Título A comparative study of machine learning and deep learning algorithms for padel tennis shot classification
Publicación externa No
Alcance Article
Naturaleza Científica
Cuartil JCR 2
Cuartil SJR 2
Impacto SJR 0.81
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147921596&doi=10.1007%2fs00500-023-07874-x&partnerID=40&md5=b27325d2b9593ad3637cf71253508913
Fecha de publicacion 01/02/2023
ISI 000929507500001
Scopus Id 2-s2.0-85147921596
DOI 10.1007/s00500-023-07874-x
Abstract Data processing in sports is a phenomenon increasingly present at all levels, from professionals in search of tools to improve their performance to beginners motivated by the quantification of their physical activity. In this work, a comparison between some of the main machine learning and deep learning algorithms is carried out in order to classify padel tennis strokes. Up to 13 representative padel tennis strokes are classified. Before a classification of padel tennis strokes is performed, a sufficiently representative data set is needed that collects numerous examples of the performance of these strokes. Since there was no similar data set in the literature, we proceeded to the creation of such a data set, for which we developed a data collection system based on an electronic device with an inertial measurement unit. Using the developed data set, the machine learning and deep learning algorithms were hyperparameterized to compare their performance under the best possible configurations. The algorithms were fed with both the temporal series of the acceleration and speed of the six degrees of freedom and also with feature engineering input, consisting in calculating the mean, maximum, and minimum values for each axis. The algorithms evaluated are: fully connected or dense neural networks, 1D convolutional neural networks, decision tree, K nearest neighbors, support vector machines, and eigenvalue classification. According to the results achieved, the best algorithm is the 1D convolutional neural network with temporal series input that achieves an accuracy higher than 93%. However, other simpler algorithms such as dense networks and support vector machines achieve similar results. © 2023, The Author(s).
Palabras clave Acceleration; Classification (of information); Convolution; Convolutional neural networks; Data handling; Decision trees; Deep learning; Degrees of freedom (mechanics); Eigenvalues and eigenfunctions; Learning algorithms; Learning systems; Nearest neighbor search; Support vector machines; Comparatives studies; Convolutional neural network; Data set; Deep learning; Machine-learning; Padel tennis; Performance; Physical activity; Shot classification; Support vectors machine; Tennis
Miembros de la Universidad Loyola

Change your preferences Gestionar cookies