Biometric Mouse Intelligent System for Student's Emotional and Examination Process Analysis

Kaklauskas, Artūras; Krutinis, Mindaugas; Seniut, Mark

doi:10.1109/icalt.2009.130

Cited by 16 publications

(12 citation statements)

References 5 publications

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“…The main statements of the dissertation were published in 12 scientific articles: with Science Citation Index Expanded (accessed via Web of Science) ( Kaklauskas et al 2010aKaklauskas et al , 2011aKaklauskas et al , 2012Kaklauskas et al , 2013Kanapeckienė et al 2010;Tupėnaitė et al 2010;Urbanavičienė et al 2009) and 5 -in the proceedings in Thomson ISI database (Kaklauskas et al 2007b(Kaklauskas et al , 2008c(Kaklauskas et al , 2009a(Kaklauskas et al , 2009bZavadskas et al 2008c), 1 Lithuanian patent No: LT 5673 on behalf of VGTU was acquired.…”

Section: Approval Of the Resultsmentioning

confidence: 99%

Development of the intelligent tutoring system for built environment

Seniut¹

2012

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The dissertation examines the problem of students' training and testing. To solve the problem, new model, algorithms and system have been developed and adapted. The developed system enables organizing of individual and group training and self-control process. Integrated intelligent search subsystem helps to develop the study plan for the themes of interest. The system continuously monitors the user and can respond to his failure. The developed built environment computer learning subsystem performs practical calculations to assess the built environment projects. The main problem solved by development of the subsystem is reduction of time needed for users to create their own systems. Knowledge database created during the system development can be a good source of knowledge for new or similar projects evaluations. Calculation results can be saved for further research or shared with other users of the system. The dissertation consists of Introduction, 4 Chapters, Conclusions, References, and List of Author's Publications. The introduction reveals the investigated problem, importance of the thesis and the object of research and describes the purpose and tasks of the dissertation, research methodology, scientific novelty, the practical significance of results examined in the dissertation and defended statements. The introduction ends in presenting the author's publications on the subject of the defended dissertation, offering the material of made presentations in conferences and defining the structure of the dissertation. Chapter 1 revises scientific literature. The world experience in development of intelligent tutoring systems is analyzed. Also definitions of intelligent tutoring systems and the built environment are provided. Chapter 2 overviews the multi objective assessment methods and discusses their integration into the overall system. Chapter 3 presents the model of intelligent tutoring system for built environment. All parts off developed system are reviewed including methods, submodels, algorithms and databases. Chapter 4 presents three real life case studies that show practical application results of the created system. 12 articles focusing on the subject of the discussed dissertation are published: 7 articles-in the Thomson ISI Web of Science register and 5 articlesin the proceedings in Thomson ISI data base. Patent "Electronic information retrieval system and method" on behalf of VGTU was acquired.

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Section: Approval Of the Resultsmentioning

confidence: 99%

Development of the intelligent tutoring system for built environment

Seniut¹

2012

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“…Kaklauskas et al [22,23] developed a comprehensive mouse sensor system that evaluates users' psychological, physiological and behavioral input for the detection of users' stress level. The system records the location, speed and distance, hand shaking, and force pressure from motions of a mouse.…”

Section: B Related Work:mouse Movement Analysis For Affectivementioning

confidence: 99%

Mouse Trajectories and State Anxiety: Feature Selection with Random Forest

Yamauchi

2013

2013 Humaine Association Conference on Affective Computing and Intelligent Interaction

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Do users' mouse activities reveal their affective states, as other bodily expressions such as postures and gestures signal emotions? When people are frustrated while trying to solve a puzzle or math problem, their frustration can be manifested in the way they use a computer mouse, such as pressing a button hard. But when a user is engaged in an innocuous and mundane task, what mouse activities provide a clue to detect affective states? To address these questions, we extracted 134 mouse trajectory variables in a choice-reaching experiment (N=234, female = 137, male = 97) and selected 3~8 key features by applying random forest regression. Using Spielberger's State Anxiety Inventory, we investigated the extent to which the selected trajectory features predict state anxiety of new subjects (N = 133; female = 75, male = 58). Results indicate that distributions of temporal features (e.g., velocity) as well as spatial characteristics (e.g., direction change) are indicative of users' state anxiety. A theoretical rationale, pros and cons of using mouse movement analysis and the role of other psychological variable for mouse-based affective computing are also discussed.

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“…In [159], the author aims to introduce an eCommerce system that is aware of the user emotion via a combination of speech, facial image, motor functioning and body gesture. Kaklauskas et al [160] use an intelligent mouse-based system to monitor students' emotional state, while in [161], the authors propose a combination of keystroke and mouse dynamics to estimate affective state and communication preference in a given workspace in order to improve worker efficiency. In [162], the authors use a mobile phone platform to detect user emotion via data acquired from the user's social media applications.…”

Section: Prediction Of Higher Level Individual Characteristicsmentioning

confidence: 99%

Predictive biometrics: a review and analysis of predicting personal characteristics from biometric data

Fairhurst

Abreu

2017

IET biom.

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Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, 'higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future.

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Biometric Mouse Intelligent System for Student's Emotional and Examination Process Analysis

Cited by 16 publications

References 5 publications

Development of the intelligent tutoring system for built environment

Development of the intelligent tutoring system for built environment

Mouse Trajectories and State Anxiety: Feature Selection with Random Forest

Predictive biometrics: a review and analysis of predicting personal characteristics from biometric data

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