A novel self-supervised contrastive learning approach improves emotion recognition performance and robustness from wearable physiological signals compared to state-of-the-art techniques.
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.
Dissanayake et al. (Sat,) studied this question.
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