A comparative study of machine learning and deep learning algorithms for padel tennis shot classification

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.

MultiSenseBadminton: Wearable Sensor–Based Biomechanical Dataset for Evaluation of Badminton Performance

“…To avoid altering the racket's weight or feel with attached sensors, we chose motion tracking technology worn on the hand (Perception Neuron studio) to measure racket movements. This decision was informed by studies in racket sports [69][70][71][72][73] , where IMU sensors on the hand provided stroke classification performance comparable to sensors on the racket 74 , and in some cases, even superior correlation with player performance-related measures 72 . This approach allows us to gather essential swing information via IMU sensor-based data from the hand, maintaining the racket's natural feel and balance during play and offering proxy measures for racket dynamics.…”

A Comparative Study of Feature Selection Technique for Predicting the Professional Tennis Matches Outcome in a Grand Slam Tournament

“…Examples of the application of DM are education [11], [12], insurance and healthcare [13], [14], [15], finance and banking sector [16], [17], social media analytics [18], [19] and others. Various DM techniques have been applied in predicting tennis match outcomes [20], [21], [22], [23], [24], [25]. Among these DM techniques, classification is often used as the outcome in the form of a binary response.…”

“…When the label/class variables are discrete or categorical, the classification technique is identified as the most popular and effective DM method for classifying data in the prediction model [28]. Tennis match outcomes can be predicted using a variety of methods under the classification technique, including Logistic Regression (LR), Naïve Bayes (NB), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest (RF) [22], [24], [29], [30]. Work by [23] applied three various techniques such as LR, RF, and SVM, in determining serves performance using different indicators such as Percentage of Ace over Double Faults, Previous Percentage of games owned, Number of championships, Percentage of games won in the same tournament, Games won before in the same round and many more.…”