GTA-Net: An IoT-Integrated 3D Human Pose Estimation System for Real-Time Adolescent Sports Posture Correction
Shizhe Yuan, Li Zhou

TL;DR
GTA-Net is an IoT-integrated 3D human pose estimation system that uses advanced neural networks to provide real-time sports posture correction for adolescents, outperforming existing methods in accuracy and robustness.
Contribution
The paper introduces GTA-Net, a novel system combining GCN, TCN, and Hierarchical Attention for real-time, accurate 3D pose estimation in IoT environments, addressing occlusions and rapid movements.
Findings
Achieves MPJPE of 32.2mm on Human3.6M
Outperforms existing methods in accuracy
Maintains robustness under occlusions and fast movements
Abstract
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…
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Taxonomy
TopicsHuman Pose and Action Recognition · Hand Gesture Recognition Systems · Infrared Thermography in Medicine
MethodsSoftmax · Attention Is All You Need
