自动化情绪识别系统使用血量脉冲和XGBoost学习系统
Lokesh Naidu Lebaka1, 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
这项研究引入了一种新的机器学习方法,用于使用血液体积脉冲 (BVP) 信号来检测情绪. 开发的模型实现了71.88%的准确性,突出了BVP.
科学领域:
- 物理计算的物理计算.
- 情感计算是一种情感计算.
- 生物医学信号处理
背景情况:
- 情绪检测对于心理健康监测和人机交互至关重要.
- 血量脉冲 (BVP) 信号提供了反映自主神经系统活动的非侵入性生理测量.
- 现有的情绪识别方法通常依赖于复杂的多式联络数据或缺乏实时适用性.
研究的目的:
- 开发和评估用于使用血量脉冲 (BVP) 信号检测情绪的机器学习模型.
- 从BVP信号中识别出最能表明不同情绪状态的关键特征.
- 探索使用可穿戴传感器数据用于医疗保健中的情感识别的可行性.
主要方法:
- 利用公开可用的CASE数据集,包括30名受试者的BVP信号.
- 预处理了BVP信号并提取了39个跨时间,频率和时间频率域的特征.
- 使用XGBoost算法开发了一种情绪检测模型,优化了前10个最重要的特征.
主要成果:
- 在情绪检测方面,XGBoost模型实现了71.88%的最大分类准确度.
- 最具影响力的特征来自时间 (5),时间频率 (4) 和频率 (1) 领域.
- 从BVP的时间频率表示的性被确定为分类中最重要的单一特征.
结论:
- 血量脉冲 (BVP) 信号,特别是像斜率这样的特征,对于准确的情绪检测具有显著的潜力.
- 机器学习模型,如XGBoost,可以有效地利用BVP功能进行情绪分类.
- 通过可穿戴设备记录的BVP信号为在医疗保健环境中开发实用的情绪检测系统提供了一个有希望的途径.
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