Machine Learning Approach for Pitch Type Classification Based on Pelvis and Trunk Kinematics Captured with Wearable Sensors
The large stream of data from wearable devices integrated with sports routines has changed the traditional approach to athletes’ training and performance monitoring. However, one of the challenges of data-driven training is to provide actionable insights tailored to individual training optimization....
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/23/9373 |
| _version_ | 1797399583666470912 |
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| author | Larisa Gomaz Celine Bouwmeester Erik van der Graaff Bart van Trigt DirkJan Veeger |
| author_facet | Larisa Gomaz Celine Bouwmeester Erik van der Graaff Bart van Trigt DirkJan Veeger |
| author_sort | Larisa Gomaz |
| collection | DOAJ |
| description | The large stream of data from wearable devices integrated with sports routines has changed the traditional approach to athletes’ training and performance monitoring. However, one of the challenges of data-driven training is to provide actionable insights tailored to individual training optimization. In baseball, the pitching mechanics and pitch type play an essential role in pitchers’ performance and injury risk management. The optimal manipulation of kinematic and temporal parameters within the kinetic chain can improve the pitcher’s chances of success and discourage the batter’s anticipation of a particular pitch type. Therefore, the aim of this study was to provide a machine learning approach to pitch type classification based on pelvis and trunk peak angular velocity and their separation time recorded using wearable sensors (PITCHPERFECT). The Naive Bayes algorithm showed the best performance in the binary classification task and so did Random Forest in the multiclass classification task. The accuracy of Fastball classification was 71%, whilst the accuracy of the classification of three different pitch types was 61.3%. The outcomes of this study demonstrated the potential for the utilization of wearables in baseball pitching. The automatic detection of pitch types based on pelvis and trunk kinematics may provide actionable insight into pitching performance during training for pitchers of various levels of play. |
| first_indexed | 2024-03-09T01:43:11Z |
| format | Article |
| id | doaj.art-94d224fdf6ff4363aa06b00a252563e5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T01:43:11Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-94d224fdf6ff4363aa06b00a252563e52023-12-08T15:25:42ZengMDPI AGSensors1424-82202023-11-012323937310.3390/s23239373Machine Learning Approach for Pitch Type Classification Based on Pelvis and Trunk Kinematics Captured with Wearable SensorsLarisa Gomaz0Celine Bouwmeester1Erik van der Graaff2Bart van Trigt3DirkJan Veeger4Delft Institute of Applied Mathematics, Delft University of Technology, 2628 CD Delft, The NetherlandsBioMechanical Engineering, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, 2628 CD Delft, The NetherlandsPITCHPERFECT, 4814 GA Breda, The NetherlandsBioMechanical Engineering, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, 2628 CD Delft, The NetherlandsBioMechanical Engineering, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, 2628 CD Delft, The NetherlandsThe large stream of data from wearable devices integrated with sports routines has changed the traditional approach to athletes’ training and performance monitoring. However, one of the challenges of data-driven training is to provide actionable insights tailored to individual training optimization. In baseball, the pitching mechanics and pitch type play an essential role in pitchers’ performance and injury risk management. The optimal manipulation of kinematic and temporal parameters within the kinetic chain can improve the pitcher’s chances of success and discourage the batter’s anticipation of a particular pitch type. Therefore, the aim of this study was to provide a machine learning approach to pitch type classification based on pelvis and trunk peak angular velocity and their separation time recorded using wearable sensors (PITCHPERFECT). The Naive Bayes algorithm showed the best performance in the binary classification task and so did Random Forest in the multiclass classification task. The accuracy of Fastball classification was 71%, whilst the accuracy of the classification of three different pitch types was 61.3%. The outcomes of this study demonstrated the potential for the utilization of wearables in baseball pitching. The automatic detection of pitch types based on pelvis and trunk kinematics may provide actionable insight into pitching performance during training for pitchers of various levels of play.https://www.mdpi.com/1424-8220/23/23/9373baseballpitchingwearablesclassificationpitch types |
| spellingShingle | Larisa Gomaz Celine Bouwmeester Erik van der Graaff Bart van Trigt DirkJan Veeger Machine Learning Approach for Pitch Type Classification Based on Pelvis and Trunk Kinematics Captured with Wearable Sensors Sensors baseball pitching wearables classification pitch types |
| title | Machine Learning Approach for Pitch Type Classification Based on Pelvis and Trunk Kinematics Captured with Wearable Sensors |
| title_full | Machine Learning Approach for Pitch Type Classification Based on Pelvis and Trunk Kinematics Captured with Wearable Sensors |
| title_fullStr | Machine Learning Approach for Pitch Type Classification Based on Pelvis and Trunk Kinematics Captured with Wearable Sensors |
| title_full_unstemmed | Machine Learning Approach for Pitch Type Classification Based on Pelvis and Trunk Kinematics Captured with Wearable Sensors |
| title_short | Machine Learning Approach for Pitch Type Classification Based on Pelvis and Trunk Kinematics Captured with Wearable Sensors |
| title_sort | machine learning approach for pitch type classification based on pelvis and trunk kinematics captured with wearable sensors |
| topic | baseball pitching wearables classification pitch types |
| url | https://www.mdpi.com/1424-8220/23/23/9373 |
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