A Piezoelectric Touch Sensing and Random Forest Based Technique for
Emotion Recognition
Abstract
Emotion recognition, a process of automatic cognition of human emotions,
has great potential to improve the degree of social intelligence. Among
various recognition methods, Emotion recognition based on touch event’s
temporal and force information receives global interests. Although
previous studies have shown promise in the field of keystroke-based
emotion recognition, they are limited by the need for long-term text
input and the lack of high-precision force sensing technology, hindering
their real-time performance and wider applicability. To address this
issue, in this paper, a piezoelectric-based keystroke dynamic technique
is presented for quick emotion detection. The nature of piezoelectric
materials enables high-resolution force detection. Meanwhile, the data
collecting procedure is highly simplified because only the password
entry is needed. International Affective Digitized Sounds (IADS) are
applied to elicit users’ emotions, and a PAD emotion scale is used to
evaluate and label the degree of emotion induction. A Random Forest (RF)
based algorithm is used in order to reduce the training dataset and
improve algorithm portability. Finally, an average recognition accuracy
of 79.37% of 4 emotions (happiness, sadness, fear, disgust) is
experimentally achieved. The proposed technique improves the reliability
and practicability of emotion recognition in realistic social systems.