AGIR: Assessing 3D Gait Impairment with Reasoning based on LLMs
This addresses the need for precise and interpretable gait assessment in clinical practice for neurodegenerative disease diagnosis and monitoring, representing a novel hybrid approach.
The paper tackled the problem of assessing gait impairment for neurodegenerative diseases by introducing AGIR, a pipeline combining a VQ-VAE motion tokenizer and a fine-tuned LLM with Chain-of-Thought reasoning, which achieved robust and accurate gait score assignment with clinically meaningful rationales.
Assessing gait impairment plays an important role in early diagnosis, disease monitoring, and treatment evaluation for neurodegenerative diseases. Despite its widespread use in clinical practice, it is limited by subjectivity and a lack of precision. While recent deep learning-based approaches have consistently improved classification accuracies, they often lack interpretability, hindering their utility in clinical decision-making. To overcome these challenges, we introduce AGIR, a novel pipeline consisting of a pre-trained VQ-VAE motion tokenizer and a subsequent Large Language Model (LLM) fine-tuned over pairs of motion tokens and Chain-of-Thought (CoT) reasonings. To fine-tune an LLM for pathological gait analysis, we first introduce a multimodal dataset by adding rationales dedicated to MDS-UPDRS gait score assessment to an existing PD gait dataset. We then introduce a two-stage supervised fine-tuning (SFT) strategy to enhance the LLM's motion comprehension with pathology-specific knowledge. This strategy includes: 1) a generative stage that aligns gait motions with analytic descriptions through bidirectional motion-description generation, 2) a reasoning stage that integrates logical Chain-of-Thought (CoT) reasoning for impairment assessment with UPDRS gait score. Validation on an existing dataset and comparisons with state-of-the-art methods confirm the robustness and accuracy of our pipeline, demonstrating its ability to assign gait impairment scores from motion input with clinically meaningful rationales.