设计和开发一个非接触式基于心电图的人类情绪识别系统,使用SVM和射频分类器
Aftab Alam1, Shabana Urooj2, Abdul Quaiyum Ansari1
1Department of Electrical Engineering, Jamia Millia Islamia, Delhi 110025, India.
Diagnostics (Basel, Switzerland)
|June 28, 2023
概括
这项研究开发了一种机器学习系统,使用心电图 (ECG) 信号来识别人类的情绪. 该系统实现了高精度,随机森林模型达到98.2%,证明了情感意识的人机交互的潜力.
科学领域:
- 人与计算机的交互
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 情绪识别对于推进人机交互 (HMI) 至关重要.
- 情绪智能系统可以增强日常生活,并有助于心理状态评估.
- 早期发现情绪状态可能有助于预防抑郁症等心理健康问题.
研究的目的:
- 提出使用心电图 (ECG) 信号的单模式情绪分类器.
- 评估机器学习模型在从心电图数据中分类人类情绪方面的表现.
- 评估使用非接触式心电图用于情绪识别的可行性.
主要方法:
- 使用基于电容传感器的心电图带获取非接触式心电图信号.
- 开发和训练了支持矢量机 (SVM) 和随机森林 (RF) 分类器.
- 利用了来自45个受试者在三个年龄组的ECG数据的10倍交叉验证.
主要成果:
- 随机森林 (RF) 分类器实现了98.2%的最低准确率.
- 支持矢量机 (SVM) 分类器达到86.6%的最低准确度.
- 这两种模型都展示了来自心电图信号的有效情绪分类能力.
结论:
- 机器学习模型可以有效地使用非接触式心电图信号对人类情绪进行分类.
- 拟议的系统显示了开发先进的情感意识HMI的前景.
- 高分类准确度表明,在心理状态监测中,可能存在现实应用.
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