele durante uma incisão, cirurgias estéticas da face (face lifting), e outras aplicações.
The modeling of biological materials is really important today, for the agreement of diverse practical surgical in the medicine. In the area of plastic surgery, the knowledge of the behavior of the human skin has a basic importance in the surgical processes. Since 1957, one technique in plastic surgery, known as skin expansion, has been much used. This technique has as objective the attainment of an additional amount of skin, for the use in the burned patients, breast reconstruction, removal of skin with not desired appearance is and other applications. This work has as objective supply to doctors the knowledge of the skin behavior, through a numerical and experimental analysis, when submitted to the expansion process. During the development of this work, we will show the methodology used to study the human skin and the results reached with the accompaniment of patients submitted to this type of surgical process. To reach the objective, different constitutive equations had been studied, looking for the best viscoelastic constants, to describe the behavior of the skin under expansion. In the study, we also evaluate the changes of the constants during the evolution of the expansion and the behavior of the skin in different regions of the human body. In the presented results, we find maximum errors for the constants of the adopted constitutive equation, to be in 28 percent order. Recent studies involving the human skin present errors in the order of 75 percent, for the elastic constants of the material. With the analysis of the results, we will be show to the behavior of the human skin, in vivo, in different regions of the body. The knowledge of a constitutive equation, that is, of the behavior of the stresses and strains that occur in the skin during the expansion process, will provide to the doctors the possibility to evaluate the diverse techniques used in the plastic surgery: The expansion of skin, breast reconstruction, analysis of sutures, analysis of scars, behavior of the skin during an incision, face lifting, and other applications.

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21

Pritchett, Malika Naomi. "Coloniality and the Science of Applied Behavior Analysis". Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1703416/.

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Human life is to be universally cherished and valued. Policies about how to value lives are often developed following gross human rights violations. Some of the most horrific violations have occurred under the guise of biomedical and behavioral research. As a result, policies have been developed to protect participants. Presumably, the primary responsibility of the researcher is their protection. There are, however, potential tensions between protections and research agendas, which set the occasion for over selection of participants with vulnerabilities. This dynamic may establish competing contingencies that devalue, and potentially harm, participants. Power imbalances inherent in the researcher-participant relationship establish the researcher as the dominant knowledge seeking authority and the participant as the subservient subject. Ideally, research in applied behavior analysis is driven by a steadfast orientation toward the enhancement of human life and the amelioration of suffering. The purpose of this paper is to present an analysis of human rights trends in the Journal of Applied Behavior Analysis. The dependent measures are based on ethical principles established for the protection of participants and recommendations concerning participatory research practices in applied behavior analysis. The results indicate that in some cases, protections have been minimally reported. Furthermore, power imbalances are highly likely given the processes and outcomes reported. The trends appear to be moving in an unfavorable direction in most cases. Findings are discussed on three levels: 1) a conceptual analysis of potential contingencies that influence applied behavior analytic research, 2) considerations around coloniality, and, 3) recommendations to neutralize and diffuse power imbalances to ensure the applied spirit of the science is actualized.

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22

Hayes, Kate. "A Qualitative Analysis of Student Behavior and Language During Group Problem Solving". Fogler Library, University of Maine, 2009. http://www.library.umaine.edu/theses/pdf/HayesK2009.pdf.

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23

Ahn, Hyungil 1976. "Modeling and analysis of affective influences on human experience, prediction, decision making, and behavior". Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/61929.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 207-212).
Subjective and affective elements are well-known to influence human decision making. This dissertation presents a theoretical and empirical framework on how human decision makers' subjective experience and affective prediction influence their choice behavior under uncertainty, frames and emotions. The framework extends and integrates existing theories of prospect theory (PT) and reinforcement learning (RL), drawing on a growing literature offering the role of affect in decision making and the neural underpinnings of human decision behavior. The proposed Affective-Cognitive (AC) model extends Prospect Theory (PT)- based subjective value functions to model human experienced-utility and predicted-utility functions. The AC model assumes that the shapes (or parameters) of these subjective value functions dynamically vary with the decision makers affective states in sequential decision making. Human decision-making experiments were conducted to empirically infer how people adjust the parameters (i.e., shape and reference point) of their experienced-utility and predicted-utility functions in sequential decision-making situations involving incidental affective states (e.g., anger, fear, economic fear) and task-related confidence. I constructed a new model combining measures to evaluate risk preferences: behavioral choices, selfreported experience self-reported experience, self-reported predicted utility, self-reported confidence. The analysis results show how domain uncertainty, framing, and emotion state of decision makers influence their subjective experience and discriminability, affective prediction, optimal decisions and exploratory regulation. I found empirically that there were significant interaction effects of framing and emotion on risk preferences: negative emotions made people more risk-averse in face of gains. When it comes to losses, anger made people more risk-averse and fear more risk seeking. I also characterized how gender and emotion influence confidence and exploratory choice behavior. The theoretical analysis nicely supports empirical findings from human experiments. The new model provides a theory that better explain and simulate human behavior under uncertainty, frames and emotions.
by Hyung-il Ahn.
Ph.D.

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24

Grimes, George Mark. "Analysis of Human Computer Interaction Behavior for Assessment of Affect, Cognitive Load, and Credibility". Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/556821.

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This dissertation presents three studies consisting of seven experiments that investigate the relationship between human-computer interaction (HCI) behavior and changes in cognitive states by using keystroke dynamics (KD) and mouse dynamics (MD) as physiological indicators of cognitive change. The first two chapters discuss the importance of being able to detect changes in affect, cognitive load, and deception and provide a theoretical base for this research, pulling heavily from cognitive science, psychology and communication literature. We also discuss the current state of the art in keystroke and mouse dynamics and what makes the techniques presented here different. Chapters three and four present five experiments that explore the influence of affect and cognitive load on KD and MD. The results of these experiments suggest that many features of typing and mouse movement behavior including transition time, rollovers, duration, number of direction changes, and distance traveled are influenced by changes in affect and cognitive load. In chapter five we operationalize these findings in a credibility assessment context and describe two experiments in which participants behave deceptively in computer mediated interactions. In both experiments, we find significant differences in typing behavior, in line with the findings of the first two studies. Chapter six summarizes the results and provides a way forward for future research in human computer interaction. The work presented in this dissertation describes a novel approach to inferring cognitive changes using low cost, non-invasive, and transparent monitoring of HCI behavior with important implications for both research and practice.

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25

Hustad, John T. P. "Self-regulation, alcohol consumption and consequences in college student heavy drinkers a latent growth analysis /". Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2006. http://proquest.umi.com/login?COPT=REJTPTU0NWQmSU5UPTAmVkVSPTI=&clientId=3739.

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26

Parsatemijani, Mojtaba. "Intention Behavior Gap in Purchasing Used Products : A Quantitative Analysis of Factors Determining Purchase of Secondhand Household Durables in Sweden". Thesis, Umeå universitet, Företagsekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-172354.

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The incremental awareness ofenvironmental issues and sustainability has shifted the retail industryin past years. Consumers are increasingly demanding for sustainable products and services and consequently, companies need to address customers’ demands to maintain their profitability. One of the most sustainable consumption patterns is thrift shopping which has several economic, social, and environmental benefits. Despite the huge sustainable benefits of secondhand shopping,only a few studies investigated the behavior of thrift shoppers to identify determinants or barriers to their secondhand purchase behavior. This study is going to address this gap in the literature by extending the theory of planned behavior in the context of secondhand shopping. This leadsto three research questions which provide a strong theoretical and practical contribution to the secondhand shopping literature:RQ1: How large is the gap between the intention and the behavior of consumers buying secondhand products?RQ2: Is the gap more contributed by those who intend to buy them while they do not so, or those who intend not to buy them but do so?RQ3: What are the determinants ofpurchasing secondhand products?This study aims at investigating Swedish consumers’ secondhand purchasing behavior regardingsecondhand household durables. A quantitative research approach was adopted using a self-administered survey among 179respondents.The main findings of this study state that there is a positive gap between secondhand purchase intention and behavior. Inclined abstainers whose intention to buy secondhand products is higher than their thrift shopping behavior coverthe largestshare of the respondent in this study. Additionally, the results of this study posit that thrift shoppers’ attitude toward used goodsand thrift shopping, their past experience of secondhand shopping, social norms and peer pressure, trust in the performance of used products, product availability and shoppers’ access to thrift stores, and thrift shoppers’ perceived value are the positive determinants of thrift shopping behavior through the mediation role of purchase intention. Conversely,thethrift shoppers’ perception of the risks associated with used products negatively impacts on their secondhand shopping behavior.

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27

Wang, Zhen. "Human disease-behavior interactions on complex networks models: incorporating evolutionary game into epidemiology". HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/22.

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In the past decade, the study of disease dynamics on complex networks has attracted great attention from both theoretical and empirical viewpoints. Under such a framework, people try to predict the outbreak of disease and propose immunization mechanisms. However, this framework possesses a limitation, which makes it inconsistent with realistic cases. That is, this framework does not consider the impact of human behavior or decision-making progress on disease dynamic characters and prevention measures. To further resolve this problem, we in this thesis propose behavioral epidemiology based on game theory, which involves the interactions between disease dynamics and human behavior in complex networks. Motivated by realistic cases, we proceed with the research from theoretical models and consider the following aspects. We .rst re-construct a scheme of risk perception incorporating local and global information and show that this new evaluation scenario not only promotes vaccination uptake, but also eliminates the disease spreading. This interesting .nding could be attributed to the positive feedback mechanism between vaccination uptake and disease spreading. Then, we introduce a self-protection measure, which, due to low cost, can only provide temporary protection. By simulations and analysis we show that this measure leads to multiple e.ects: contrary with cases of low (high) e.ciency and cost of the self-protection measure, middle values drive more infection and larger cost, which is related to the loss of positive feedback between prevention measures and disease propagation. Subsequently, another scheme of adaptive protection is proposed, where a healthy agent can cut the connection with infected ones. We .nd that adaptive protection can e.ectively eradicate the disease and result in an optimal level of pruning infected links. Di.erent from these proposals focusing on individual interest, we lastly study a subsidy policy from the viewpoint of population bene.t. We .nd that disease can be well controlled with an increase of the vaccination level, while the total expense reduces. Taken together, these .ndings of the thesis further demonstrate that the interplay between disease dynamics and human behavior plays an important role in the control of diseases. The models presented in this thesis, especially combining with empirical data, may serve as a foundation for further investigation of the subject in the future.

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28

Wang, Jiachao. "Bayesian analysis for quantification of individual rat and human behavioural patterns during attentional set-shifting tasks". Thesis, University of St Andrews, 2018. http://hdl.handle.net/10023/14843.

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Attentional set-shifting tasks, consisting of multiple stages of discrimination learning, have been widely used in animals and humans to investigate behavioural flexibility. However, there are several learning criteria (e.g., 6-correct-choice-in-a-row, or 10-out- of-12-correct) by which a subject might be judged to have learned a discrimination. Furthermore, the currently frequentist approach does not provide a detailed analysis of individual performance. In this PhD study, a large set of archival data of rats performing a 7-stage intra-dimensional/extra-dimensional (ID/ED) attentional set- shifting task was analysed, using a novel Bayesian analytical approach, to estimate each rat's learning processes over its trials within the task. The analysis showed that the Bayesian learning criterion may be an appropriate alternative to the frequentist n- correct-in-a-row criterion for studying performance. The individual analysis of rats' behaviour using the Bayesian model also suggested that the rats responded according to a number of irrelevant spatial and perceptual information sources before the correct stimulus-reward association was established. The efficacy of the Bayesian analysis of individual subjects' behaviour and the appropriateness of the Bayesian learning criterion were also supported by the analysis of simulated data in which the behavioural choices in the task were generated by known rules. Additionally, the efficacy was also supported by analysis of human behaviour during an analogous human 7-stage attentional set-shifting task, where participants' detailed learning processes were collected based on their trial-by-trial oral report. Further, an extended Bayesian approach, which considers the effects of feedback (correct vs incorrect) after each response in the task, can even help infer whether individual human participants have formed an attentional set, which is crucial when applying the set-shifting task to an evaluation of cognitive flexibility. Overall, this study demonstrates that the Bayesian approach can yield additional information not available to the conventional frequentist approach. Future work could include refining the rat Bayesian model and the development of an adaptive trial design.

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29

Nellen, Stefani. "How humans solve scheduling problems analysis of human behavior in the plan-a-day task /". [S.l. : s.n.], 2002. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB9918630.

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30

Yee, Terence. "Examining Help-Seeking Intentions of Chinese Individuals: A Path Analysis". Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc984257/.

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Utilizing the theory of planned behavior, I examined the variables that affect Chinese individuals' help-seeking intention. A total of 251 Chinese individuals participated in this study. Results showed that the variables in the theory of planned behavior accounted for 16% of the variance in help-seeking intention. Specifically, attitude (r = .22, p < .001) and perceived behavioral control (r = .22, p < .001) were found to be significant predictors of help-seeking intention. Based on these results, mental health professionals can design outreach interventions, such as psychoeducational programs, to improve Chinese individuals' help-seeking attitude and perceived behavioral control in an attempt to increase mental health service utilization. Additionally, counselors can discuss with clients' their attitude and perceived behavioral control regarding seeking counseling in an attempt to assist clients in being committed to the counseling process.

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31

Sayles, Tiffany P. "The Effects of a Human Trafficking Prevention Workshop Package on Participant Written and Simulation Responses". Thesis, University of North Texas, 2013. https://digital.library.unt.edu/ark:/67531/metadc407816/.

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This study evaluated the effects of a community workshop designed to teach community members about human trafficking prevention. Participants were trained to identify the critical and non-critical features of human trafficking and safe ways to respond to identified trafficking situations. A pre-post treatment design was used to assess the effects of a community workshop across written and verbal target behaviors. This included written responses as well as simulation assessments across five different trafficking scenarios. Results indicate that all participants engaged in more correct responding within the written assessment and asked specific relevant questions with greater confidence within the simulation assessment following training. However, social media and empathy responses following the workshop did not differ from baseline. This study is one of the first empirical studies aimed at formally evaluating the effects of human trafficking prevention workshops. Results are discussed in the context of instructional design, measurement of outcomes, and interdisciplinary collaboration.

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32

Amato, A. "SEMANTIC ANALYSIS AND UNDERSTANDING OF HUMAN BEHAVIOUR IN VIDEO STREAMING". Doctoral thesis, Università degli Studi di Milano, 2011. http://hdl.handle.net/2434/155497.

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This thesis investigates the semantic analysis of the human behaviour captured by video streaming, both from the theoretical and technological points of view. The video analysis based on the semantic content is in fact still an open issue for the computer vision research community, especially when real-time analysis of complex scenes is concerned. Automated video analysis can be described and performed at different abstraction levels, from the pixel analysis up to the human behaviour understanding. Similarly, the organisation of computer vision systems is often hierarchical with low-level image processing techniques feeding into tracking algorithms and, then, into higher level scene analysis and/or behaviour analysis modules. Each level of this hierarchy has its open issues, among which the main ones are: - motion and object detection: dynamic background modelling, ghosts, suddenly changes in illumination conditions; - object tracking: modelling and estimating the dynamics of moving objects, presence of occlusions; - human behaviour identification: human behaviour patterns are characterized by ambiguity, inconsistency and time-variance. Researchers proposed various approaches which partially address some aspects of the above issues from the perspective of the semantic analysis and understanding of the video streaming. Many progresses were achieved, but usually not in a comprehensive way and often without reference to the actual operating situations. A popular class of approaches has been devised to enhance the quality of the semantic analysis by exploiting some background knowledge about scene and/or the human behaviour, thus narrowing the huge variety of possible behavioural patterns by focusing on a specific narrow domain. In general, the main drawback of the existing approaches to semantic analysis of the human behaviour, even in narrow domains, is inefficiency due to the high computational complexity related to the complex models representing the dynamics of the moving objects and the patterns of the human behaviours. In this perspective this thesis explores an innovative, original approach to human behaviour analysis and understanding by using the syntactical symbolic analysis of images and video streaming described by means of strings of symbols. A symbol is associated to each area of the analysed scene. When a moving object enters an area, the corresponding symbol is appended to the string describing the motion. This approach allows for characterizing the motion of a moving object with a word composed by symbols. By studying and classifying these words we can categorize and understand the various behaviours. The main advantage of this approach consists in the simplicity of the scene and motion descriptions so that the behaviour analysis will have limited computational complexity due to the intrinsic nature both of the representations and the related operations used to manipulate them. Besides, the structure of the representations is well suited for possible parallel processing, thus allowing for speeding up the analysis when appropriate hardware architectures are used. The theoretical background, the original theoretical results underlying this approach, the human behaviour analysis methodology, the possible implementations, and the related performance are presented and discussed in the thesis. To show the effectiveness of the proposed approach, a demonstrative system has been implemented and applied to a real indoor environment with valuable results. Furthermore, this thesis proposes an innovative method to improve the overall performance of the object tracking algorithm. This method is based on using two cameras to record the same scene from different point of view without introducing any constraint on cameras’ position. The image fusion task is performed by solving the correspondence problem only for few relevant points. This approach reduces the problem of partial occlusions in crowded scenes. Since this method works at a level lower than that of semantic analysis, it can be applied also in other systems for human behaviour analysis and it can be seen as an optional method to improve the semantic analysis (because it reduces the problem of partial occlusions).

