Method and data analysis based on ECG signal for emotion recognition

Jie Chen; Meirong Lin; Zhiguo Zheng; Hongyan Lv

doi:10.1117/12.3009419

16 October 2023 Method and data analysis based on ECG signal for emotion recognition

Jie Chen, Meirong Lin, Zhiguo Zheng, Hongyan Lv

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128033E (2023) https://doi.org/10.1117/12.3009419
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

Emotion recognition is an important link in utilizing the relationship between computers and emotion measurement. The development of medical technology requires people to no longer be limited to physical health, and psychological health is also increasingly valued. Collecting ECG signals and using machine learning for emotion recognition is an important development direction in the field of artificial intelligence. After the original signal is processed and noise filtered, features can be extracted. In this method, the TFIDF algorithm is used. High-dimensional data after Fourier transform could have a higher recognition rate in the SVM classifier. ECG signals as an intrinsic physiological characteristic signal of the human body, could more intuitively express human emotions and serve as a daily monitoring method for emotions, which helps in self-diagnosis and self-analysis of emotions. The average recognition rate of this paper for the five emotions is about 77% with the fact that all data comes from real life scenarios.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jie Chen, Meirong Lin, Zhiguo Zheng, and Hongyan Lv "Method and data analysis based on ECG signal for emotion recognition", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128033E (16 October 2023); https://doi.org/10.1117/12.3009419

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KEYWORDS

Emotion

Electrocardiography

Machine learning

Feature extraction

Detection and tracking algorithms

Education and training

Artificial intelligence

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