English(EN) A fine-grained attention and geometric correspondence model for musculoskeletal risk classification in athletes using multimodal visual and skeletal features

新AI模型利用视觉和骨骼数据评估运动员受伤风险

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-10 04:00

研究人员开发了ViSK-GAT，一个新颖的深度学习框架，旨在评估运动员的肌肉骨骼风险。该多模态模型整合了视觉和骨骼数据，采用细粒度注意力模块进行模态内特征细化，以及多模态几何对应模块进行跨模态对齐。该系统在关键指标上达到了93%以上，并展现出0.1205的低RMSE和0.0156的MAE，优于现有的最先进方法。 AI

影响该模型能够更早地检测到运动员潜在的伤病，从而实现个性化的训练调整和伤病预防策略。

排序理由这是一篇研究论文，详细介绍了一种用于特定应用的新型深度学习模型。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Md. Abdur Rahman, Mohaimenul Azam Khan Raiaan, Tamanna Shermin, Md Rafiqul Islam, Mukhtar Hussain, Sami Azam · 2026-06-10 04:00

A fine-grained attention and geometric correspondence model for musculoskeletal risk classification in athletes using multimodal visual and skeletal features

arXiv:2509.05913v3 Announce Type: replace Abstract: Musculoskeletal disorders pose significant risks to athletes, and early risk assessment is essential for prevention. However, most existing methods are designed for controlled settings and fail to reliably assess risk in complex…

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A fine-grained attention and geometric correspondence model for musculoskeletal risk classification in athletes using multimodal visual and skeletal features

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