Paper Info
Title
Perception Clusters: Automated Mood Recognition Using a Novel Cluster-Driven Modelling System
Abstract
AbstractAutomated 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.
Year
DOI
Venue
2021
10.1145/3422819
ACM Transactions on Computing for Healthcare
DocType
Volume
Issue
Journal
2
1
ISSN
Citations 
PageRank 
2691-1957
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Aftab Khan174.49
Alexandros Zenonos281.93
Georgios Kalogridis300.34
Yaowei Wang413429.62
Stefanos Vatsikas500.34
Mahesh Sooriyabandara600.34