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33

Lobb, Steven Lee. "Human Destructiveness and Authority: The Milgram Experiments and the Perpetration of Genocide". W&M ScholarWorks, 1995. https://scholarworks.wm.edu/etd/1539625988.

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August, Euna Marie. "A Cross-National Analysis of the Human Papillomavirus, Sexually Transmitted Infections, and Sexual Behavior among Men". Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/3959.

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There is a paucity of research on the risk for sexually transmitted infections (STIs) and sexual behavior among general populations of men. Research with male populations predominantly has focused on those subgroups considered to be at high risk of disease transmission, such as gay and bisexual men, injection drug users, and adolescents/young adults. Considerably fewer studies have examined factors among men, in general, and heterosexual men, specifically. Therefore, I conducted analyses with a cross-national sample of adult, sexually active men in Brazil, Mexico, and the United States to investigate sexual behaviors and risk factors associated with the human papillomavirus (HPV) and other STIs. The research questions were: 1) How does sexual risk differ among men residing in Brazil, Mexico, and the US by age cohort?; 2) Do men's sexual behaviors change after being tested for HPV and other STIs?; and 3) Do men's sexual behaviors change after being informed of diagnosis with HPV and other STIs? These research questions were explored through a quantitative assessment of secondary data collected through a risk factor questionnaire administered using computer assisted self-interviewing. The study findings underscore the need for public health interventions to address STI risk and transmission among men across the lifespan. Additionally, this study revealed the potential of STI testing as an effective strategy to reduce sexual risk-taking among men. While this research identifies key issues of importance in improving men's sexual health, additional research is needed to provide an enhanced contextual understanding of socio-cultural, interpersonal, and community level factors that affect sexual behaviors and decision-making among men.

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35

Foster, Caley M. "The Effect of Motive and Coworker Liking on the Intention to Perform Organizational Citizenship Behavior". TopSCHOLAR®, 2013. http://digitalcommons.wku.edu/theses/1271.

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Organizational Citizenship Behavior (OCB) is defined as employee behavior that benefits others yet is not required by the employee’s job description. OCB can be divided into two categories: behavior that is either directed toward individuals (OCBI) or behavior that is directed toward the organization (OCBO). Researchers have posited that there are three different motives behind OCB: impression management, prosocial values, and organizational concern. Additionally, researchers have recognized the importance of coworker relationships within organizations and have suggested that the degree to which one likes his or her coworkers may serve as an additional source of motivation to engage in different types of OCB. This research consisted of two studies investigating the effect of motive and coworker liking on intention to engage in OCBO and OCBI. Participants in both studies read a vignette manipulating motive and degree of coworker liking. The second study incorporated two additional items in the manipulation check to increase the saliency of the coworker liking manipulation. In both Study 1 and Study 2 participants were more likely to engage in OCBO than in OCBI. Both studies failed to support the hypotheses that either motive or degree of liking would predict the type of OCB performed.

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36

Ciman, Matteo. "Smartphones as ubiquitous devices for behavior analysis and better lifestyle promotion". Doctoral thesis, Università degli studi di Padova, 2016. http://hdl.handle.net/11577/3424364.

