The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Human Emotion Recognition: Review of Sensors and Methods
Open accessAbstract
Automated emotion recognition (AEE) is an important issue in various fields of activities which use human emotional reactions as a signal for marketing, technical equipment, or human–robot interaction. This paper analyzes scientific research and technical papers for sensor use analysis, among various methods implemented or researched. This paper covers a few classes of sensors, using contactless methods as well as contact and skin-penetrating electrodes for human emotion detection and the measurement of their intensity. The results of the analysis performed in this paper present applicable methods for each type of emotion and their intensity and propose their classification. The classification of emotion sensors is presented to reveal area of application and expected outcomes from each method, as well as their limitations. This paper should be relevant for researchers using human emotion evaluation and analysis, when there is a need to choose a proper method for their purposes or to find alternative decisions. Based on the analyzed human emotion recognition sensors and methods, we developed some practical applications for humanizing the Internet of Things (IoT) and affective computing systems.
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Citations
Cites
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“To andle this issue, the majority of studies of emotion eval ation f cuses on other classifications [3,21], which i clude dimensions of emotions, in most cases valence (activation—negati /positive) and arousal (high/low) [23,24], and analyses only basic emotions which can be defined more easily.”
BackgroundEkman, P., & Friesen, W. V. (2019) · PsycTESTS Dataset · doi:10.1037/t27734-000 · Cited by 6,019
What are emotions? And how can they be measured?
Scherer, K. R. (2005) · Social Science Information · doi:10.1177/0539018405058216 · Cited by 4,264
Photoplethysmography and its application in clinical physiological measurement
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Looking at pictures: Affective, facial, visceral, and behavioral reactions
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Detecting Stress During Real-World Driving Tasks Using Physiological Sensors
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A review of affective computing: From unimodal analysis to multimodal fusion
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Publication recommendations for electrodermal measurements
Society for Psychophysiological Research Ad Hoc Committee on Electrodermal Measures (2012) · Psychophysiology · doi:10.1111/j.1469-8986.2012.01384.x · Cited by 1,288
Emotion recognition system using short-term monitoring of physiological signals
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Wearable photoplethysmographic sensors—past and present
Tamura, T., Maeda, Y., Sekine, M., & Yoshida, M. (2014) · Electronics · doi:10.3390/electronics3020282 · Cited by 912 · cited 3×
“(i) the static part of signal depends on the structure of the tissue and the average blood volume arterial and venous blood, and it varies very slowly depending on respiration; (ii) the dynamic part represents changes in the blood volume that occurs between the systolic and diastolic phases of the cardiac cycle [104].”
BackgroundInfluential
Cited by
The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise
Nicolò, A., Massaroni, C., Schena, E., & Sacchetti, M. (2020) · Sensors · doi:10.3390/s20216396 · Cited by 440
Recent development of respiratory rate measurement technologies
Liu, H., Allen, J., Zheng, D., & Chen, F. (2019) · Physiological Measurement · doi:10.1088/1361-6579/ab299e · Cited by 266
Advanced Applications of Industrial Robotics: New Trends and Possibilities
Dzedzickis, A., Subačiūtė-Žemaitienė, J., Šutinys, E., Samukaitė-Bubnienė, U., & Bučinskas, V. (2021) · Applied Sciences · doi:10.3390/app12010135 · Cited by 228
Self-citationDeepVANet: A Deep End-to-End Network for Multi-modal Emotion Recognition
Zhang, Y., Hossain, M. Z., & Rahman, S. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-85613-7_16 · Cited by 29
Hung, C.-L., Lin, Y.-L., Chou, C.-M., & Wang, C.-J. (2023) · Healthcare · doi:10.3390/healthcare11020157 · Cited by 26
Metadata
- Authors
- Dzedzickis, A., Kaklauskas, A., & Bucinskas, V.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2020-01-21
- Volume
- 20
- Issue
- 3
- Pages
- 592
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W3001587372
- Cited by
- 582
- References
- 15
- FWCI
- 70.09
- Type
- journal article
- Language
- en
- Open access
- gold
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