Classification of Table Tennis Strokes in Wearable Device using Deep Learning

Nuno Ferreira, Nuno Ferreira, José Torres, José Torres, Pedro Sobral, Pedro Sobral, Rui Moreira, Rui Moreira, Christophe Soares, Christophe Soares

2022

Abstract

Analysis of sports performance using mobile and wearable devices is becoming increasingly popular, helping users improve their sports practice. In this context, the goal of this work has been the development of an Apple Watch application, capable of detecting important strokes in the table tennis sport, using a deep learning (DL) model. A dataset of table tennis strokes has been created based on the watch’s accelerometer and gyroscope sensors. The dataset collection was done in the Portuguese table tennis federation training sites, from several athletes, supervised by their coaches. To obtain the best DL model, three different architecture models where trained, compared and evaluated, using the complete dataset: a LSTM based on Create ML/Core ML frameworks (62.70% F1 score) and two Tensorflow based architectures, a CNN-LSTM (96.02% F1 score) and a ConvLSTM (97.33% F1 score).

Paper Citation

in Harvard Style

Ferreira N., Torres J., Sobral P., Moreira R. and Soares C. (2022). Classification of Table Tennis Strokes in Wearable Device using Deep Learning. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 629-636. DOI: 10.5220/0010871100003116

in Bibtex Style

@conference{icaart22,
author={Nuno Ferreira and José Torres and Pedro Sobral and Rui Moreira and Christophe Soares},
title={Classification of Table Tennis Strokes in Wearable Device using Deep Learning},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={629-636},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010871100003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Classification of Table Tennis Strokes in Wearable Device using Deep Learning
SN - 978-989-758-547-0
AU - Ferreira N.
AU - Torres J.
AU - Sobral P.
AU - Moreira R.
AU - Soares C.
PY - 2022
SP - 629
EP - 636
DO - 10.5220/0010871100003116