Affective states recognition through touch dynamics

This work exploits Touch Dynamics to recognize affective states of a user while using a mobile device. To the aim, the acquired touch pattern is segmented in swipes, successively a wide set of handcrafted features is computed to characterize the swipe. The affective analysis is obtained through machine learning techniques. Data have been collected developing a specific App designed to acquire common unlock Android touch patterns. In this way the user interaction has been preserved as the more natural and neutral possible in real environments. Affective state labels have been obtained adopting a well-known psychological questionnaire. Three affective states have been considered: anxiety, stress and depression. Tests, performed on 115 users, reported an overall accuracy of 73.6% thus demonstrating the viability of the proposed approach.