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Ubiquitous Computing, the third computing era, has taken into people' life a huge amount of invisible and pervasive devices, i.e., smartphones, tablets, sensors, etc. Considering their pervasiveness and their presence in people' everyday life, in the last few years research focused its attention on how it is possible to use smartphones to improve people' life. In particular, nowadays smartphones are equipped with an ample set of different sensors, i.e., accelerometer, barometer, light sensor, etc., that can constantly acquire data from the surrounding environment of the user, or about his/her activity and behavior during the day. This data acquisition can be done in a non-intrusive way, since smartphones are always in people' pocket or bag and are already part of individuals' life. Thanks to these sensors, a smartphone is able to acquire a huge amount of data, which can be used for different purposes. For example, the use of ubiquitous computing for healthcare and medical purposes is extremely interesting. In particular, it can improve people' life, help patients to accept their therapies and reduce the drop-out-from-therapy phenomena, and improve medical practice, thus decreasing medical costs both for patients and for governments. Actually, ubiquitous computing for healthcare is facing and addressing several problems that will be discussed in this thesis. First of all, since there is not a single smartphone vendor (which is an advantage from the end-user point of view), when considering smartphone applications for healthcare it is necessary to consider market fragmentation. This kind of applications aims to reach the highest number of possible users, since increasing the number of possible users increases the number of possible patients that could benefit from the usage of a particular application. Clearly, the objective of reaching the highest number of users has to consider also the problem of containing expenses. For this particular problem, we will analyze cross-platform frameworks for smartphone applications development, also called ``write once, run everywhere'' framework''. We will highlight pros and cons of them, considering both the developer and the end user point of view and power consumption, providing a deep analysis of their main characteristics and evaluating them with respect to the native mobile application development approach. Afterward, we will focus our attention to the possibility of using smartphones to infer individuals' moods and feelings. In particular, since smartphones are always present in everybody life, it is possible to use them to transparently and pervasively acquire data and information about how the smartphone is used by the user to understand his/her behavior. In this way, we explored the possibility to use smartphone data, without the usage of any external sensor, thus reducing the entry cost and increasing user acceptance, to assess their stress state. In particular, we will see how the way people interact with their smartphone changes depending on their stress condition, opening the doors for the possibility to pervasively monitor stress of people all the day long without interfering with their life and providing the possibility to doctors to monitor their patients with objective data. Finally, since ubiquitous computing can be used to improve people' life, we explore the usage of serious games with mobile applications to improve people lifestyle. We applied this idea to the possibility to ``teach'' people to take stairs instead of elevators or escalators. In this way the user makes more physical activity among the day, and consequently have fewer problems like obesity, heart attacks, etc. Moreover, serious games can be used to help doctors with their diagnosis, in particular when dealing with children, since the standard tests designed for adults can be unlikely applied to children, thus affecting the quality of the diagnosis. This thesis shows how using serious games and touch interfaces, the best ones for children, we are able to perform diagnosis and rehabilitation of particular diseases that can be treated better if early diagnosed, providing to the doctor good alternatives to standard exercises and tests.
Ubiquitous computing, è il nome dato a quella che viene definita la terza "era computazionale", che ha introdotto, nella vita delle persone, un grande numero di dispositivi invisibili e pervasivi, come smartphone, tablet, sensori, ecc. Considerata la loro forte presenza nella vita quotidiana, negli ultimi anni la ricerca si è focalizzata su come utilizzarli per migliorare la qualità della vita delle persone. Al giorno d'oggi gli smartphone sono equipaggiati con uno svariato numero di sensori, come accelerometro, barometro, sensore di luminosità, ecc., che sono in grado di acquisire costantemente informazioni riguardanti l'ambiente circostante, o determinare il comportamento della persona durante la giornata. Questa acquisizione di dati può avvenire in maniera non intrusiva, in quanto gli smartphone sono sempre più presenti nelle tasche o nelle borse delle persone e sono ormai diventati parte attiva e integrante della loro vita. Le informazioni acquisite possono essere utilizzate per diversi scopi. Per esempio, particolarmente interessante risulta essere l'uso dell'ubiquitous computing in ambito medico per migliorare la qualità di vita delle persone, aiutare i pazienti con le loro terapie e ridurre il fenomeno dell'abbandono di una terapia, e migliorare infine la pratica medica, riducendo quindi le spese sostenute sia da parte dei pazienti che dagli Stati per i propri cittadini. Attualmente, l'uso dell'ubiquitous computing in ambito medico sta affrontando diversi problemi che sono stati presi in considerazione ed analizzati in questa tesi. Per prima cosa, poichè non esiste un unico produttore di smartphone (cosa che da un punto di vista dell'utente finale è un aspetto positivo), quando vengono sviluppate applicazioni in ambito medico risulta essere particolarmente importante tenere in considerazione la frammentazione del mercato. Questo tipo di applicazioni mirano a raggiungere il maggior numero possibile di potenziali utenti in modo da aumentare anche il numero di pazienti che potrebbero beneficiare dall'utilizzo di tale applicazione. Chiaramente, l'obiettivo di raggiungere il maggior numero possibile di utenti, e quindi di supportare il maggior numero di possibili piattaforme e device, si scontra con il problema di contenere i costi di sviluppo. In questa tesi, vengono analizzati i framework cross-platform per lo sviluppo di applicazioni in ambito mobile, chiamati anche framework "write once, run everywhere", sottolineando pro e contro, considerando sia il punto di vista dello sviluppatore che quello dell'utente finale (in particolare per quel che riguarda il consumo di energia), fornendo un'analisi approfondita delle maggiori caratteristiche e comparandoli con l'approccio nativo. Successivamente, verrà posta attenzione sulla possibilità di utilizzare gli smartphone per inferire lo stato emotivo delle persone. In particolare, è possibile utilizzarle gli smartphone per acquisire in maniera trasparente informazioni sull'interazione con lo smartphone stesso, e come questa sia influenzato dalla condizione psicologica dell'utente. I dati provenienti dallo smartphone e dai suoi sensori, senza l'uso di dispositivi esterni (riducendo quindi il costo iniziale ed aumentando la predisposizione dell'utente a tale sistema di monitoraggio), vengono successivamente analizzati per inferire la condizione di stress/no stress dell'utente. In particolare, la tesi dimostra come il modo in cui le persone interagiscono con il loro smartphone è influenzato dal loro livello di stress, ed è quindi possibile monitorare lo stress in maniera pervasiva durante la vita di tutti i giorni, senza interferire con essa e fornendo la possibilità ai medici di monitorare costantemente i loro pazienti con dati oggettivi. Infine, poichè l'ubiquitous computing può essere utilizzato per migliorare la vita delle persone, la tesi analizza l'uso dei serious games per migliorare il loro lo stile di vita. In particolare, viene sfruttata questa idea per insegnare alle persone ad utilizzare le scale al posto di ascensori o scale mobili. In questo modo, si aumenta l'attività fisica durante la giornata, adottando quindi uno stile di vita più attivo, riducendo così la probabilità di avere problemi quali obesità, attacchi di cuore, ecc. Inoltre, i serious games possono essere utilizzati anche per aiutare i dottori con le loro diagnosi, in particolare quando gli utenti finali sono bambini, in quanto gli esercizi standard attualmente utilizzati sono stati definiti per gli adulti e possono risultare complicati per i bambini, influenzando quindi in maniera negativa la qualità della diagnosi. Questa tesi mostra come utilizzando i serious games e le interfacce touch, le migliori per i bambini, siamo in grado di effettuare una diagnosi ed una riabilitazione per particolari patologie che possono essere trattate con maggiore efficacia se diagnosticate precocemente, fornendo al medico curante delle valide alternative agli esercizi utilizzati attualmente.

