通过先进的机器学习和可穿戴技术,分析印度学生的心理压力
Shruti Gedam1, Sandip Dutta2, Ritesh Jha2
1Department of Computer Science and Engineering, Birla Institute of Technology (BIT), Mesra, Ranchi, Jharkhand, India. shrutgedam@gmail.com.
这项研究开发了一种低成本的可穿戴设备和机器学习 (ML) 模型,用于准确检测心理压力. 这种新型系统实现了96.17%的准确性,为实时压力监测提供了一个可访问的工具.
科学领域:
- 生物医学工程 生物医学工程
- 计算神经科学是一种神经科学.
- 机器学习 机器学习
背景情况:
- 心理压力是一个广泛的社会问题,需要准确的检测方法.
- 可穿戴生理数据为非侵入性压力监测提供了一个有希望的途径.
- 现有的方法往往缺乏广泛采用所需的可访问性,成本效益或实时能力.
研究的目的:
- 为了比较机器学习 (ML) 算法用于使用可穿戴生理信号检测心理压力.
- 提出和验证一种新的,自动的,高性能,低成本的ML模型来检测心理压力.
- 在基准数据集和实时试点研究中评估开发系统的性能.
主要方法:
- 采集了200名参与者在四种压力下使用一种基于Arduino的可穿戴设备收集的生理数据 (ECG,GSR,ST).
- 研究了九个ML算法,使用多变量和单变量特征进行应力分类.
- 在SWELL-KW和WESAD基准数据集上验证了拟议的模型,并进行了实时试点测试.
主要成果:
- 拟议的模型在检测心理压力方面达到96.17%的准确性,XGBoost在多变量分析中表现出卓越的性能.
- XGBoost在单变量分析中表现出一致的准确性,突出了其在应力检测方面的可靠性.
- 基准数据集验证产生了高准确率 (92.38%的SWELL-KW,WESAD的94.21%),实时试点测试实现了97.5%的准确率与低延迟.
结论:
- 开发的低成本可穿戴设备和ML模型为自动心理压力检测提供了有效和可访问的解决方案.
- XGBoost算法是使用生理信号进行心理压力分类的可靠选择.
- 这项研究为推进心理压力检测技术提供了宝贵的见解和可靠的资源.
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