Invisible experience to real-time assessment in elite tennis athlete training: Sport-specific movement classification based on wearable MEMS sensor data
This study examined the reliability of a tennis stroke classification and assessment platform consisting of a single low-cost MEMS sensor in a wrist-worn wearable device, smartphone, and computer. The data that was collected was transmitted via Bluetooth and analyzed by machine learning algorithms. Twelve right-handed male elite tennis athletes participated in the study, and each athlete performed 150 strokes. The results from three machine learning algorithms regarding their recognition and classification of the real-time data stream were compared. Stroke recognition and classification went through pre-processing, segmentation, feature extraction, and classification with Support Vector Machine (SVM), including SVM without normalization, SVM with Min-Max, SVM with Z-score normalization, K-nearest neighbor (K-NN), and Naive Bayes (NB) machine learning algorithms. During the data training process, 10-fold cross-validation was used to avoid overfitting and suitable parameters were found within the SVM classifiers. The best classifier was achieved when C = 1 using the RBF kernel function. Different machine learning algorithms` classification of unique stroke types yielded highly reliable clusters within each stroke type with the highest test accuracy of 99% achieved by SVM with Min-Max normalization and 98.4% achieved using SVM with a Z-score normalization classifier.
© Copyright 2023 Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology. SAGE Publications. Kaikki oikeudet pidätetään.
Aiheet: | tennis reaaliaikainen käsittely harjoittelu liike järjestys anturi teknologia |
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Aihealueet: | urheilukilpailut tekniset ja luonnontieteet |
Tagging: | maschinelles Lernen |
DOI: | 10.1177/17543371211050312 |
Julkaisussa: | Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology |
Julkaistu: |
2023
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Vuosikerta: | 237 |
Numero: | 4 |
Sivuja: | 271-282 |
Julkaisutyypit: | artikkeli |
Kieli: | englanti (kieli) |
Taso: | kehittynyt |