Detecting Work Stress in Offices by Combining Unobtrusive Sensors
Detecting Work Stress in Offices by Combining Unobtrusive Sensors
复制标题
DOI：
10.1109/taffc.2016.2610975
复制
发表时间：
2018-04-01
影响因子：
11.2
通讯作者：
Kraaij, Wessel
中科院分区：
文献类型：
--
作者：
Koldijk, Saskia;Neerincx, Mark A.;Kraaij, Wessel
员工经常报告工作压力的经历。在SWELL项目中，我们研究了新的上下文感知普适系统如何支持知识工作者减轻压力。本文的重点是开发自动分类器来推断工作条件和压力相关的心理状态，从一组多模态的传感器数据（计算机记录，面部表情，姿势和生理）。我们解决了两个方法和应用机器学习的挑战：1）使用几个（物理上）不显眼的传感器检测工作压力，2）考虑个体差异。几种分类方法的比较表明，对于我们的SWELL-KW数据集，通过SVM可以以90%的准确率区分中性和压力工作条件。面部表情产生的信息最有价值，其次是面部表情。此外，我们发现，主观变量“脑力劳动”可以更好地从传感器数据预测，例如，“感知压力”。几种回归方法的比较表明，决策树可以最好地预测脑力劳动（相关系数为0.82）。面部表情产生最有价值的信息，其次是姿势。我们发现，特别是在估计心理状态时，解决个体差异是有意义的。当我们在相似用户的特定子组上训练模型时，（几乎在所有情况下）专用模型的性能与通用模型一样好或更好。
Employees often report the experience of stress at work. In the SWELL project we investigate how new context aware pervasive systems can support knowledge workers to diminish stress. The focus of this paper is on developing automatic classifiers to infer working conditions and stress related mental states from a multimodal set of sensor data (computer logging, facial expressions, posture and physiology). We address two methodological and applied machine learning challenges: 1) Detecting work stress using several (physically) unobtrusive sensors, and 2) Taking into account individual differences. A comparison of several classification approaches showed that, for our SWELL-KW dataset, neutral and stressful working conditions can be distinguished with 90 percent accuracy by means of SVM. Posture yields most valuable information, followed by facial expressions. Furthermore, we found that the subjective variable mental effort' can be better predicted from sensor data than, e.g., 'perceived stress'. A comparison of several regression approaches showed that mental effort can be predicted best by a decision tree (correlation of 0.82). Facial expressions yield most valuable information, followed by posture. We find that especially for estimating mental states it makes sense to address individual differences. When we train models on particular subgroups of similar users, (in almost all cases) a specialized model performs equally well or better than a generic model.