Automatic Stroke Measurement Method in Speed Skating: Analysis of the First 100 m after the Start
In speed skating, the number of strokes in the first 100 m section serves as an important metric of final performance. However, the conventional method, relying on human vision, has limitations in terms of real-time counting and accuracy. This study presents a solution for counting strokes in the fi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/22/4651 |
| _version_ | 1797459447871700992 |
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| author | Yeong-Je Park Ji-Yeon Moon Eui Chul Lee |
| author_facet | Yeong-Je Park Ji-Yeon Moon Eui Chul Lee |
| author_sort | Yeong-Je Park |
| collection | DOAJ |
| description | In speed skating, the number of strokes in the first 100 m section serves as an important metric of final performance. However, the conventional method, relying on human vision, has limitations in terms of real-time counting and accuracy. This study presents a solution for counting strokes in the first 100 m of a speed skating race, aiming to overcome the limitations of human vision. The method uses image recognition technology, specifically MediaPipe, to track key body joint coordinates during the skater’s motion. These coordinates are calculated into important body angles, including those from the shoulder to the knee and from the pelvis to the ankle. To quantify the skater’s motion, the study introduces generalized labeling logic (GLL), a key index derived from angle data. The GLL signal is refined using Gaussian filtering to remove noise, and the number of inflection points in the filtered GLL signal is used to determine the number of strokes. The method was designed with a focus on frontal videos and achieved an excellent accuracy of 99.91% when measuring stroke counts relative to actual counts. This technology has great potential for enhancing training and evaluation in speed skating. |
| first_indexed | 2024-03-09T16:52:28Z |
| format | Article |
| id | doaj.art-1809ae94aaa0425c8b9f2bae793a07f7 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T16:52:28Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-1809ae94aaa0425c8b9f2bae793a07f72023-11-24T14:39:30ZengMDPI AGElectronics2079-92922023-11-011222465110.3390/electronics12224651Automatic Stroke Measurement Method in Speed Skating: Analysis of the First 100 m after the StartYeong-Je Park0Ji-Yeon Moon1Eui Chul Lee2Department of Artificial Intelligence and Informatics, Graduate School, Sangmyung University, Hongjimun 2-Gil 20, Jongno-Gu, Seoul 03016, Republic of KoreaDepartment of Physical Education, Graduate School, Sangmyung University, Hongjimun 2-Gil 20, Jongno-gu, Seoul 03016, Republic of KoreaDepartment of Human-Centered Artificial Intelligence, Sangmyung University, Hongjimun 2-Gil 20, Jongno-Gu, Seoul 03016, Republic of KoreaIn speed skating, the number of strokes in the first 100 m section serves as an important metric of final performance. However, the conventional method, relying on human vision, has limitations in terms of real-time counting and accuracy. This study presents a solution for counting strokes in the first 100 m of a speed skating race, aiming to overcome the limitations of human vision. The method uses image recognition technology, specifically MediaPipe, to track key body joint coordinates during the skater’s motion. These coordinates are calculated into important body angles, including those from the shoulder to the knee and from the pelvis to the ankle. To quantify the skater’s motion, the study introduces generalized labeling logic (GLL), a key index derived from angle data. The GLL signal is refined using Gaussian filtering to remove noise, and the number of inflection points in the filtered GLL signal is used to determine the number of strokes. The method was designed with a focus on frontal videos and achieved an excellent accuracy of 99.91% when measuring stroke counts relative to actual counts. This technology has great potential for enhancing training and evaluation in speed skating.https://www.mdpi.com/2079-9292/12/22/4651speed skatingstraight sectionstrokeimage recognitionposture analysis |
| spellingShingle | Yeong-Je Park Ji-Yeon Moon Eui Chul Lee Automatic Stroke Measurement Method in Speed Skating: Analysis of the First 100 m after the Start Electronics speed skating straight section stroke image recognition posture analysis |
| title | Automatic Stroke Measurement Method in Speed Skating: Analysis of the First 100 m after the Start |
| title_full | Automatic Stroke Measurement Method in Speed Skating: Analysis of the First 100 m after the Start |
| title_fullStr | Automatic Stroke Measurement Method in Speed Skating: Analysis of the First 100 m after the Start |
| title_full_unstemmed | Automatic Stroke Measurement Method in Speed Skating: Analysis of the First 100 m after the Start |
| title_short | Automatic Stroke Measurement Method in Speed Skating: Analysis of the First 100 m after the Start |
| title_sort | automatic stroke measurement method in speed skating analysis of the first 100 m after the start |
| topic | speed skating straight section stroke image recognition posture analysis |
| url | https://www.mdpi.com/2079-9292/12/22/4651 |
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