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

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.

Automated summary

This study 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). The results show that the effect of lighting level on work engagement varies across individuals, which highlights the necessity of developing personalized models. The study also proposes a method to predict engagement level for different individuals based on physiological parameters using Random Forest (RF) and Artificial Neural Network (ANN), showing that RF outperforms ANN in most prediction cases.