Valid knowledge of performance provided by a motion capturing system in shot put
Extended feedback on knowledge of performance in sports techniques is very challenging and requires a high level of expertise. This poses a significant problem for experiments on providing extended feedback, as it is essential to ensure that the “correct” feedback is given for it to be effective. In...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Sports and Active Living |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fspor.2024.1482701/full |
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| author | Stefan Künzell Anna Knoblich Annika Stippler |
| author_facet | Stefan Künzell Anna Knoblich Annika Stippler |
| author_sort | Stefan Künzell |
| collection | DOAJ |
| description | Extended feedback on knowledge of performance in sports techniques is very challenging and requires a high level of expertise. This poses a significant problem for experiments on providing extended feedback, as it is essential to ensure that the “correct” feedback is given for it to be effective. In this study, we investigate whether the correct feedback can be determined based on kinematic data. Ten participants and one model were recorded during shot put using a Motion Capturing (MoCap) system and simultaneously captured on video. The videos were analysed by two experts, and the two most critical errors were noted. By qualitatively comparing the deviations of the participants from the model, the experts’ error feedback was identified in the motion curves of the MoCap system. The expert feedback for two participants was sealed in an envelope. In a qualitative analysis of the motion data, the error feedback was then determined and subsequently compared with the experts’ feedback. These error feedbacks largely matched. It was shown that, in principle, it is possible to extract errors from the kinematic angle and distance curves of the movement. This study opens the door to an automated version of the qualitative assessment of movements by AI. Further research can now focus on the topic of conveying AI-generated feedback. This could then also provide a valid foundation for experiments on the effects of knowledge of performance. |
| format | Article |
| id | doaj-art-6ba12516ca1740e980a8d750c846d4a4 |
| institution | Kabale University |
| issn | 2624-9367 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Sports and Active Living |
| spelling | doaj-art-6ba12516ca1740e980a8d750c846d4a42025-01-17T06:50:45ZengFrontiers Media S.A.Frontiers in Sports and Active Living2624-93672025-01-01610.3389/fspor.2024.14827011482701Valid knowledge of performance provided by a motion capturing system in shot putStefan KünzellAnna KnoblichAnnika StipplerExtended feedback on knowledge of performance in sports techniques is very challenging and requires a high level of expertise. This poses a significant problem for experiments on providing extended feedback, as it is essential to ensure that the “correct” feedback is given for it to be effective. In this study, we investigate whether the correct feedback can be determined based on kinematic data. Ten participants and one model were recorded during shot put using a Motion Capturing (MoCap) system and simultaneously captured on video. The videos were analysed by two experts, and the two most critical errors were noted. By qualitatively comparing the deviations of the participants from the model, the experts’ error feedback was identified in the motion curves of the MoCap system. The expert feedback for two participants was sealed in an envelope. In a qualitative analysis of the motion data, the error feedback was then determined and subsequently compared with the experts’ feedback. These error feedbacks largely matched. It was shown that, in principle, it is possible to extract errors from the kinematic angle and distance curves of the movement. This study opens the door to an automated version of the qualitative assessment of movements by AI. Further research can now focus on the topic of conveying AI-generated feedback. This could then also provide a valid foundation for experiments on the effects of knowledge of performance.https://www.frontiersin.org/articles/10.3389/fspor.2024.1482701/fullfeedbackexperimental studiesknowledge of performancevalidityobjectivity |
| spellingShingle | Stefan Künzell Anna Knoblich Annika Stippler Valid knowledge of performance provided by a motion capturing system in shot put Frontiers in Sports and Active Living feedback experimental studies knowledge of performance validity objectivity |
| title | Valid knowledge of performance provided by a motion capturing system in shot put |
| title_full | Valid knowledge of performance provided by a motion capturing system in shot put |
| title_fullStr | Valid knowledge of performance provided by a motion capturing system in shot put |
| title_full_unstemmed | Valid knowledge of performance provided by a motion capturing system in shot put |
| title_short | Valid knowledge of performance provided by a motion capturing system in shot put |
| title_sort | valid knowledge of performance provided by a motion capturing system in shot put |
| topic | feedback experimental studies knowledge of performance validity objectivity |
| url | https://www.frontiersin.org/articles/10.3389/fspor.2024.1482701/full |
| work_keys_str_mv | AT stefankunzell validknowledgeofperformanceprovidedbyamotioncapturingsysteminshotput AT annaknoblich validknowledgeofperformanceprovidedbyamotioncapturingsysteminshotput AT annikastippler validknowledgeofperformanceprovidedbyamotioncapturingsysteminshotput |