从VR模拟中使用决策树方法进行生理信号分析和压力分类
Syem Ishaque1, Naimul Khan1, Sridhar Krishnan1
1Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada.
Bioengineering (Basel, Switzerland)
|July 29, 2023
概括
这项研究使用生理数据和机器学习来对虚拟现实游戏中的压力水平进行分类. 先进的模型在二元压力分类中实现了100%的准确性,证明了有效的压力管理潜力.
科学领域:
- 生理监测是指对身体进行生理监测.
- 机器学习用于健康.
- 虚拟现实应用程序 虚拟现实应用程序
背景情况:
- 长时间的压力会对心血管和免疫系统产生负面影响.
- 评估压力影响需要监测交感神经系统 (SNS) 和副交感神经系统 (PNS).
- 通过心电图 (ECG),电磁皮肤反应 (GSR) 和呼吸 (RESP) 传感器获取生理数据至关重要.
研究的目的:
- 为了分析虚拟现实 (VR) 气泡花游戏期间的生理应激反应.
- 开发和评估用于准确压力分类的机器学习模型.
- 调查新功能在压力检测方面的有效性.
主要方法:
- 收集了15名受试者在各种任务 (包括VR游戏) 期间的生理数据 (ECG,GSR,RESP).
- 提取了21个特征,并从其他11个特征中开发了一个新的K-means特征.
- 使用了个性化的分类和回归树 (CART),决策树 (DT),集成梯度增强 (EGB) 和极端梯度增强 (XGBoost) 模型.
主要成果:
- 心率 (HR),GSR和RESP下降,而高频率 (HF) 在VR游戏后增加.
- 个性化CART实现了87.75%的准确度,用于二元应力分类.
- 对于二元应力分类,EGB模型达到了100%的准确性,超过了现有的研究.
- XGBoost和DT模型使用新的K-means功能以72.22%的准确性对五个应激类进行了分类.
结论:
- 提出的方法,特别是EGB,在分类压力水平方面表现出高效.
- 新的K-means功能提高了模型性能,并减少了应力分类中的错误.
- 虚拟现实游戏与先进的机器学习相结合,为压力管理和评估提供了一个有希望的方法.
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