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Measurement and prediction of work engagement under different indoor lighting conditions using physiological sensing

机译：使用生理感测的不同室内照明条件下工作啮合的测量与预测

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Employee productivity is of paramount importance to most organizations. Studies have shown that a suitable indoor lighting condition is key to help employees remain productive and comfortable in their office spaces. However, it is very difficult to monitor and quantify productivity, which limits our ability to select indoor conditions that maximize the performance of the building occupants. Instead, work engagement is a measurable parameter that is directly related to productivity. Therefore, this paper proposes a method to investigate the effect of lighting level on occupants' work engagement by studying the frontal asymmetry index (FAI) measured by electroencephalography (EEG). Statistical analysis is performed to investigate the work engagement of the occupants under three typical lighting levels (i.e., 200 lux, 500 lux, and 1000 lux) while they are performing cognitive tasks. The results show that the effect of lighting level on work engagement varies across individuals, which highlights the necessity of developing personalized models. Therefore, this study also proposes a method to predict engagement level for different individuals based on the lighting level and their galvanic skin response (GSR), heart rate (HR), and skin temperature (ST) using the Random Forest (RF) and Artificial Neural Network (ANN). The results show that RF outperforms ANN in most of the prediction cases, and the final classification accuracies are 83.3% for the 3-scale case and 62.2% for the 5-scale case. This opens the possibility of using easily measurable physiological parameters to estimate human brain activities and predict their work engagement under different lighting scenarios.
机译：员工生产力对大多数组织至关重要。研究表明，适用的室内照明条件是帮助员工在办公空间中保持富有成效和舒适的关键。然而，监控和量化生产率非常困难，这限制了我们选择最大化建筑物性能的室内条件的能力。相反，工作参与是一个可衡量的参数，与生产力直接相关。因此，本文提出了一种通过研究通过脑电图（EEG）测量的额相不对称指数（FAI）来研究照明水平对乘员工作参与的影响。进行统计分析，以研究占用者在三个典型的照明水平下的工作啮合（即，200勒克斯，500勒克斯和1000勒克斯），同时进行认知任务。结果表明，照明水平对工作啮合的影响各不相同，这突出了开发个性化模型的必要性。因此，本研究还提出了一种使用随机森林（RF）和人工的照明水平及其电流皮肤响应（GSR），心率（HR），心率（HR），心率（HR），心率（HR）和皮肤温度（ST）来预测不同个体的接合水平的方法神经网络（ANN）。结果表明，在大多数预测案例中，RF优于大多数预测案例，3尺度案例的最终分类准确性为83.3％，5尺度案例为62.2％。这使得可以使用易于可测量的生理参数来估算人的大脑活动并在不同的照明场景下预测其工作参与。

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