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 in [104] was implemented using computer aided diagnosis, later transferred in a small memory footprint that runs on an edge platform.
“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].”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [104]
The Introduction (page unavailable) describes a cloud-based emotion-aware healthcare system: physiological signals are sent to the cloud for machine-learning analysis. That is contrary to the attributed edge-platform implementation. The passage also does not describe a computer-aided diagnosis process or a later transfer to a small-memory device. (LLM, conf=0.79)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredAuthors in [104] and [105] introduce TinyML as a medical embedded system for diagnosis of focal liver lesion (FLL) using a DNN model trained on liver images obtained using ultrasound imaging.
“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.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [104]
The Introduction describes the proposed system as an emotion-aware healthcare framework that collects respiration, photoplethysmography, and fingertip-temperature data. Section 4.4 identifies its classifiers as random forest, support vector machine, and logistic regression for emotion recognition—not a DNN trained on ultrasound liver images to diagnose focal liver lesions. No page numbers are available in our copy. (LLM, conf=0.88)
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Experimental — accuracy not yet measured
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- 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
- Social Sciences › Psychology › Experimental and Cognitive Psychology › 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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