WEBA dataset as the Reflection of Work content effect on Workload perception in Real life Working conditions.

The effect of work content on workload, stress, and performance was not well addressed in the literature, due to the lack of comprehensive conceptualization, problem definition, and relevant dataset. The gap between laboratory-simulated studies and real-life working conditions delays the generalization, hindering the development of performance management and monitoring tools. Contributing to this topic, a data collection effort is organized, which considers unique work conditions and work content factors of a coffee shop, to conceptualize scenarios that better highlight their effect on human performance, thus creating the Work content Effect on BAristas (WEBA) dataset. Utilizing sensor technologies to recognize the ongoing activities, physical work activities and heart rates of five baristas in 55 shifts with different work content combinations during real-life working processes were recorded, while the integration of subjective and objective measures of workload and emotions were deployed as perceived workload indicators. Heart rate signals during normal conditions without working were measured as the baseline. This dataset is unique in its conceptualization and useful for scrutinizing more nuances of the effect of work content on performance and the well-being of employees, as well as facilitating better human factor engineering, workplace and work task design.