AI RESEARCH
Evaluating Large Language Models for Gait Classification Using Text-Encoded Kinematic Waveforms
arXiv CS.LG
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ArXi:2603.13317v1 Announce Type: new Background: Machine learning (ML) enhances gait analysis but often lacks the level of interpretability desired for clinical adoption. Large Language Models (LLMs) may offer explanatory capabilities and confidence-aware outputs when applied to structured kinematic data. This study. therefore. evaluated whether general-purpose LLMs can classify continuous gait kinematics when represented as textual numeric sequences and how their performance compares to conventional ML approaches.