Human Computer Interaction (HCI) can be made more efficient if the interactive systems are able to respond to the users' emotional state. The foremost task for designing such systems is to recognize the users' emotional state during interaction. Most of the interactive systems, now a days, are being made touch enabled. In this work, we propose a model to recognize the emotional state of the users of touchscreen devices. We propose to compute the affective state of the users from 2D screen gesture using the number of touch events and pressure generated for each event as the only two features. No extra hardware setup is required for the computation. Machine learning technique was used for the classification. Four discriminative models, namely the Naïve Bayes, K-Nearest Neighbor (KNN), Decision Tree and Support Vector Machine (SVM) were explored, with SVM giving the highest accuracy of 96.75%.

Affective systems are supposed to improve user satisfaction and hence usability by identifying and complementing the affective state of a user at the time of interaction. The first and most important challenge for building such systems is to identify the affective state in a systematic way. This is generally done based on computational models. Building such models requires affective data. In spite of the extensive growth in this research area, there are a number of challenges in affect induction method for collecting the affective data as well as for building models for real-time prediction of affective states. In this article, we have reported a novel method for inducing particular affective states to unobtrusively collect the affective data as well as a minimalist model to predict the affective states of a user from her/his typing pattern on a touchscreen of a smartphone. The prediction accuracy for our model was 86.60%. The method for inducing the specific affective states and the model to predict these states are validated through empirical studies comprising EEG signals of twenty two participants.

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It is very important to know users' behavior to design and build effective interactive systems, tools, or applications. The behavioral study not only helps to assure the success of any design or product but also helps other researchers from various related areas. In this study, we have systematically collected and analyzed the behavioral data for smartphone usage by 1711 students of 188 academic institutions throughout India. We have observed students' behavior on smartphone usages both inside and outside the classroom. We conducted the study focusing on two aspects: to find the behavioral differences on the smartphone usage based on the gender, and academic level; and to identify the most frequently performed smartphone activities by the students inside and outside the classroom. Although there are few similarities with the existing related studies, we have found many dissimilarities as well. It is expected that the findings of the study will help many researchers from various fields including HCI, Mobile HCI, Behavioral Science, Psychology, and Education.