Personalization of Emotion Recognition for Everyday Life Using Physiological Signals From Wearables

Emotion recognition from physiological signals has many promising applications, but it remains an unsolved challenge. There are many scientific papers trying to construct general systems suitable for the entire population. Our research focuses on the subject-oriented approach and strategies for pers...

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Bibliographic Details
Published in2022 10th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) pp. 1 - 5
Main Author Perz, Bartosz
Format Conference Proceeding
LanguageEnglish
Published IEEE 18.10.2022
Subjects
Affective computing
Conferences
Deep architecture
deep learning
Emotion recognition
Fuses
personalization
physiological signals
Sociology
validation
Wearable computers
wearables
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Summary:Emotion recognition from physiological signals has many promising applications, but it remains an unsolved challenge. There are many scientific papers trying to construct general systems suitable for the entire population. Our research focuses on the subject-oriented approach and strategies for personalization of emotion recognition. Our solutions will fuse generalized and individualized deep learning architectures in different ways. For experimental purposes, we will collect unique datasets gathered in everyday life using various wearables: smartwatches and chest straps. We will also develop appropriate validation procedures capable of measuring both the personalization and generalization properties of the considered methods. This paper describes the problem's background, its scope, and our research methodology. Additionally, we present our achievements so far and our future work.
DOI:10.1109/ACIIW57231.2022.10086031