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Erschienen in: International Journal of Machine Learning and Cybernetics 3/2021

14.10.2020 | Original Article

A knowledge discovery and visualisation method for unearthing emotional states from physiological data

verfasst von: Nectarios Costadopoulos, Md Zahidul Islam, David Tien

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 3/2021

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Abstract

In this paper we propose a knowledge discovery and visualisation method for unearthing emotional states from physiological data typically available from wearable devices. In addition we investigate the viability of using a limited set of wearable sensors to extract decision tree rules which are representative of physiological changes taking place during emotional changes. Our method utilised a fusion of pre-processing and classification techniques using decision trees to discover logic rules relating to the valence and arousal emotional dimensions. This approach normalised the signal data in a manner that enabled accurate classification and generated logic rules for knowledge discovery. Furthermore, the use of three target classes for the emotional dimensions was effective at denoising the data and further enhancing classification and useful rule extraction. There are three key contributions in this work, firstly an exploration and validation of our knowledge discovery methodology, secondly successful extraction of high accuracy rules derived from physiological data and thirdly knowledge discovery and visualisation of relationships within-participant physiological data that can be inferred relating to emotions. Additionally, this work may be utilised in areas such as the medical sciences where interpretable rules are required for knowledge discovery.

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Metadaten
Titel
A knowledge discovery and visualisation method for unearthing emotional states from physiological data
verfasst von
Nectarios Costadopoulos
Md Zahidul Islam
David Tien
Publikationsdatum
14.10.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 3/2021
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-020-01205-4

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