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    科学领域:

    • 计算机科学 计算机科学
    • 生物医学工程 生物医学工程
    • 心理学 心理学 心理学

    背景情况:

    • 慢性压力显著影响健康,并可能导致生理和心理障碍.
    • 目前的自动压力检测方法通常依赖于侵入性的生理传感器,限制了实际的日常使用.
    • 现有的基于传感器的方法面临准确性限制和用户不适.

    研究的目的:

    • 开发一种新的,非侵入性的自动压力检测方法.
    • 调查使用眼睛数据 (视线方向和地标) 进行压力检测的有效性.
    • 为了比较长期短期存储器 (LSTM) 和门式循环单元 (GRU) 网络的性能,用于此任务.

    主要方法:

    • 利用眼睛的凝视方向和眼睛的地标被标准视频摄像机捕获.
    • 采用了两种类型的循环神经网络 (RNN):长期短期记忆 (LSTM) 和门式循环单元 (GRU).
    • 在UBFC-Phys数据集上进行实验,以评估拟议的方法.

    主要成果:

    • 仅使用凝视数据,压力检测准确率达到83.69%.
    • 整合眼界地标提高了准确度,达到90.12%.
    • 将目光和地标数据结合起来,达到90.83%的最大准确率.

    结论:

    • 眼睛数据,特别是视线方向和地标,为准确,非侵入性的压力检测提供了有效的基础.
    • 循环神经网络 (LSTM和GRU) 适用于分析时间眼数据以检测压力.
    • 拟议的基于摄像头的方法为传统的生理传感方法提供了实用和准确的替代方案.