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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source doesn’t support the claim as written.
In the citing paper
“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]
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe cited source doesn’t support the claim as written.
In the citing paper
“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]
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
(1992) · Cited by 23,431
Peng, H., Long, F., & Ding, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.159 · Cited by 10,818
(1974) · Medical Entomology and Zoology · Cited by 7,295
Core affect and the psychological construction of emotion.
Russell, J. A. (2003) · Psychological Review · doi:10.1037/0033-295x.110.1.145 · Cited by 6,113
(2005) · CTIT technical reports series · Cited by 5,307
DEAP: A Database for Emotion Analysis ;Using Physiological Signals
Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., & Patras, I. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.15 · Cited by 5,093
Toward machine emotional intelligence: analysis of affective physiological state
Picard, R. W., Vyzas, E., & Healey, J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.954607 · Cited by 2,334
Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects
Lahat, D., Adali, T., & Jutten, C. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2460697 · Cited by 1,296
Wearable photoplethysmographic sensors—past and present
Tamura, T., Maeda, Y., Sekine, M., & Yoshida, M. (2014) · Electronics · doi:10.3390/electronics3020282 · Cited by 912
THE JOURNAL OF INTERNATIONAL MEDICAL RESEARCH
Şenol, V., Ünalan, D., Akça, R. P., & Baştürk, M. (2018) · Cited by 896
Research works printed but not linked: 2
- Popat, K. A. & Sharma, P. (2013). Wearable computer applications: A feature perspective. International Journal of Engineering and Technology Innovation, 3(1). https://pdfs.semanticscholar.org/ec9c/33e897e38e691f60baf828303a0ae64e1899.pdf.
- Peng, H., Ding, C., & Long, F. (2005) Minimum redundancy maximum relevance feature selection (MRMR). IEEE Intelligent Systems, 20. https://www.researchgate.net/publication/237133616_Minimum_Redundancy-_Maximum_Relevance_Feature_Selection. Accessed May 2018.
Cited by
Sorted by citations
A comprehensive survey on sentiment analysis: Approaches, challenges and trends
Birjali, M., Kasri, M., & Beni‐Hssane, A. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107134 · Cited by 1,058
A Comprehensive Survey on TinyML
Abadade, Y., Temouden, A., Bamoumen, H., Benamar, N., Chtouki, Y., & Hafid, A. S. (2023) · IEEE Access · doi:10.1109/access.2023.3294111 · Cited by 366
“The authors in [104] and [105] aimed to construct a smart wearable device to achieve the recognition of emotions through physiological signals emitted by the human body.”
BackgroundMethodMachine Learning for Healthcare Wearable Devices: The Big Picture
Sabry, F., Eltaras, T., Labda, W., Alzoubi, K., & Malluhi, Q. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/4653923 · Cited by 321
Multimodal Emotion Recognition with Deep Learning: Advancements, challenges, and future directions
Vijayaraghavan, G., Mala, T., Dhanasekaran, P., & Uma, E. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102218 · Cited by 263
Cross-Subject Multimodal Emotion Recognition Based on Hybrid Fusion
Çimtay, Y., Ekmekcioǧlu, E., & Özhan, Ş. Ç. (2020) · IEEE Access · doi:10.1109/access.2020.3023871 · Cited by 207
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
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
