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Please use this identifier to cite or link to this item: http://dspace.vgtu.lt/handle/1/459

Title: Web-Based Biometric Mouse Intelligent System for Analysis of Emotional State and Labour Productivity
Authors: Zavadskas, Edmundas Kazimieras
Kaklauskas, Artūras
Seniut, Mark
Dzemyda, Gintautas
Ivanikovas, Sergėjus
Stankevič, Voitech
Šimkevičius, Česlovas
Jaruševičius, Aurimas
Keywords: biometric techniques
web-based
biometric mouse
intelligent system
emotional and labour productivity analysis
subsystems
historical information
Issue Date: 2008
Publisher: Vilniaus Gedimino technikos universitetas
Citation: Zavadskas, E. K.; Kaklauskas, A.; Seniut, M.; Dzemyda, G.; Ivanikovas, S.; Stankevič, V.; Šimkevičius, Č.; Jaruševičius, A. 2008. Web-based biometric mouse intelligent system for analysis of emotional state and labour productivity, in The 25th International Symposium on Automation and Robotics in Construction ISARC–2008: Selected papers, Ed. by E. Zavadskas, A. Kaklauskas, M. J. Skibniewski, June 26–29, 2008, Vilnius, Lithuania. Vilnius: Technika, 429–434. doi:10.3846/isarc.20080626.429
Abstract: This paper describes the Web-based Biometric Mouse Intelligent (WBMI) System developed by the authors for measuring and analysis of user’s emotions and labour productivity with a biometric mouse. The research included development of the WBMI System, which works in the background and is able to assess user‘s emotional state and labour productivity during work with a computer. The system captures information about user’s emotional state and labour productivity using three main biometric techniques: physiological (skin conductance, amplitude of hand tremble, skin temperature), psychological (e-self-reports) and behavioural/motor-behavioural (mouse pressure, speed of mouse pointer movement, acceleration of mouse pointer movement, scroll wheel turns, right- and left-click frequency). The system extracts physiological and motor-behavioural parameters from mouse actions and palm characteristics, and the user fills in the psychological (e-self-reports) data, which can be used to analyse correlations with user’s emotional state and labour productivity. Main features of the WBMI System are discussed, and the final recommendations for future research and improvement are included.
URI: http://dspace1.vgtu.lt/handle/1/459
ISBN: 978-9955-28-329-4
Appears in Collections:Konferencijų straipsniai

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