Authors:
Nuno Micael Ferreira
1
;
2
;
José M. Torres
2
;
3
;
Pedro Sobral
2
;
3
;
Rui Moreira
2
;
3
and
Christophe Soares
2
;
3
Affiliations:
1
AppGeneration Software Technologies Lda, Porto, Portugal
;
2
ISUS Unit, FCT - University Fernando Pessoa, Porto, Portugal
;
3
LIACC, University of Porto, Porto, Portugal
Keyword(s):
Deep Learning, Edge AI, Activity Detection, Table Tennis Sports, Wearable and Mobile AI Apps.
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).