Name
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.5220/0010412402010208 | BIODEVICES: PROCEEDINGS OF THE 14TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES - VOL 1: BIODEVICES |
Keywords | DocType | Citations |
Deep Learning, Human Action Recognition, Neural Networks, Computer Vision, Taekwondo | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paulo Barbosa | 1 | 0 | 0.34 |
| Pedro Cunha | 2 | 0 | 0.34 |
| Vítor H. Carvalho | 3 | 0 | 0.34 |
| Filomena O. Soares | 4 | 0 | 0.68 |