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Renly, Steven K. McCray Louis M. "Naval Special Warfare 21 : an analysis of organizational change in the 21st century /". Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2001. http://library.nps.navy.mil/uhtbin/hyperion-image/01Dec%5FRenly.pdf.

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Thesis (M.S. in Defense Analysis)--Naval Postgraduate School, December 2001.
Thesis advisor(s): Anna Simons, Gordon H. McCormick. Graduation date for Louis M. McCray: March 2002. Includes bibliographical references (p. 89). Also available online.

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38

Emelianchik-Key, Kelly, Rebekah J. Byrd y Guardia Amanda C. La. "Adolescent Non-Suidical Self-injury: Analysis of the Youth Risk Behavior Survey". Digital Commons @ East Tennessee State University, 2016. https://dc.etsu.edu/etsu-works/892.

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Self-injury is a significant issue with a variety of psychological, social, legal and ethical consequences and implications (Froeschle & Moyer, 2004; McAllister, 2003; Nock & Mendes, 2008; White Kress, Drouhard, & Costin, 2006). Self-injurious behavior is commonly associated with the cutting, bruising or burning of the skin. It also can include trichotillomania, interfering with wound healing and extreme nail biting (Klonsky & Olino, 2008; Zila & Kiselica, 2001). In assessing severity, it is important to note that self-inflicted wounds typically do not require any medical attention, as those who engage in self-injury will usually care for any open wounds in order to prevent infection (Walsh, 2006). The typical duration of a self-injurious act is usually less than 30 minutes, resulting in immediate relief from the emotional turmoil precipitating the behavior (Alderman, 1997; Gratz, 2007). It is difficult to estimate the prevalence of self-injury for many reasons. Nock (2009) noted that reports indicating increased estimates in this behavior derive from “anecdotal reports and estimates from small cross-sectional studies” (p. 81). Given the many ethical and legal ramifications involved in working with clients that self-injure, it is important to understand how self-injury typically manifests itself, how it affects differing populations based on gender and cultural differences, and the level of danger it truly represents to the person choosing to utilize it.

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39

Singh, Ramendra. "An Analysis of Transformational Leadership Skills of Marketing, Sales, Human Resources, and Information Technology Leaders in Relation to Their Job". Thesis, Brandman University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10242925.

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Purpose: The purpose of this quantitative descriptive study was to identify transformational leadership skills exhibited by executives in mid-size companies, working in the fields of Marketing, Sales, HR, and IT. Additionally, this study also tried to identify top domains and skills for each group, and analyze the similarities and differences between groups.

Methodology: The study was structured around three research questions. As this was a quantitative study, self-rating data on 10 domains and 80 skills were collected using the Transformational Leadership Skills inventory instrument. These data were then analyzed using multiple statistical methods.

Findings: Analysis of data produced multiple findings in relation to top, middle, and bottom tier skills and domains for each group, thereby supporting the hypothesis that the situational context of job function is material for transformational leadership.

Conclusions: There were six conclusions that applied to all groups, thereby providing valuable insights in relation to similarities across job functions. Additionally, there were multiple differences found in the use of transformational leadership skills across Marketing, Sales, HR, and IT functions, leading to the creation of distinct transformational leadership skill portfolio for each group.

Recommendations: There were six implications for actions that were developed from the conclusions, ranging from hiring, to talent development, to succession planning. Additionally, eight recommendations for future research were also presented, ranging from logical extensions of this study into different verticals, to complementary new studies that would expand the body of knowledge, to longitudinal cross-sectional study that can be developed over time.

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40

Plochocki, Jeffrey H. "Mechanical regulation of limb joint growth : computational analysis of chondral modeling and implications for the reconstruction of behavior from articular form /". free to MU campus, to others for purchase, 2003. http://wwwlib.umi.com/cr/mo/fullcit?p3091958.

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41

Sköld, Bore. "Estimating Carbon Footprint : A quantitative analysis of greenhouse gas emission related to human behavior and diet in Västerbotten". Thesis, Umeå universitet, Epidemiologi och global hälsa, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-111972.

