Artificial Neural Network-based Emotion Classification Using Physiological Signal

被引：0

作者：

Lin, Shu-Yen ^{[1
]}

Liao, Guan-Hao ^{[1
]}

机构：

[1] Yuan Ze Univ, Dept Elect Engn, Jhongli 32003, Taiwan

来源：

2020 6TH INTERNATIONAL CONFERENCE ON APPLIED SYSTEM INNOVATION (ICASI) | 2020年

关键词：

PPG; SVM; Neural Network; Emotion Classification; PHOTOPLETHYSMOGRAPHY;

D O I：

10.1109/ICASI49664.2020.9426328

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this work, we propose an artificial neural network based emotion classification (ANN-EC) with two opposite emotions, happiness and sadness. Compared with the support vector machine based emotion classification (SVM-EC), ANN-EC performs more flexible in classification. Also, ANN-EC shows higher accuracy than SVM-EC. ANN-EC with multiple features reaches 96.25% accuracy and it performs better than just using asingle feature.

引用

页码：27 / 29

页数：3

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