Performance Benchmarking of Psychomotor Skills Using Wearable Devices: An Application in Sport
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916743418478592 |
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| author | Pandukabhaya, Mahela Fonseka, Tharaka Kulathunge, Madhumini Godaliyadda, Roshan Ekanayake, Parakrama Senanayake, Chanaka Herath, Vijitha |
| author_facet | Pandukabhaya, Mahela Fonseka, Tharaka Kulathunge, Madhumini Godaliyadda, Roshan Ekanayake, Parakrama Senanayake, Chanaka Herath, Vijitha |
| contents | Mastering psychomotor skills, such as those essential in sports, rehabilitation, and professional training, often requires a precise understanding of motion patterns and performance metrics. This study proposes a versatile framework for optimizing psychomotor learning through human motion analysis. Utilizing a wearable IMU sensor system, the motion trajectories of a given psychomotor task are acquired and then linked to points in a performance space using a predefined set of quality metrics specific to the psychomotor skill. This enables the identification of a benchmark cluster in the performance space, which represents a group of reference points that define optimal performance across multiple criteria, allowing correspondences to be established between the performance clusters and sets of trajectories in the motion space. As a result, common or specific deviations in the performance space can be identified, enabling remedial actions in the motion space to optimize performance. A thorough validation of the proposed framework is done in this paper using a Table Tennis forehand stroke as a case study. The resulting quantitative and visual representation of performance empowers individuals to optimize their skills and achieve peak performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_16168 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Performance Benchmarking of Psychomotor Skills Using Wearable Devices: An Application in Sport Pandukabhaya, Mahela Fonseka, Tharaka Kulathunge, Madhumini Godaliyadda, Roshan Ekanayake, Parakrama Senanayake, Chanaka Herath, Vijitha Signal Processing Mastering psychomotor skills, such as those essential in sports, rehabilitation, and professional training, often requires a precise understanding of motion patterns and performance metrics. This study proposes a versatile framework for optimizing psychomotor learning through human motion analysis. Utilizing a wearable IMU sensor system, the motion trajectories of a given psychomotor task are acquired and then linked to points in a performance space using a predefined set of quality metrics specific to the psychomotor skill. This enables the identification of a benchmark cluster in the performance space, which represents a group of reference points that define optimal performance across multiple criteria, allowing correspondences to be established between the performance clusters and sets of trajectories in the motion space. As a result, common or specific deviations in the performance space can be identified, enabling remedial actions in the motion space to optimize performance. A thorough validation of the proposed framework is done in this paper using a Table Tennis forehand stroke as a case study. The resulting quantitative and visual representation of performance empowers individuals to optimize their skills and achieve peak performance. |
| title | Performance Benchmarking of Psychomotor Skills Using Wearable Devices: An Application in Sport |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2411.16168 |