Tennis Timing Assessment by a Machine Learning-Based Acoustic Detection System: A Pilot Study
<b>Background/Objectives:</b> In tennis, timing plays a crucial factor as it influences the technique and effectiveness of strokes and, therefore, matches results. However, traditional technical evaluation methods rely on subjective observations or video motion-tracking technology, mainl...
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MDPI AG
2025-01-01
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| Series: | Journal of Functional Morphology and Kinesiology |
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| Online Access: | https://www.mdpi.com/2411-5142/10/1/47 |
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| author | Lucio Caprioli Amani Najlaoui Francesca Campoli Aatheethyaa Dhanasekaran Saeid Edriss Cristian Romagnoli Andrea Zanela Elvira Padua Vincenzo Bonaiuto Giuseppe Annino |
| author_facet | Lucio Caprioli Amani Najlaoui Francesca Campoli Aatheethyaa Dhanasekaran Saeid Edriss Cristian Romagnoli Andrea Zanela Elvira Padua Vincenzo Bonaiuto Giuseppe Annino |
| author_sort | Lucio Caprioli |
| collection | DOAJ |
| description | <b>Background/Objectives:</b> In tennis, timing plays a crucial factor as it influences the technique and effectiveness of strokes and, therefore, matches results. However, traditional technical evaluation methods rely on subjective observations or video motion-tracking technology, mainly focusing on spatial components. This study evaluated the reliability of an acoustic detection system in analyzing key temporal elements of the game, such as the rally rhythm and timing of strokes. <b>Methods:</b> Based on a machine learning algorithm, the proposed acoustic detection system classifies the sound of the ball’s impact on the racket and the ground to measure the time between them and give immediate feedback to the player. We performed trials with expert and amateur players in controlled settings. <b>Results:</b> The ML algorithm showed a detection accuracy higher than 95%, while the average accuracy of the whole system that was applied on-court was 85%. Moreover, this system has proven effective in evaluating the technical skills of a group of players on the court and highlighting their areas for improvement, showing significant potential for practical applications in player training and performance analysis. <b>Conclusions:</b> Quantitatively assessing timing offers a new perspective for coaches and players to improve performance and technique, providing objective data to set training regimens and optimize game strategies. |
| format | Article |
| id | doaj-art-3f4c91a48e194e6ba32f5b14a60fb904 |
| institution | Kabale University |
| issn | 2411-5142 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Functional Morphology and Kinesiology |
| spelling | doaj-art-3f4c91a48e194e6ba32f5b14a60fb9042025-08-20T03:43:39ZengMDPI AGJournal of Functional Morphology and Kinesiology2411-51422025-01-011014710.3390/jfmk10010047Tennis Timing Assessment by a Machine Learning-Based Acoustic Detection System: A Pilot StudyLucio Caprioli0Amani Najlaoui1Francesca Campoli2Aatheethyaa Dhanasekaran3Saeid Edriss4Cristian Romagnoli5Andrea Zanela6Elvira Padua7Vincenzo Bonaiuto8Giuseppe Annino9Sports Engineering Laboratory, Department of Industrial Engineering, University of Rome Tor Vergata, 00133 Rome, ItalySports Engineering Laboratory, Department of Industrial Engineering, University of Rome Tor Vergata, 00133 Rome, ItalySports Engineering Laboratory, Department of Industrial Engineering, University of Rome Tor Vergata, 00133 Rome, ItalySports Engineering Laboratory, Department of Industrial Engineering, University of Rome Tor Vergata, 00133 Rome, ItalySports Engineering Laboratory, Department of Industrial Engineering, University of Rome Tor Vergata, 00133 Rome, ItalyDepartment of Human Science and Promotion of Quality of Life, San Raffaele Rome University, 00166 Rome, ItalyRobotics and Artificial Intelligence Laboratory—ENEA “Casaccia” Research Centre, 00123 Rome, ItalyDepartment of Human Science and Promotion of Quality of Life, San Raffaele Rome University, 00166 Rome, ItalySports Engineering Laboratory, Department of Industrial Engineering, University of Rome Tor Vergata, 00133 Rome, ItalyHuman Performance Laboratory, Centre of Space Bio-Medicine, Department of Medicine Systems, University of Rome Tor Vergata, 00133 Rome, Italy<b>Background/Objectives:</b> In tennis, timing plays a crucial factor as it influences the technique and effectiveness of strokes and, therefore, matches results. However, traditional technical evaluation methods rely on subjective observations or video motion-tracking technology, mainly focusing on spatial components. This study evaluated the reliability of an acoustic detection system in analyzing key temporal elements of the game, such as the rally rhythm and timing of strokes. <b>Methods:</b> Based on a machine learning algorithm, the proposed acoustic detection system classifies the sound of the ball’s impact on the racket and the ground to measure the time between them and give immediate feedback to the player. We performed trials with expert and amateur players in controlled settings. <b>Results:</b> The ML algorithm showed a detection accuracy higher than 95%, while the average accuracy of the whole system that was applied on-court was 85%. Moreover, this system has proven effective in evaluating the technical skills of a group of players on the court and highlighting their areas for improvement, showing significant potential for practical applications in player training and performance analysis. <b>Conclusions:</b> Quantitatively assessing timing offers a new perspective for coaches and players to improve performance and technique, providing objective data to set training regimens and optimize game strategies.https://www.mdpi.com/2411-5142/10/1/47tennistimingacoustic detectionmachine learningperformance analysis |
| spellingShingle | Lucio Caprioli Amani Najlaoui Francesca Campoli Aatheethyaa Dhanasekaran Saeid Edriss Cristian Romagnoli Andrea Zanela Elvira Padua Vincenzo Bonaiuto Giuseppe Annino Tennis Timing Assessment by a Machine Learning-Based Acoustic Detection System: A Pilot Study Journal of Functional Morphology and Kinesiology tennis timing acoustic detection machine learning performance analysis |
| title | Tennis Timing Assessment by a Machine Learning-Based Acoustic Detection System: A Pilot Study |
| title_full | Tennis Timing Assessment by a Machine Learning-Based Acoustic Detection System: A Pilot Study |
| title_fullStr | Tennis Timing Assessment by a Machine Learning-Based Acoustic Detection System: A Pilot Study |
| title_full_unstemmed | Tennis Timing Assessment by a Machine Learning-Based Acoustic Detection System: A Pilot Study |
| title_short | Tennis Timing Assessment by a Machine Learning-Based Acoustic Detection System: A Pilot Study |
| title_sort | tennis timing assessment by a machine learning based acoustic detection system a pilot study |
| topic | tennis timing acoustic detection machine learning performance analysis |
| url | https://www.mdpi.com/2411-5142/10/1/47 |
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