GTA-Net: An IoT-integrated 3D human pose estimation system for real-time adolescent sports posture correction
With the advancement of artificial intelligence, 3D human pose estimation-based systems for sports training and posture correction have gained significant attention in adolescent sports. However, existing methods face challenges in handling complex movements, providing real-time feedback, and accomm...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S111001682401264X |
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| author | Shizhe Yuan Li Zhou |
| author_facet | Shizhe Yuan Li Zhou |
| author_sort | Shizhe Yuan |
| collection | DOAJ |
| description | With the advancement of artificial intelligence, 3D human pose estimation-based systems for sports training and posture correction have gained significant attention in adolescent sports. However, existing methods face challenges in handling complex movements, providing real-time feedback, and accommodating diverse postures, particularly with occlusions, rapid movements, and the resource constraints of Internet of Things (IoT) devices, making it difficult to balance accuracy and real-time performance. To address these issues, we propose GTA-Net, an intelligent system for posture correction and real-time feedback in adolescent sports, integrated within an IoT-enabled environment. This model enhances pose estimation in dynamic scenes by incorporating Graph Convolutional Networks (GCN), Temporal Convolutional Networks (TCN), and Hierarchical Attention mechanisms, achieving real-time correction through IoT devices. Experimental results show GTA-Net’s superior performance on Human3.6M, HumanEva-I, and MPI-INF-3DHP datasets, with Mean Per Joint Position Error (MPJPE) values of 32.2 mm, 15.0 mm, and 48.0 mm, respectively, significantly outperforming existing methods. The model also demonstrates strong robustness in complex scenarios, maintaining high accuracy even with occlusions and rapid movements. This system enhances real-time posture correction and offers broad applications in intelligent sports and health management. |
| format | Article |
| id | doaj-art-aeb10f7f76ab4335a4767583852e16b8 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-aeb10f7f76ab4335a4767583852e16b82025-01-29T05:00:12ZengElsevierAlexandria Engineering Journal1110-01682025-01-01112585597GTA-Net: An IoT-integrated 3D human pose estimation system for real-time adolescent sports posture correctionShizhe Yuan0Li Zhou1School of Physical Education, Xinyang Normal University, Xinyang, 464000, China; Corresponding author.McGill University Montréal, 27708, CanadaWith the advancement of artificial intelligence, 3D human pose estimation-based systems for sports training and posture correction have gained significant attention in adolescent sports. However, existing methods face challenges in handling complex movements, providing real-time feedback, and accommodating diverse postures, particularly with occlusions, rapid movements, and the resource constraints of Internet of Things (IoT) devices, making it difficult to balance accuracy and real-time performance. To address these issues, we propose GTA-Net, an intelligent system for posture correction and real-time feedback in adolescent sports, integrated within an IoT-enabled environment. This model enhances pose estimation in dynamic scenes by incorporating Graph Convolutional Networks (GCN), Temporal Convolutional Networks (TCN), and Hierarchical Attention mechanisms, achieving real-time correction through IoT devices. Experimental results show GTA-Net’s superior performance on Human3.6M, HumanEva-I, and MPI-INF-3DHP datasets, with Mean Per Joint Position Error (MPJPE) values of 32.2 mm, 15.0 mm, and 48.0 mm, respectively, significantly outperforming existing methods. The model also demonstrates strong robustness in complex scenarios, maintaining high accuracy even with occlusions and rapid movements. This system enhances real-time posture correction and offers broad applications in intelligent sports and health management.http://www.sciencedirect.com/science/article/pii/S111001682401264X3D human pose estimationIntelligent sports trainingTemporal convolutional networksInternet of ThingsReal-time feedbackGraph Convolutional Networks |
| spellingShingle | Shizhe Yuan Li Zhou GTA-Net: An IoT-integrated 3D human pose estimation system for real-time adolescent sports posture correction Alexandria Engineering Journal 3D human pose estimation Intelligent sports training Temporal convolutional networks Internet of Things Real-time feedback Graph Convolutional Networks |
| title | GTA-Net: An IoT-integrated 3D human pose estimation system for real-time adolescent sports posture correction |
| title_full | GTA-Net: An IoT-integrated 3D human pose estimation system for real-time adolescent sports posture correction |
| title_fullStr | GTA-Net: An IoT-integrated 3D human pose estimation system for real-time adolescent sports posture correction |
| title_full_unstemmed | GTA-Net: An IoT-integrated 3D human pose estimation system for real-time adolescent sports posture correction |
| title_short | GTA-Net: An IoT-integrated 3D human pose estimation system for real-time adolescent sports posture correction |
| title_sort | gta net an iot integrated 3d human pose estimation system for real time adolescent sports posture correction |
| topic | 3D human pose estimation Intelligent sports training Temporal convolutional networks Internet of Things Real-time feedback Graph Convolutional Networks |
| url | http://www.sciencedirect.com/science/article/pii/S111001682401264X |
| work_keys_str_mv | AT shizheyuan gtanetaniotintegrated3dhumanposeestimationsystemforrealtimeadolescentsportsposturecorrection AT lizhou gtanetaniotintegrated3dhumanposeestimationsystemforrealtimeadolescentsportsposturecorrection |