SigRep: Towards Robust Wearable Emotion Recognition with Contrastive Representation Learning

Article

Dissanayake, Vipula, Seneviratne, Sachith, Rana, Rajib, Wen, Elliot, Kaluarachchi, Tharindu and Nanayakkara, Suranga. 2022. "SigRep: Towards Robust Wearable Emotion Recognition with Contrastive Representation Learning." IEEE Access. 10, pp. 18105-18120. https://doi.org/10.1109/ACCESS.2022.3149509
Article Title

SigRep: Towards Robust Wearable Emotion Recognition with Contrastive Representation Learning

ERA Journal ID210567
Article CategoryArticle
AuthorsDissanayake, Vipula (Author), Seneviratne, Sachith (Author), Rana, Rajib (Author), Wen, Elliot (Author), Kaluarachchi, Tharindu (Author) and Nanayakkara, Suranga (Author)
Journal TitleIEEE Access
Journal Citation10, pp. 18105-18120
Number of Pages16
Year2022
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN2169-3536
Digital Object Identifier (DOI)https://doi.org/10.1109/ACCESS.2022.3149509
Web Address (URL)https://ieeexplore.ieee.org/document/9706192
Abstract

Extracting emotions from physiological signals has become popular over the past decade. Recent advancements in wearable smart devices have enabled capturing physiological signals continuously and unobtrusively. However, signal readings from different smart wearables are lossy due to user activities, making it difficult to develop robust models for emotion recognition. Also, the limited availability of data labels is an inherent challenge for developing machine learning techniques for emotion classification. This paper presents a novel self-supervised approach inspired by contrastive learning to address the above challenges. In particular, our proposed approach develops a method to learn representations of individual physiological signals, which can be used for downstream classification tasks. Our evaluation with four publicly available datasets shows that the proposed method surpasses the emotion recognition performance of state-of-the-art techniques for emotion classification. In addition, we show that our method is more robust to losses in the input signal.

Keywordsemotion recognition, representation learning, self-supervised learning, wearable signals
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460212. Speech recognition
461199. Machine learning not elsewhere classified
Byline AffiliationsUniversity of Auckland, New Zealand
University of Melbourne
School of Mathematics, Physics and Computing
Institution of OriginUniversity of Southern Queensland
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