Automatic Recognition of Flow During Work Activities Using Context and Physiological Signals

Flow is a positive affective state occurring when individuals are fully immersed into an activity. Being in flow during work activities can lead to higher performance and productivity. Despite the importance of flow at work, few approaches have been proposed for its automatic recognition using sensor data and most existing studies are conducted in laboratory settings with simulated work activities. In this paper, we investigate the use of physiological data, collected using wrist-worn devices, combined with context information, obtained through self-reports, to automatically distinguish between low and high levels of flow. We investigate the role of the context for flow perceptions and in its automatic recognition. Further, we compare the performance of several sensor fusion strategies based on shallow and deep learning. To evaluate our approach we use a data set of 390 activities collected during actual work days. Our results show that using raw blood volume pulse, electrodermal activity and the type of activity as input to a sensor-based late fusion approach, implemented using convolutional neural networks, allows to reach a balanced accuracy of 70.93%.