Erdong Chen

The work proposes BioGait-VLM, a tri-modal (RGB vision, language, explicit biomechanics) framework built on a frozen InternVL3.5-1B, combining a Temporal Evidence Distillation branch with a Biomechanical Tokenization branch that projects 3D skeleton kinematics into language-aligned semantic tokens; on a subject-disjoint 8-class benchmark of 1,181 clips formed by merging public GAVD with a newly collected 30-patient degenerative cervical myelopathy (DCM) cohort, it reaches 68.1% accuracy and 52.9% macro F1 (98.1% F1 on DCM, 80.4% on Abnormal gait), and in a blinded review of 52 held-out clips four DCM-domain experts selected it as superior in 69.2% of cases (36/52, p<0.001).

The work proposes BioGait-VLM, a tri-modal (RGB vision, language, explicit biomechanics) framework built on a frozen InternVL3.5-1B, combining a Temporal Evidence Distillation branch with a Biomechanical Tokenization branch that projects 3D skeleton kinematics into language-aligned semantic tokens; on a subject-disjoint 8-class benchmark of 1,181 clips formed by merging public GAVD with a newly collected 30-patient degenerative cervical myelopathy (DCM) cohort, it reaches 68.1% accuracy and 52.9% macro F1 (98.1% F1 on DCM, 80.4% on Abnormal gait), and in a blinded review of 52 held-out clips four DCM-domain experts selected it as superior in 69.2% of cases (36/52, p<0.001).