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Bodily sensation maps: exploring a new direction for detecting emotions from user self-reported data
Authors:García-Magariño I., Chittaro L., Plaza I.
Published in:International Journal of Human-Computer Studies, 113, May 2018, pp. 32–47.
Abstract:The ability of detecting emotions is essential in different fields such
as user experience (UX), affective computing, and psychology. This paper
explores the possibility of detecting emotions through user-generated
bodily sensation maps (BSMs). The theoretical basis that inspires this
work is the proposal by Nummenmaa et al. (2014) of BSMs for 14 emotions.
To make it easy for users to create a BSM of how they feel, and
convenient for researchers to acquire and classify users’ BSMs, we
created a mobile app, called EmoPaint. The app includes an interface for
BSM creation, and an automatic classifier that matches the created BSM
with the BSMs for the 14 emotions. We conducted a user study aimed at
evaluating both components of EmoPaint. First, it shows that the app is
easy to use, and is able to classify BSMs consistently with the
considered theoretical approach. Second, it shows that using EmoPaint
increases accuracy of users’ emotion classification when compared with
an adaptation of the well-known method of using the Affect Grid with the
Circumplex Model, focused on the same set of 14 emotions of Nummenmaa
et al. Overall, these results indicate that the novel approach of using
BSMs in the context of automatic emotion detection is promising, and
encourage further developments and studies of BSM-based methods.