Hybrid affective computing—keyboard, mouse and touch screen: from review to experiment

Abstract

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.

Student behavior analysis to measure engagement levels in online learning environments

“…They are also expensive and are neither easily available nor usable in daily life. In recent studies, NLP and voice recognition have not yielded satisfactory results due to cultural and linguistic differences [10].…”

Special issue on affective design using big data

“…In line with the technologies of big data, affective computing has been examined over the past few years including product design (Ayas 2011; Koutsabasis and Istikopoulou 2013), fashion design (Sokolova and Fernández-Caballero 2015), web design (Koutsabasis and Istikopoulou 2013), media communication (Bergen and Ross 2013;Cao et al 2014), computer game (Yannakakis et al 2014), human computer interaction (Bakhtiyari, Taghavi, and Husain 2015;Park and Zhang 2015), service development (Hensher 2014; Morris and Guerra 2015) and urban landscape design. From the literature, a growing interest in mining multi-disciplinary affective data by both researchers and industry can be seen.…”

New Insights Towards Developing Recommender Systems

“… Explicit: the input data comes from factual data about items or users (e.g., item features, user demographic data and time) or a direct user feedback, such as ratings to items made by users.  Implicit: the input data is based on the behavioral usage such as a user's purchase behavior, browser session location, number of times a user has heard a song or detection of user's feeling about the song [18].…”