caa a22 4500 606159142 CHVBK 20210128100623.0 cr unu---uuuuu 210128e20150801xx s 000 0 eng 10.1007/s00521-014-1790-y doi (NATIONALLICENCE)springer-10.1007/s00521-014-1790-y Hybrid affective computing—keyboard, mouse and touch screen: from review to experiment [Elektronische Daten] [Kaveh Bakhtiyari, Mona Taghavi, Hafizah Husain] Emotions play an important role in human interactions. They can be integrated into the computer system to make human-computer interaction (HCI) more effective. Affective computing is an innovative computational modeling and detecting user's emotions to optimize system responses in HCI. However, there is a trade-off between recognition accuracy and real-time performance in some of the methods such as processing the facial expressions, human voice and body gestures. Other methods lack efficiency and usability in real-world applications such as natural language processing and electroencephalography signals. To accomplish a reliable, usable and high-performance system, this paper proposes an intelligent hybrid approach to recognize users' emotions by using easily accessible and low computational cost input devices including keyboard, mouse (touch pad: single touch) and touch screen display (single touch). Using the proposed approach, the system is developed and trained in a supervised mode by artificial neural network and support vector machine (SVM) techniques. The result shows an increase in accuracy of 6% (93.20%) by SVM in comparison with the currently existing methods. It is a significant contribution to show new directions of future research in emotion recognition, user modeling and emotional intelligence. The Natural Computing Applications Forum, 2014 Affective computing nationallicence Human emotion recognition nationallicence Keyboard keystroke dynamics nationallicence Mouse touch pad movement nationallicence Touch screen monitor nationallicence Human-computer interaction (HCI) nationallicence Bakhtiyari Kaveh Interactive Systems, Department of Computer and Cognitive Science, Faculty of Engineering, University of Duisburg-Essen, 47048, Duisburg, North Rhine-Westphalia, Germany aut Taghavi Mona Department of Electrical, Electronics and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM (The National University of Malaysia), 43600, Bangi, Selangor Darul Ehsan, Malaysia aut Husain Hafizah Department of Electrical, Electronics and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM (The National University of Malaysia), 43600, Bangi, Selangor Darul Ehsan, Malaysia aut Neural Computing and Applications Springer London 26/6(2015-08-01), 1277-1296 0941-0643 26:6<1277 2015 26 521 https://doi.org/10.1007/s00521-014-1790-y text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1790-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Bakhtiyari Kaveh Interactive Systems, Department of Computer and Cognitive Science, Faculty of Engineering, University of Duisburg-Essen, 47048, Duisburg, North Rhine-Westphalia, Germany aut NATIONALLICENCE 700 1- Taghavi Mona Department of Electrical, Electronics and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM (The National University of Malaysia), 43600, Bangi, Selangor Darul Ehsan, Malaysia aut NATIONALLICENCE 700 1- Husain Hafizah Department of Electrical, Electronics and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM (The National University of Malaysia), 43600, Bangi, Selangor Darul Ehsan, Malaysia aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/6(2015-08-01), 1277-1296 0941-0643 26:6<1277 2015 26 521