Tennis action recognition and evaluation with inertial measurement unit and SVM
Action recognition in tennis plays a crucial role for athletes and coaches, aiding in understanding and evaluating the players' skill levels to formulate more effective training plans and tactical strategies. To enhance the recognition and grading of tennis player actions, this study introduces...
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| Language: | English |
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Elsevier
2024-12-01
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| Series: | Systems and Soft Computing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772941924000838 |
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| _version_ | 1850054025316139008 |
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| author | Jinxia Gao Guodong Zhang |
| author_facet | Jinxia Gao Guodong Zhang |
| author_sort | Jinxia Gao |
| collection | DOAJ |
| description | Action recognition in tennis plays a crucial role for athletes and coaches, aiding in understanding and evaluating the players' skill levels to formulate more effective training plans and tactical strategies. To enhance the recognition and grading of tennis player actions, this study introduces the use of inertial measurement units and flexible resistive sensors for data collection. An improved Support Vector Machine is employed for data classification to achieve efficient action recognition. The results demonstrated that the proposed classification algorithm achieved an average accuracy of 95.35 % in recognizing actions of elite athletes, with the highest accuracy (96.38 %) observed in forehand strokes. In the case of sub-elite athletes, the algorithm achieved an impressive average accuracy of 97.67 %. For amateur enthusiasts, the algorithm exhibited an average accuracy of 94.08 %. Furthermore, elite athletes exhibited larger peak values in the three-axis acceleration waveform during ball striking. Specifically, the absolute peak value of acceleration in the Y-axis for elite athletes reached 78 m/s², representing an increase of 39 m/s² and 8 m/s² compared to the other two levels of athletes, respectively. Additionally, on the X and Z axes, elite athletes' acceleration peak values reached 59 m/s² and 78 m/s², significantly higher than those of sub-elite athletes and amateur enthusiasts. Moreover, the acceleration curves of elite athletes demonstrated a higher overall regularity. These findings indicate that the proposed action recognition method has a significant impact on recognition and evaluation, providing valuable insights for action recognition and assessment across various domains and advancing the application of artificial intelligence technology in the field of sports. |
| format | Article |
| id | doaj-art-bbfef8d2c87746358186a4abf5701e2a |
| institution | DOAJ |
| issn | 2772-9419 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Systems and Soft Computing |
| spelling | doaj-art-bbfef8d2c87746358186a4abf5701e2a2025-08-20T02:52:23ZengElsevierSystems and Soft Computing2772-94192024-12-01620015410.1016/j.sasc.2024.200154Tennis action recognition and evaluation with inertial measurement unit and SVMJinxia Gao0Guodong Zhang1Pysical Education College, Sichuan University, Chengdu 61000, PR ChinaDepartment of Sports, Shanxi Agricultural University, Jinzhong 030801, PR China; Corresponding authorAction recognition in tennis plays a crucial role for athletes and coaches, aiding in understanding and evaluating the players' skill levels to formulate more effective training plans and tactical strategies. To enhance the recognition and grading of tennis player actions, this study introduces the use of inertial measurement units and flexible resistive sensors for data collection. An improved Support Vector Machine is employed for data classification to achieve efficient action recognition. The results demonstrated that the proposed classification algorithm achieved an average accuracy of 95.35 % in recognizing actions of elite athletes, with the highest accuracy (96.38 %) observed in forehand strokes. In the case of sub-elite athletes, the algorithm achieved an impressive average accuracy of 97.67 %. For amateur enthusiasts, the algorithm exhibited an average accuracy of 94.08 %. Furthermore, elite athletes exhibited larger peak values in the three-axis acceleration waveform during ball striking. Specifically, the absolute peak value of acceleration in the Y-axis for elite athletes reached 78 m/s², representing an increase of 39 m/s² and 8 m/s² compared to the other two levels of athletes, respectively. Additionally, on the X and Z axes, elite athletes' acceleration peak values reached 59 m/s² and 78 m/s², significantly higher than those of sub-elite athletes and amateur enthusiasts. Moreover, the acceleration curves of elite athletes demonstrated a higher overall regularity. These findings indicate that the proposed action recognition method has a significant impact on recognition and evaluation, providing valuable insights for action recognition and assessment across various domains and advancing the application of artificial intelligence technology in the field of sports.http://www.sciencedirect.com/science/article/pii/S2772941924000838Inertial Measurement UnitSVMTennis action recognitionAction evaluationFlexible resistive sensor |
| spellingShingle | Jinxia Gao Guodong Zhang Tennis action recognition and evaluation with inertial measurement unit and SVM Systems and Soft Computing Inertial Measurement Unit SVM Tennis action recognition Action evaluation Flexible resistive sensor |
| title | Tennis action recognition and evaluation with inertial measurement unit and SVM |
| title_full | Tennis action recognition and evaluation with inertial measurement unit and SVM |
| title_fullStr | Tennis action recognition and evaluation with inertial measurement unit and SVM |
| title_full_unstemmed | Tennis action recognition and evaluation with inertial measurement unit and SVM |
| title_short | Tennis action recognition and evaluation with inertial measurement unit and SVM |
| title_sort | tennis action recognition and evaluation with inertial measurement unit and svm |
| topic | Inertial Measurement Unit SVM Tennis action recognition Action evaluation Flexible resistive sensor |
| url | http://www.sciencedirect.com/science/article/pii/S2772941924000838 |
| work_keys_str_mv | AT jinxiagao tennisactionrecognitionandevaluationwithinertialmeasurementunitandsvm AT guodongzhang tennisactionrecognitionandevaluationwithinertialmeasurementunitandsvm |