Discovering Emotional Logic Rules from Physiological Data of Individuals

Nectarios Costadopoulos, Zahid Islam, David Tien

Research output: Book chapter/Published conference paperConference paperpeer-review

3 Citations (Scopus)

Abstract

This paper discusses our work on discovering a set of emotional logic rules, derived from physiological data of individuals from a wearable technology perspective. We concentrated the analysis on physiological data such as plethysmography, respiration, galvanic skin response, and temperature that can be detected by wearable sensors. We sourced our data from the DEAP dataset, which is a popular labelled Affective Computing dataset. Our approach implemented a fusion of preprocessing and data mining techniques, to discover logic rules relating to the valence and arousal emotional dimensions. Our findings indicate that while there are similar changes in heart rates or galvanic skin response across individuals during emotional stimuli, every individual has a unique and quantifiable physiological reaction.

Original languageEnglish
Title of host publicationProceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019
Place of PublicationKobe, Japan
PublisherIEEE Computer Society
Pages468-474
Number of pages7
ISBN (Electronic)9781728128160
DOIs
Publication statusPublished - Jul 2019
Event18th International Conference on Machine Learning and Cybernetics, ICMLC 2019: ICMLC 2019 - Kobe, Japan
Duration: 07 Jul 201910 Jul 2019
https://translate.google.com/translate?hl=en&sl=ja&u=https://enotice.vtools.ieee.org/public/47253&prev=search (conference info)

Publication series

NameProceedings - International Conference on Machine Learning and Cybernetics
Volume2019-July
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Conference

Conference18th International Conference on Machine Learning and Cybernetics, ICMLC 2019
Country/TerritoryJapan
CityKobe
Period07/07/1910/07/19
Internet address

Fingerprint

Dive into the research topics of 'Discovering Emotional Logic Rules from Physiological Data of Individuals'. Together they form a unique fingerprint.

Cite this