• Energy Research

Modern knowledge work is highly intense and demanding, exposing workers to long-term psychosocial stress. In order to address the problem, stress detection technologies have been developed, enabling the continuous assessment of personal stress based on multimodal sensor data. However, stakeholders lack insights into how employees perceive different monitoring technologies and whether they are willing to share stress-indicative data in order to sustain well-being at the individual, team, and organizational levels in the knowledge work context. To fill this research gap, we developed a theoretical model for knowledge workers’ interest in sharing their stress-indicative data collected with unobtrusive sensors and examined it empirically using structural equation modeling (SEM) with a survey of 181 European knowledge workers. The results did not show statistically significant privacy concerns regarding environmental sensors such as air quality, sound level, and motion sensors. On the other hand, concerns about more privacy-sensitive methods such as tracking personal device usage patterns did not prevent user acceptance nor intent to share data. Overall, knowledge workers were highly interested in employing stress monitoring technologies to measure their stress levels and receive information about their personal well-being. The results validate the willingness to accept the unobtrusive, continuous stress detection in the context of knowledge work.

Modern buildings are expected to fulfill energy efficiency regulations while providing a healthy and comfortable living environment for their occupants. Demand-controlled heating, ventilation, and air conditioning can improve energy efficiency and well-being, especially if changes in indoor environmental factors can be forecast reliably enough. As a first step, this study afforded a baseline for CO2 forecasting evaluation by providing for the public use a comprehensive dataset covering a full year. Secondly, we studied the applicability of four machine learning methods, Ridge regression, Decision Tree, Random Forest, and Multilayer Perceptron, for modeling the future concentration of CO2 in indoors. We evaluated their prediction accuracy within different forecasting and history window time frames and the impact of multiple sensor modalities. All models performed better than the baseline method of predicting the last observed value, and the Decision Tree was found to be almost as accurate as the computationally heavier Random Forest model. When the future forecasting window was longer than a minute, the optimal sensor modalities included occupant activity data from passive infrared sensors in addition to CO2 concentration. Our findings suggest that machine learning can be applied in multimodal time-series data to find a simple, accurate, and resource-efficient forecasting model for proactive control of indoor environments.