• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Emotion Recognition using Short-Term Multi-Physiological Signals

Vol. 16, No. 3, March 31, 2022
10.3837/tiis.2022.03.018, Download Paper (Free):


Technology for emotion recognition is an essential part of human personality analysis. To define human personality characteristics, the existing method used the survey method. However, there are many cases where communication cannot make without considering emotions. Hence, emotional recognition technology is an essential element for communication but has also been adopted in many other fields. A person's emotions are revealed in various ways, typically including facial, speech, and biometric responses. Therefore, various methods can recognize emotions, e.g., images, voice signals, and physiological signals. Physiological signals are measured with biological sensors and analyzed to identify emotions. This study employed two sensor types. First, the existing method, the binary arousal-valence method, was subdivided into four levels to classify emotions in more detail. Then, based on the current techniques classified as High/Low, the model was further subdivided into multi-levels. Finally, signal characteristics were extracted using a 1-D Convolution Neural Network (CNN) and classified sixteen feelings. Although CNN was used to learn images in 2D, sensor data in 1D was used as the input in this paper. Finally, the proposed emotional recognition system was evaluated by measuring actual sensors.


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Cite this article

[IEEE Style]
T. Kang, "Emotion Recognition using Short-Term Multi-Physiological Signals," KSII Transactions on Internet and Information Systems, vol. 16, no. 3, pp. 1076-1094, 2022. DOI: 10.3837/tiis.2022.03.018.

[ACM Style]
Tae-Koo Kang. 2022. Emotion Recognition using Short-Term Multi-Physiological Signals. KSII Transactions on Internet and Information Systems, 16, 3, (2022), 1076-1094. DOI: 10.3837/tiis.2022.03.018.

[BibTeX Style]
@article{tiis:25532, title="Emotion Recognition using Short-Term Multi-Physiological Signals", author="Tae-Koo Kang and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2022.03.018}, volume={16}, number={3}, year="2022", month={March}, pages={1076-1094}}