Authors:
Paulo Barbosa
1
;
Pedro Cunha
1
;
2
;
Vítor Carvalho
1
;
2
and
Filomena Soares
2
Affiliations:
1
12Ai - School of Technology, IPCA, Barcelos, Portugal
;
2
Algoritmi Research Centre, University of Minho, Guimarães, Portugal
Keyword(s):
Deep Learning, Human Action Recognition, Neural Networks, Computer Vision, Taekwondo.
Abstract:
Research in motion analysis area has enabled the development of affordable and easy to access technological solutions. The study presented aims to identify and quantify the movements performed by a taekwondo athlete during training sessions using deep learning techniques applied to the data collected in real time. For this purpose, several approaches and methodologies were tested along with a dataset previously developed in order to define which one presents the best results. Considering the specificities of the movements, usually fast and mostly with a high incidence on the legs, it was concluded that the best results were obtained with convolution layers models, such as, Convolutional Neural Networks (CNN) plus Long Short-Term Memory (LSTM) and Convolutional Long Short-Term Memory (ConvLSTM) deep learning models, with more than 90% in terms of accuracy validation.