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Background and objective: Researchers have been looking for a way to predict future emission rates, and come up with explanations on how to tackle the issue of global warming through changes in individual behavior for decades. The focus of these studies have, on the other hand, focused more on nutritional bases rather than cultural. This study’s objective is to provide a method, as a useful tool in further analysis on GHG-emission based on cultural behavioral factors such as socio-economic status as well as age, sex, etc. with diet as emission prediction factor. This could be a stepping stone toward future research on Co2e related to e.g. physiological factors such as BMI, blood pressure and diseases. Method: With the use of data obtained from the FFQ questionnaire within the VIP-program, combined with estimates of greenhouse gas-emission (Co2e) attributed to specific diets obtained from Röös, estimations of individual Co2e emission-levels were calculated using the software “R”. The dataset contained 159 687 observations and 152 different variables. The data was obtained from the Department of Public Health and Clinical Medicine at Umeå University. Portions sizes were mainly collected from the Swedish Food Composition Database. Box-plots and regression analysis were made to illustrate the main findings. Result: The result was a new dataset that could be applied to any population to estimate Co2e-emission on individual level based on an FFQ, given that the FFQ have the same structure as the one in the VIP. The variables that contributed to the highest amount of Co2e were animal products i.e. butter, milk and meat. Chicken, pork and fish were not nearly as Co2e heavy as the meat products containing beef such as “steak”, “minced meat” and “hamburgers”. The regression analysis showed that higher age had a positive effect on reducing emission, as well as being a woman. Education showed an increase in Co2e for higher education. There were some small differences among municipalities. Marital status gave a slight decrease in the regression, meaning married couples emits more than singles. Exercise showed an increase in Co2e for active individuals in the regression analysis. However, the most noticeable result were sex, yielding a relatively big decrease in Co2e-emission for women compared to men. Conclusion: People at younger ages, within the observed age groups 40-60, seemed to reduce their carbon footprint more in relation to the higher age groups over the last 20 years. Overall, the general diet-based carbon footprint in Västerbotten seems to have increased slightly during the last 17 years. A remarkable dip were noticed in 2003, however this might not have been due to any behavioral changes, since the trend broke in 2006 and instantly receded back to the normal levels. This study confirms the fact that meat and dairy products are responsible for a significant amount of the diet-based emission. This topic needs to be studied more, and with this method of applying GHG-emission measures to individual diet-based data, a gate has been opened for a new field of research.

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42

Okamoto, Masako. "Improvement of human brain mapping methods with functional near-infrared spectroscopy and their application to food behavior analysis". Kyoto University, 2005. http://hdl.handle.net/2433/144575.

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Kyoto University (京都大学)
0048
新制・論文博士
博士(農学)
乙第11708号
論農博第2572号
新制||農||917(附属図書館)
学位論文||H17||N4079(農学部図書室)
23570
UT51-2005-F860
(主査)教授村田幸作, 教授安達修二, 教授北畠直文
学位規則第4条第2項該当

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43

Shapiro, Gail Joyce. "Identifying the Factors That Influence Conflict Management Behavior of Human Resource Professionals in the Workplace: An Analysis of the Relationship Between Personality and Conflict Management Behavior". NSUWorks, 2014. http://nsuworks.nova.edu/shss_dcar_etd/26.

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Effective conflict management in the workplace can reduce the negative consequences of conflict. These negative outcomes can include low productivity, health-related stress, increased employee turnover, or litigation. A Human Resource (HR) professional can help mitigate these negative outcomes in the workplace when using effective conflict management behavior with employees. However, there is a void in research pertaining to HR professionals’ use of conflict management behavior. This quantitative, correlational research study examined whether personality has an impact on assertive or cooperative conflict management behavior of HR professionals in the workplace. Statistical testing found a significant relationship between the harmonious, people-person (a Blue personality in the True Colors personality model) and the assertive conflict management behavior. Another finding of this study sheds light on the need for conflict resolution (CR) education and training in the HR field. The majority of HR professionals in this study said “yes” to wanting and needing training and education in the CR skillset. This skillset includes many of the same specific strategies that are used by mediators (reflective listening, reframing, building rapport, step-by-step problem-solving, etc.). These skills would also enable HR professionals to manage and resolve interpersonal employee conflict before it escalates and leads to negative outcomes.

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44

艶艶, 陳. y Yanyan Chen. "Integrated studies on structure and formation mechanism of environmental consciousness in rural and urban China". Thesis, https://doors.doshisha.ac.jp/opac/opac_link/bibid/BB12977312/?lang=0, 2016. https://doors.doshisha.ac.jp/opac/opac_link/bibid/BB12977312/?lang=0.

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中国における都市部と農村部異なる制度的・社会経済的背景により、独特な環境意識を生まれていると考えられる。本研究は、現地調査によりデータを収集し、統計分析を駆使したことにより、都市部と農村部における環境意識の特有の構造と形成メカニズムを解明することを目的とする。先行研究の成果を踏まえ、都市部と農村部の社会構造を考慮し、環境意識に関する総合的な理論モデルを提案し、環境意識の三つのディメンションに分けて展開する。理論的に検討することと実証的なデータの分析結果を基に、環境意識形成の内在因子と外部影響要因を明らかにした。
Long-time institutional and socioeconomic segmentations make rural China become a distinctive society from the urban China. The remarkable rural and urban division in China supplies us a good context to explore the formation and diverse social facets of environmental consciousness. This study aims to clarify the specific structure and formation mechanism of environmental consciousness under the different social backgrounds of rural and urban China based on the statistical results derived from survey data. Three dimensions of environmental consciousness and an integrated theoretical framework which involves both social structural and social psychological variables are proposed. Based on the proposed theoretical framework and examined data analyses, the inner causes and externally influencing factors of environmental consciousness were clarified.
博士(文化情報学)
Doctor of Culture and Information Science
同志社大学
Doshisha University

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45

Martin, Allison L. "Functional analysis and treatment of human-directed undesirable behaviors in captive chimpanzees (Pan troglodytes)". Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26687.

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Thesis (M. S.)--Psychology, Georgia Institute of Technology, 2009.
Committee Chair: Maple, Terry L.; Committee Member: Bloomsmith, Mollie A.; Committee Member: Kelley, Michael E.; Committee Member: Marr, M. Jackson. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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46

Chen, Yun-Hui y 陳韻惠. "The Effect of Green Human Resource Management on Turnover Intention: Moderated Path Analysis of Employer Brand Attractiveness, Corporate Social Responsibility and Prosocial Behavior". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/w8pvs5.

