对情绪检测的一种不引人注目的方法
1School of Computing Sciences, University of East Anglia, UK.
Studies in health technology and informatics
|February 23, 2026
概括
这项研究开发了一个使用神经网络的网络应用程序,通过日志条目和自拍来实时检测压力. 用户发现压力监测功能有效,更喜欢日志输入.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 人与计算机的交互
背景情况:
- 压力在现代生活中显著影响身体和精神健康.
- 早期发现压力对于有效管理至关重要.
- 技术解决方案为压力检测提供了一个主动的方法.
研究的目的:
- 研究一种基于网络的学习方法,用于不引人注目的压力检测.
- 开发和评估人工神经网络 (ANN) 和卷积神经网络 (CNN) 模型,用于实时压力分类.
- 评估用户对压力检测网络应用程序的感知,利用日志和自拍功能.
主要方法:
- 开发了两个神经网络模型 (ANN和CNN) 用于压力检测.
- 在客户端使用TensorFlow.js.js处理的Web应用程序中实现模型.
- 通过日志条目和自拍收集用户输入,用于实时分类.
- 评估了应用程序,有7名用户评估了感知到的准确性和功能有效性.
主要成果:
- 网络应用程序和压力检测模型按预期运行.
- 用户报告平均主观准确度为3.57分之4的平均分数.
- 100%的用户发现这些功能对监控和跟踪压力有效.
- 用户表示他们更喜欢日志功能,而不是自拍功能.
结论:
- 开发的基于网络的系统证明了实时客户端压力检测的可行方法.
- 用户反表明压力监测工具的积极接待和感知有效性.
- 期刊功能显示,作为压力检测应用程序中用户输入的首选方法,它很有前途.
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