Detecting stress in computer users, while technically challenging, is of the utmost importance in the workplace, especially now that remote working scenarios are becoming ubiquitous. In this context, cost-effective, subject-independent systems are needed that can be embedded in consumer devices and classify users’ stress in a reliable and unobtrusive fashion. Leveraging keyboard and mouse dynamics is particularly appealing in this context as it exploits readily available sensors. However, available studies are mostly performed in laboratory conditions, and there is a lack of on-field investigations in closer-to-real-world settings. In this study, keyboard and mouse data from 62 volunteers were experimentally collected in-the-wild using a purpose-built Web application, designed to induce stress by asking each subject to perform 8 computer tasks under different stressful conditions. The application of Multiple Instance Learning (MIL) to Random Forest (RF) classification allowed the devised system to successfully distinguish 3 stress-level classes from keyboard (76% accuracy) and mouse (63% accuracy) data. Classifiers were further evaluated via confusion matrix, precision, recall, and F1-score.

中文翻译：

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通过键盘和鼠标动力学在计算机用户中进行压力检测

在技​​术上具有挑战性的同时，检测计算机用户的压力在工作场所中至关重要，尤其是在远程工作场景变得无处不在的情况下。在这种情况下，需要经济高效，独立于主题的系统，该系统可以嵌入到消费设备中，并以可靠且不干扰用户的方式对用户的压力进行分类。在这种情况下，利用键盘和鼠标的动态特性特别吸引人，因为它利用了现成的传感器。然而，可用的研究大多在实验室条件下进行，并且缺乏在接近真实世界的环境中进行现场调查的能力。在这项研究中，使用专用Web应用程序通过野外实验收集了来自62位志愿者的键盘和鼠标数据，通过要求每个受试者在不同的压力条件下执行8项计算机任务来诱发压力。将多实例学习（MIL）应用于随机森林（RF）分类，使设计的系统能够成功区分键盘（76％的准确度）和鼠标（63％的准确度）数据中的3个压力等级类别。通过混淆矩阵，精度，召回率和F1分数进一步评估了分类器。