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碩士
國立彰化師範大學
人力資源管理研究所
108
In the past literature, many studies had showed that the implementation of human resources management has a significant impact on the turnover intention of employees. In this modern society with rapid technological advancement and increasingly prosperous economic activities, environmental protection is gradually being paid attention to by society. Enterprises also began to reflect on whether they had impacts on the environment during the operation process. Thus, the concept of green human resource management gradually matured. However, there have been few studies about the intermediary mechanism between green human resource management and turnover intention in the past. This study explores the impact of green human resource management on turnover intention based on social cognition theory, and employer brand attractiveness as mediator, and corporate social responsibility and prosocial behavior as moderator. In this study, the first-line servers in the service industry were sampled, and the valid questionnaires were completed by 217 servers. The valid questionnaire response rate was 62%. The results of the study show that: (1) Green human resource management had negative impact to turnover intention by employer brand attractiveness.(2) High corporate social responsibility strengthens the positive effect on green human resource management to employer brand attractiveness.(3) High prosocial behavior strengthens the positive effect on green human resource management to employer brand attractiveness.

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47

Shi, Cheng-Hong y 施程鴻. "Diesel Car Purchasing Behavior Intention Analysis". Thesis, 2010. http://ndltd.ncl.edu.tw/handle/01708173431188311937.

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碩士
國立交通大學
運輸科技與管理學系
98
When Taiwan government canceled the limitation of diesel cars and allowed it to import and sell in 2004, the annual sale volumes of the diesel cars in Taiwan grew times than before. The researches for purchasing of diesel cars are very rare. This paper attempts to study the factors which influence the behavior intention of purchasing diesel cars. In this study, we design the questionnaire based on the Theory of Planning Behavior. After referencing to the literature, we added two dimensions, “Product Quality” and “Perceived Risk”, to the research structure. Rasch model can present important factors from different clusters of samples. By using regression and ANOVA, this study explored the influence extent of the consumers’ intention to purchasing diesel cars in different aspects. The study survey was conducted in the BBS (PTT) and the visitors to 20th Taipei INT’L Auto Show 2010 during December, 2009. The effective sample size is 1009. This study is the first study that analyzes the consumers’ behavior of purchasing diesel cars by Rasch model. The results of regression analysis support all the assumptions, and the R2 is as high as 51.2%. The results from the Rasch model analysis indicate that “Behavioral Intention” is that the consumers generally have the willingness to buy diesel cars. “Product Quality” said that, safety and fuel-efficient are more important than the brand. “Attitude” said that consumers agree that the diesel cars are energy saving and environment protecting. “Subjective Norms” was informed that relatives generally agree diesel cars. The purchasing budget of consumers are 600 thousand to one million NT dollars, so the companies could launch cheaper diesel cars. The government could popularize more environmental protection concepts to increase the consumers’ purchasing intentions. The most important factor that affects consumers' behaviors of purchasing diesel cars in Taiwan is environmental factor, and it is different from the result mentioned in Lee (2009) that consumers in South Korea take the operating costs as the key factor. This study may provide reference on marketing for the government and automobile-related industry as a future policy or strategy.

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48

Choi, Hye Jeong. "Factors affecting how individuals explain their behavioral intention to others". Diss., 2008.

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Thesis (M.A.)--Michigan State University. Dept. of Advertising, Public Relations & Retailing, 2008.
"[Academic advisory] Dr. Hee Sun Park"--Acknowledgments. Title from PDF t.p. (viewed on Aug. 6, 2009) Includes bibliographical references (p. 99-102). Also issued in print.

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49

Huang, Alan y 黃雅楠. "Silhouette-Based Human Behavior Analysis". Thesis, 2005. http://ndltd.ncl.edu.tw/handle/32491999308904038040.

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碩士
國立交通大學
資訊工程系所
93
In this thesis, we develop a real-time visual surveillance system for human behavior analysis. It operates on monocular color-scale video imagery with a stationary background scene. At the first step in the system process, it extracts the silhouette of the target object by traditional video tracking method, background subtraction combined with Gaussian Mixture Model ( GMM ). And furthermore, it detects the contour of the object silhouette. At the second step, the system employs a combination of shape analysis and geometry analysis on the contour to decompose the detected silhouette to several undefined parts（unlabeled body parts）. After the decomposition process, it labels each the separated part （head、torso、hands、feet）by the use of our hierarchical statistical-shape-similarity algorithm ( HSSS ). As the above steps have been processed successfully, the last step in our system is to extract local features of the detected body part（orientation、centroid. . . etc）, and the global features of the entire silhouette（aspect ratio、 block density. . . etc）, and then these features can be used to guide the high-level human behavior analysis. In the on-line behavior analysis process, an unknown sequence will be matched with the templates collected in our database. The database is established offline by the use of real video captures, which is a group of labeled reference sequence representing typical behaviors. In short, our system can detect the human body parts and classify the posture of human at individual imagery, then identify the event of a query sequence which involves human beings. It runs at 20~25Hz for 240 x 160 resolution images on a single Pentium-M 1600Mhz PC.

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50

Chiang, Lung-Hsing y 江龍興. "Silhouette Analysis for Human Behavior Recognition". Thesis, 2010. http://ndltd.ncl.edu.tw/handle/17797410687087172255.

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碩士
國立臺灣科技大學
電子工程系
98
Human skeleton extraction and matching shape sequences from video are important issues in visual surveillance. In this research, we propose a novel human skeleton extraction scheme and apply this scheme to human fall detection scheme. On matching shape sequences, we investigate the dynamic elastic matching to solve this issue. The dynamic elastic matching is an iterative technique for gradually transforming of two shape sequences. We conduct intensive experiments to verify the proposed schemes based on the public domain gait database. The experimental results show that the dynamic elastic matching yield satisfactory performance in the detection rate in comparison with the state of the art approaches at the expense of high computational costs.

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