Arousal-Valence Classification from Peripheral Physiological Signals Using Long Short-Term Memory Networks

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The automated recognition of human emotions plays an important role in developing machines with emotional intelligence. However, most of the affective computing models are based on images, audio, videos and brain signals. There is a lack of prior studies that focus on utilizing only peripheral physiological signals for emotion recognition, which can ideally be implemented in daily life settings using wearables, e.g., smartwatches. Here, an emotion classification method using peripheral physiological signals, obtained by wearable devices that enable continuous monitoring of emotional states, is presented. A Long Short-Term Memory neural network-based classification model is proposed to accurately predict emotions in real-time into binary levels and quadrants of the arousal-valence space. The peripheral sensored data used here were collected from 20 participants, who engaged in a naturalistic debate. Different annotation schemes were adopted and their impact on the classification performance was explored. Evaluation results demonstrate the capability of our method with a measured accuracy of >93% and >89% for binary levels and quad classes, respectively. This paves the way for enhancing the role of wearable devices in emotional state recognition in everyday life.
Publisher
IEEE
Issue Date
2021-11-01
Language
English
Citation

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp.686 - 689

ISSN
1557-170X
DOI
10.1109/embc46164.2021.9630252
URI
http://hdl.handle.net/10203/312235
Appears in Collection
CS-Conference Papers(학술회의논문)
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