The DOI matches its Crossref record; 33 of the 34 references it cites were found in a registry or an index.
Emotion Recognition from Multimodal Physiological Signals for Emotion Aware Healthcare Systems
Open accessAbstract
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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“The entire process was implemented using computer aided diagnosis, which was later transfered in a small memory footprint that runs in an edge platform [104].”
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“The authors in [104] and [105] introduce TinyML as a medical embedded system for the diagnosis of of focal liver lesion (FLL) using a DNN model trained on liver images that were priory obtained using ultrasound imaging.”
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Metadata
- Authors
- Ayata, D., Yaslan, Y., & Kamasak, M. E.
- Affiliations
- Istanbul Technical University (TR)
- Corresponding author
- Ayata, Değer
- Venue
- Journal of Medical and Biological Engineering
- Published
- 2020-01-07
- Volume
- 40
- Issue
- 2
- Pages
- 149-157
- ISSN
- 1609-0985 (ISSN-L)
- 2199-4757
- 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
- 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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