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arXiv · 1803.09689

Flow From Motion: A Deep Learning Approach

Cem Eteke·Hayati Havlucu·Nisa İrem Kırbaç·Mehmet Cengiz Onbaşlı·Aykut Coşkun·Terry Eskenazi·Oğuzhan Özcan·Barış Akgün
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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.

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Cem Eteke, Hayati Havlucu, Nisa İrem Kırbaç, Mehmet Cengiz Onbaşlı, Aykut Coşkun, Terry Eskenazi, Oğuzhan Özcan, Barış Akgün. 2018-03-26. Flow From Motion: A Deep Learning Approach. https://arxiv.org/abs/1803.09689

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