LLM-Conditioned Synthesis of Pathological Gaits via Structured Gait-Language Representations

2026-06-04

AI Summary

Pathological gait data are extremely scarce due to privacy constraints, difficulties in participant recruitment, high acquisition costs, and substantial inter-individual variability, severely hindering related research. To address this challenge, this work proposes a multimodal large language model–guided generative framework that synthesizes fixed-length 3D skeletal gait sequences from structured textual descriptions. The core innovations include a pathology-aware motion tokenizer that preserves critical pathological motion characteristics in discrete representations, and a semantic enhancement mechanism enabling controllable language-to-gait generation. Under a leave-one-subject-out protocol, a GRU classifier trained on a combination of real and synthetic data achieves an accuracy of 92.77%, demonstrating a significant improvement in downstream classification performance.

Abstract

Pathological gait datasets remain scarce due to privacy, recruitment, cost, and movement variability. Our work presents a multimodal LLM-guided framework for pathology-aware 3D gait data synthesis from structured textual descriptions. The proposed method generates fixed-length synthetic skeleton-based gait sequences for pathological gait classification tasks. The framework combines motion tokenisation, pathology-aware language conditioning, LLM-based semantic augmentation, and language-to-gait generation. A key contribution is the proposed pathological tokeniser, which is designed to preserve pathology-specific motion characteristics during discrete representation learning. Experiments suggest that the proposed synthetic sequences improve downstream classification for recurrent classifiers when combined with real data. The best result is obtained using a GRU classifier trained with real and synthetic samples, achieving 92.77\% accuracy under a leave-one-subject-out protocol.

Problem

• pathological gait
• data scarcity
• gait synthesis
• multimodal representation
• motion variability

Innovation

• pathological gait synthesis
• structured gait-language representation
• pathological tokeniser
• LLM-conditioned generation
• skeleton-based motion