Flow is a precious mental status for achieving high sports performance, defined as an emotion with high valence and high arousal levels. However, a viable detection system that could provide information in real time is not yet recognized. The work presented here aims to create an online flow detection framework. A supervised machine learning model will be trained to predict valence and arousal levels on existing databases and freshly collected physiological data. As a final result, the definition of the minimally expensive (both in terms of sensors and time) amount of data needed to predict a flow status will enable the creation of a real-time detection interface of flow.