Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems

Purpose The purpose of this paper is to propose a novel emotion recognition algorithm from multimodal physiological signals for emotion aware healthcare systems. In this work, physiological signals are collected from a respiratory belt (RB), photoplethysmography (PPG), and fingertip temperature (FTT...

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Bibliographic Details
Published in:Journal of medical and biological engineering 2020-04, Vol.40 (2), p.149-157
Main Authors: Ayata, Değer, Yaslan, Yusuf, Kamasak, Mustafa E.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Arousal
Biomedical Engineering and Bioengineering
Cell Biology
Decision trees
Emotion recognition
Emotions
Engineering
Health care
Imaging
Learning algorithms
Machine learning
Original Article
Osmosis
Physiology
Plethysmography
Radiology
Signal classification
Support vector machines
Wearable technology
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Summary:Purpose The purpose of this paper is to propose a novel emotion recognition algorithm from multimodal physiological signals for emotion aware healthcare systems. In this work, physiological signals are collected from a respiratory belt (RB), photoplethysmography (PPG), and fingertip temperature (FTT) sensors. These signals are used as their collection becomes easy with the advance in ergonomic wearable technologies. Methods Arousal and valence levels are recognized from the fused physiological signals using the relationship between physiological signals and emotions. This recognition is performed using various machine learning methods such as random forest, support vector machine and logistic regression. The performance of these methods is studied. Results Using decision level fusion, the accuracy improved from 69.86 to 73.08% for arousal, and from 69.53 to 72.18% for valence. Results indicate that using multiple sources of physiological signals and their fusion increases the accuracy rate of emotion recognition. Conclusion This study demonstrated a framework for emotion recognition using multimodal physiological signals from respiratory belt, photo plethysmography and fingertip temperature. It is shown that decision level fusion from multiple classifiers (one per signal source) improved the accuracy rate of emotion recognition both for arousal and valence dimensions.
ISSN:1609-0985
2199-4757
DOI:10.1007/s40846-019-00505-7