Flow is a precious mental status for achieving high sports performance. It is defined as an emotional state with high valence and high arousal levels. However, a viable detection system that could provide information about it in real-time is not yet recognized. The prospective work presented here aims to the creation of an online flow detection framework. A supervised machine learning model will be trained to predict valence and arousal levels, both on already existing databases and freshly collected physiological data. As 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.

Follow the Flow: A Prospective on the On-Line Detection of Flow Mental State through Machine Learning

Sajno, Elena;Riva, Giuseppe
2022

Abstract

Flow is a precious mental status for achieving high sports performance. It is defined as an emotional state with high valence and high arousal levels. However, a viable detection system that could provide information about it in real-time is not yet recognized. The prospective work presented here aims to the creation of an online flow detection framework. A supervised machine learning model will be trained to predict valence and arousal levels, both on already existing databases and freshly collected physiological data. As 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.
Inglese
1st IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022
Roma
Paper
26-ott-2022
28-mar-2024
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/268143
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