BioGait-VLM：一种多模态视觉-语言-生物力学框架用于可解释的临床步态评估
BioGait-VLM: A Tri-Modal Vision-Language-Biomechanics Framework for Interpretable Clinical Gait Assessment
AI总结：
BioGait-VLM通过多模态框架提升临床步态评估的可解释性和准确性，结合生物力学标记与时间动态分析，实现更可靠的病理运动识别。
摘要：
基于视频的临床步态分析常常由于模型过度拟合环境偏见而泛化能力差，无法捕捉病理运动。为了解决这一问题，我们提出了BioGait-VLM，一种三模态视觉-语言-生物力学框架，用于可解释的临床步态评估。与标准视频编码器不同，我们的架构集成了一个时间证据蒸馏分支，以捕捉节律动态，并且一个生物力学标记分支将3D骨骼序列投影到语言对齐的语义标记中。这使模型能够独立于视觉捷径来明确推理关节力学。为了确保严谨的基准测试，我们增强了公共GAVD数据集，添加了一个高保真度的退行性颈椎髓鞘病（DCM）队列，形成一个统一的8类分类体系，建立严格受试者不重叠的协议以防止数据泄漏。在此设置下，BioGait-VLM实现了最先进的识别准确率。此外，一项盲审专家研究证实，生物力学标记显著提高了临床合理性与证据基础，为透明、隐私增强的步态评估提供了新路径。
英文摘要：
Video-based Clinical Gait Analysis often suffers from poor generalization as models overfit environmental biases instead of capturing pathological motion. To address this, we propose BioGait-VLM, a tri-modal Vision-Language-Biomechanics framework for interpretable clinical gait assessment. Unlike standard video encoders, our architecture incorporates a Temporal Evidence Distillation branch to capture rhythmic dynamics and a Biomechanical Tokenization branch that projects 3D skeleton sequences into language-aligned semantic tokens. This enables the model to explicitly reason about joint mechanics independent of visual shortcuts. To ensure rigorous benchmarking, we augment the public GAVD dataset with a high-fidelity Degenerative Cervical Myelopathy (DCM) cohort to form a unified 8-class taxonomy, establishing a strict subject-disjoint protocol to prevent data leakage. Under this setting, BioGait-VLM achieves state-of-the-art recognition accuracy. Furthermore, a blinded expert study confirms that biomechanical tokens significantly improve clinical plausibility and evidence grounding, offering a path toward transparent, privacy-enhanced gait assessment.
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