Flow From Motion: A Deep Learning Approach

TL;DR

This paper presents a deep learning method to detect the flow state in tennis players using wearable device data, achieving high accuracy and offering new insights for athlete training and human-computer interaction.

Contribution

It introduces a novel approach to identify flow states in athletes through wearable data and deep neural networks, which was not previously demonstrated.

Findings

Deep neural networks achieved around 98% accuracy in detecting flow.

Flow state detection using wearables is feasible in tennis.

Potential applications in designing new training hardware and interfaces.

Abstract

Wearable devices have the potential to enhance sports performance, yet they are not fulfilling this promise. Our previous studies with 6 professional tennis coaches and 20 players indicate that this could be due the lack of psychological or mental state feedback, which the coaches claim to provide. Towards this end, we propose to detect the flow state, mental state of optimal performance, using wearables data to be later used in training. We performed a study with a professional tennis coach and two players. The coach provided labels about the players' flow state while each player had a wearable device on their racket holding wrist. We trained multiple models using the wearables data and the coach labels. Our deep neural network models achieved around 98% testing accuracy for a variety of conditions. This suggests that the flow state or what coaches recognize as flow, can be detected using wearables data in tennis which is a novel result. The implication for the HCI community is that having access to such information would allow for design of novel hardware and interaction paradigms that would be helpful in professional athlete training.