Sit-and-Reach Pose Detection Based on Self-Train Method and Ghost-ST-GCN
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| Publicado en: | Sensors vol. 25, no. 18 (2025), p. 5624-5642 |
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| Autor principal: | |
| Otros Autores: | , |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 003 | UK-CbPIL | ||
| 022 | |a 1424-8220 | ||
| 024 | 7 | |a 10.3390/s25185624 |2 doi | |
| 035 | |a 3254645935 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231630 |2 nlm | ||
| 100 | 1 | |a Jiang Shuheng |u Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China | |
| 245 | 1 | |a Sit-and-Reach Pose Detection Based on Self-Train Method and Ghost-ST-GCN | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The sit-and-reach test is a common stretching exercise suitable for adolescents, aimed at improving joint flexibility and somatic neural control, and has become a mandatory item in China’s student physical fitness assessments. However, many students tend to perform incorrect postures during their practice, which may lead to sports injuries such as muscle strains if sustained over time. To address this issue, this paper proposes a Ghost-ST-GCN model for judging the correctness of the sit-and-reach pose. The model first requires detecting seven body keypoints. Leveraging a publicly available labeled keypoint dataset and unlabeled sit-and-reach videos, these keypoints are acquired through the proposed self-train method using the BlazePose network. Subsequently, the keypoints are fed into the Ghost-ST-GCN model, which consists of nine stacked GCN-TCN blocks. Critically, each GCN-TCN layer is embedded with a ghost layer to enhance efficiency. Finally, a classification layer determines the movement’s correctness. Experimental results demonstrate that the self-train method significantly improves the annotation accuracy of the seven keypoints; the integration of ghost layers streamlines the overall detection model; and the system achieves an action detection accuracy of 85.20% for the sit-and-reach exercise, with a response latency of less than 1 s. This approach is highly suitable for guiding adolescents to standardize their movements during independent sit-and-reach practice. | |
| 653 | |a Hip joint | ||
| 653 | |a Exercise | ||
| 653 | |a Accuracy | ||
| 653 | |a Physical fitness | ||
| 653 | |a Deep learning | ||
| 653 | |a Computer vision | ||
| 653 | |a Swimming | ||
| 653 | |a Optimization | ||
| 653 | |a Range of motion | ||
| 653 | |a Athletes | ||
| 653 | |a Coaches & managers | ||
| 653 | |a Posture | ||
| 653 | |a Online tutorials | ||
| 653 | |a Feedback | ||
| 653 | |a Sports injuries | ||
| 700 | 1 | |a Cui Haihua |u Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China | |
| 700 | 1 | |a Jin Liyuan |u University of California San Diego, La Jolla, CA 92093, USA | |
| 773 | 0 | |t Sensors |g vol. 25, no. 18 (2025), p. 5624-5642 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3254645935/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3254645935/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3254645935/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |