Knowledge workers face many challenges in the workplace: work is fragmented, disruptions are constant, tasks are complex, and work hours can be long. These challenges can affect knowledge workers’ stress, focus and awakeness, and in turn their interaction with the digital environment, the quality of work performed and their productivity in general. We report on a field study with 14 knowledge workers over an eight-week period in which we investigated, using experience sampling, how the workers experience stress and awakeness over time. During this field study, we also collected biometric data including heart- and skin-related measures, which we then used to investigate if it is possible to predict stress, focus and awakeness, in the moment. We observed and report on various trends in knowledge worker stress and awakeness levels over several weeks, finding that people tend to have certain “baseline” levels for these aspects. Moreover, we found that days with high levels of stress tend to cluster, similarly as the days with low awakeness. We further show that machine learning models can be built from the data of a single minimally invasive device to predict stress, focus, and awakeness. Overall, we found that our models were capable of large improvements in precision and recall in comparison to a random classifier for stress (25.9% increase over random for precision, 4.2% for recall) and awakeness (52.4% increase in precision, 40.8% in recall). The abstract concept of focus proved to be the hardest to predict (26.0% increase in precision, 27.8% decrease in recall).

知识工作者在工作场所面临许多挑战：工作分散，破坏不断，任务复杂且工作时间长。这些挑战会影响知识型员工的压力，专注力和清醒度，进而影响他们与数字环境的互动，所执行工作的质量以及整体的生产率。我们报告了在八周的时间内对14名知识工人进行的实地研究，其中我们使用经验抽样调查了这些工人随着时间的推移如何承受压力和觉醒。在这项现场研究期间，我们还收集了包括心脏和皮肤相关指标在内的生物统计数据，然后将其用于调查目前是否可以预测压力，注意力和清醒程度。我们观察并报告了数周以来知识工作者压力和清醒程度的各种趋势，发现人们在这些方面往往具有一定的“基准”水平。此外，我们发现，与低清醒的日子类似，压力高的日子容易聚集。我们进一步表明，可以从单个微创设备的数据构建机器学习模型，以预测压力，注意力和清醒度。总体而言，我们发现，与针对压力的随机分类器（比随机分类器提高25.9％，对召回率提高4.2％）和清醒度（精度提高52.4％，对重复率提高40.8％）相比，我们的模型能够在精度和召回率方面进行较大的改进。召回）。事实证明，焦点的抽象概念最难预测（精度提高26.0％，召回率降低27.8％）。我们发现，与处于低清醒状态的日子类似，处于高压力水平的日子容易聚集。我们进一步表明，可以从单个微创设备的数据构建机器学习模型，以预测压力，注意力和清醒度。总体而言，我们发现，与针对压力的随机分类器（比随机分类器提高25.9％，对召回率提高4.2％）和清醒度（精度提高52.4％，对重复率提高40.8％）相比，我们的模型能够在精度和召回率方面进行较大的改进。召回）。事实证明，焦点的抽象概念最难以预测（精度提高26.0％，召回率降低27.8％）。我们发现，与低清醒的日子类似，压力高的日子容易聚集。我们进一步表明，可以从单个微创设备的数据构建机器学习模型，以预测压力，注意力和清醒度。总体而言，我们发现，与针对压力的随机分类器（比随机分类器提高25.9％，对召回率提高4.2％）和清醒度（精度提高52.4％，对重复率提高40.8％）相比，我们的模型能够在精度和召回率方面进行较大的改进。召回）。事实证明，焦点的抽象概念最难以预测（精度提高26.0％，召回率降低27.8％）。总体而言，我们发现，与针对压力的随机分类器（比随机分类器提高25.9％，对召回率提高4.2％）和清醒度（精度提高52.4％，对重复率提高40.8％）相比，我们的模型能够在精度和召回率方面进行较大的改进。召回）。事实证明，焦点的抽象概念最难预测（精度提高26.0％，召回率降低27.8％）。总体而言，我们发现，与针对压力的随机分类器（比随机分类器提高25.9％，对召回率提高4.2％）和清醒度（精度提高52.4％，对重复率提高40.8％）相比，我们的模型能够在精度和召回率方面进行较大的改进。召回）。事实证明，焦点的抽象概念最难以预测（精度提高26.0％，召回率降低27.8％）。