Using smart offices to predict occupational stress
Using smart offices to predict occupational stress
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DOI：
10.1016/j.ergon.2018.04.005
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发表时间：
2018-09-01
影响因子：
3.1
通讯作者：
Cook, Diane J.
中科院分区：
文献类型：
--
作者：
Alberd, Ane;Aztiria, Asier;Cook, Diane J.
职业压力越来越多地出现在我们的社会中。通常，发现得太晚，导致工人的身心健康问题，以及公司的经济损失，因为随之而来的缺勤、出勤、积极性降低或工作人员更替。因此，需要开发早期压力检测系统，使个人能够及时采取行动，防止不可逆转的损害。为了满足这一需求，我们研究了一种方法来分析生理和行为模式的变化，使用不显眼和无处不在收集的智能办公室数据。本文的目标是建立模型，预测自我评估的压力和心理工作量的分数，以及模型，预测工作量条件的基础上，生理和行为数据。回归模型的预测自我报告的压力和心理工作量的分数从数据的基础上真实的办公室工作设置。同样，分类模型被用来检测工作量条件和这些条件的变化。还测试了处理类别不平衡的特定算法（SMOTEBoost和RUSBoost）。结果证实了压力和精神负荷水平的行为变化的可预测性，以及工作量条件的变化。结果还表明，计算机使用模式与身体姿势和动作是最好的预测为此目的。此外，自我报告的评分标准化的重要性和美国宇航局的任务负荷指数测试的工作量评估的适用性。这项工作大大有助于在智能办公环境中开发一个不显眼和无处不在的早期压力检测系统，其在工业环境中的实施将对工人的健康状况和公司的经济产生巨大的有益影响。
Occupational stress is increasingly present in our society. Usually, it is detected too late, resulting in physical and mental health problems for the worker, as well as economic losses for the companies due to the consequent absenteeism, presenteeism, reduced motivation or staff turnover. Therefore, the development of early stress detection systems that allow individuals to take timely action and prevent irreversible damage is required. To address this need, we investigate a method to analyze changes in physiological and behavioral patterns using unobtrusively and ubiquitously gathered smart office data. The goal of this paper is to build models that predict self-assessed stress and mental workload scores, as well as models that predict workload conditions based on physiological and behavior data. Regression models were built for the prediction of the self-reported stress and mental workload scores from data based on real office work settings. Similarly, classification models were employed to detect workload conditions and change in these conditions. Specific algorithms to deal with class-imbalance (SMOTEBoost and RUSBoost) were also tested. Results confirm the predictability of behavioral changes for stress and mental workload levels, as well as for change in workload conditions. Results also suggest that computer-use patterns together with body posture and movements are the best predictors for this purpose. Moreover, the importance of self-reported scores' standardization and the suitability of the NASA Task Load Index test for workload assessment is noticed. This work contributes significantly towards the development of an unobtrusive and ubiquitous early stress detection system in smart office environments, whose implementation in the industrial environment would make a great beneficial impact on workers' health status and on the economy of companies.