识别面部EMG的最佳位置用于使用后勤回归检测情绪
Vinay Kumar Barigala1, Sriram Kumar P1, Praveen Kumar Govarthan1
1School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India.
Studies in health technology and informatics
|June 30, 2023
概括
来自面部肌肉的电肌图 (EMG) 信号,特别是额头肌大 (zEMG) 和波纹超 (cEMG),显示出情绪检测的希望. 结合这些信号,在我们的研究中提高了情绪分类的准确性.
科学领域:
- 生物医学工程 生物医学工程
- 情感计算是一种情感计算.
- 信号处理 信号处理
背景情况:
- 电肌图 (EMG) 信号提供了肌肉活动的生理测量.
- 面部肌肉EMG信号越来越多地被用于情绪识别应用.
- 之前的研究已经调查了各种EMG网站用于情绪检测,取得了不同程度的成功.
研究的目的:
- 为了评估电肌图 (EMG) 信号的实用性,从腹肌大 (zEMG),梯形 (tEMG) 和波纹超 (cEMG) 来进行情绪检测.
- 使用EMG衍生功能的不同机器学习分类器的性能进行比较.
- 确定EMG信号源的最佳组合,以提高情绪分类的准确性.
主要方法:
- 在暴露于不同情绪刺激期间,从zEMG,tEMG和cEMG记录了EMG信号.
- 从记录的EMG信号中提取了11个时间域特征.
- 使用逻辑回归 (LR),支向量机 (SVM) 和多层感知子 (MLP) 模型来对特征进行分类.
- 用10倍的交叉验证准确度评估模型性能.
主要成果:
- 个别的EMG站点产生了中等的分类准确度,LR使用tEMG达到67.92%的准确度.
- 将zEMG和cEMG功能与LR相结合,精度显著提高到70.6%.
- 将tEMG功能与zEMG和cEMG一起使用导致性能下降.
结论:
- 结合肌肉主要 (zEMG) 和波纹超 (cEMG) 信号对于有效的情绪识别至关重要.
- 面部EMG信号,特别是来自zEMG和cEMG的EMG,为情绪检测系统提供了有价值的信息.
- 进一步的研究应侧重于优化特征提取和模型选择,以实现基于EMG的多站点情绪识别.
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