Perception Clusters

Abstract

Automated mood recognition has been studied in recent times with great emphasis on stress in particular. Other affective states are also of great importance, as studying them can help in understanding human behaviours in more detail. Most of the studies conducted in the realisation of an automated system that is capable of recognising human moods have established that mood is personal—that is, mood perception differs amongst individuals. Previous machine learning--based frameworks confirm this hypothesis, with personalised models almost always outperforming the generalised methods. In this article, we propose a novel system for grouping individuals in what we refer to as “perception clusters” based on their physiological signals. We evaluate perception clusters with a trial of nine users in a work environment, recording physiological and activity data for at least 10 days. Our results reveal no significant difference in performance with respect to a personalised approach and that our method performs equally better against traditional generalised methods. Such an approach significantly reduces computational requirements that are otherwise necessary for personalised approaches requiring individual models developed separately for each user. Further, perception clusters manifest a direction towards semi-supervised affective modelling in which individual perceptions are inferred from the data.