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 publicationIn Proc. of the 18th International Conference on Machine Learning and Cybernetics (ICMLC), 2019
Place of PublicationKobe, Japan
Number of pages7
Publication statusPublished - 2019
Event18th International Conference on Machine Learning and
Cybernetics 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)


Conference18th International Conference on Machine Learning and
Cybernetics 2019
Internet address


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