Cited by 182Emotion and Mood RecognitionNon-Invasive Vital Sign MonitoringHeart Rate Variability and Autonomic Control

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Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems

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Ayata, Değer, Yaslan, Yusuf, Kamasak, Mustafa E.·2020·Journal of Medical and Biological Engineering

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Abstract

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.

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Authors: Ayata, D., Yaslan, Y., & Kamasak, M. E.
Affiliations: Istanbul Technical University (TR)
Corresponding author: Ayata, Değer
ORCID: Ayata, Değer
Yaslan, Yusuf
Kamasak, Mustafa E.
Venue: Journal of Medical and Biological Engineering
Publisher: Springer Science and Business Media LLC
Published: 2020-01-07
Volume: 40
Issue: 2
Pages: 149-157
ISSN: 1609-0985 (ISSN-L)
2199-4757
DOI: 10.1007/s40846-019-00505-7
OpenAlex: W2999294457
Cited by: 182
References: 34
Citations by year: 2026: 11 · 2025: 57 · 2024: 36 · 2023: 38 · 2022: 21 · 2021: 15 · 2020: 4
FWCI: 18.84
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: Journal of Medical and Biological Engineering (publishedVersion, cc-by)
Istanbul Technical University Academic Open Archive (Istanbul Technical University) (submittedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
Keywords: emotion recognition, multimodal physiological signals, valence and arousal, decision fusion, photoplethysmography, fingertip temperature, wearable sensors, classifier combination

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Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems

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