自动化情绪识别使用基于福里埃-贝塞尔域的度
Aditya Nalwaya1, Kritiprasanna Das1, Ram Bilas Pachori1
1Department of Electrical Engineering, Indian Institute of Technology Indore, Indore 453552, India.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
这项研究从生理信号中引入了新的特性,用于情绪识别. 这些新功能在检测兴奋,价值和支配性方面实现了高精度,增强了人与计算机的互动.
科学领域:
- 物理计算的物理计算.
- 情感计算是一种情感计算.
- 机器学习用于情绪识别和识别.
背景情况:
- 越来越多的人与计算机的互动需要动态和上下文系统.
- 情绪识别需要通过生理信号了解用户的情绪状态.
- 电心电图 (ECG) 和电脑电图 (EEG) 是情绪检测的关键生理信号.
研究的目的:
- 提出新的基于的特征在富里埃-贝塞尔域的情感识别.
- 使用福里埃-贝塞尔序列扩展 (FBSE) 来表示非静止的生理信号.
- 使用这些功能开发和评估用于使用情绪检测的机器学习模型.
主要方法:
- 将心电图和脑电图信号分解为窄带模式,使用基于FBSE的实证波纹转换 (FBSE-EWT).
- 计算每个模式的拟议特征,以创建一个特征向量.
- 开发机器学习模型,特别是K-最近邻居 (KNN),用于分类.
主要成果:
- 拟议的算法实现了高准确度:97.84%的兴奋,97.91%的价值,97.86%的统治.
- 富里埃-贝塞尔领域的新特征在传统方法相比显示出更高的性能.
- FBSE在表现ECG和EEG等非静态生理信号方面被证明是有效的.
结论:
- 开发的特性非常适合从生理信号准确识别情绪.
- FBSE-EWT方法为从非静态生物信号中提取特征提供了一个强大的方法.
- 这项研究有助于更具动态性和上下文意识的人与计算机交互系统.
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