SportsGPT: An LLM-driven Framework for Interpretable Sports Motion Assessment and Training Guidance

TL;DR

SportsGPT introduces an innovative framework combining motion analysis and large language models to provide interpretable sports motion assessment and personalized training guidance, outperforming traditional methods in accuracy and professionalism.

Contribution

The paper presents a novel LLM-driven framework integrating motion alignment, interpretable assessment, and knowledge-based training guidance for sports analysis.

Findings

MotionDTW outperforms traditional alignment methods with lower error.

KISMAM provides interpretable assessment metrics.

SportsRAG generates professional training guidance with higher accuracy.

Abstract

Existing intelligent sports analysis systems mainly focus on "scoring and visualization," often lacking automatic performance diagnosis and interpretable training guidance. Recent advances in Large Language Models (LLMs) and motion analysis techniques provide new opportunities to address the above limitations. In this paper, we propose SportsGPT, an LLM-driven framework for interpretable sports motion assessment and training guidance, which establishes a closed loop from motion time-series input to professional training guidance. First, given a set of high-quality target models, we introduce MotionDTW, a two-stage time series alignment algorithm designed for accurate keyframe extraction from skeleton-based motion sequences. Subsequently, we design a Knowledge-based Interpretable Sports Motion Assessment Model (KISMAM) to obtain a set of interpretable assessment metrics (e.g., insufficient extension) by contrasting the keyframes with the target models. Finally, we propose SportsRAG, a RAG-based training guidance model built upon Qwen3. Leveraging a 6B-token knowledge base, it prompts the LLM to generate professional training guidance by retrieving domain-specific QA pairs. Experimental results demonstrate that MotionDTW significantly outperforms traditional methods with lower temporal error and higher IoU scores. Furthermore, ablation studies validate the KISMAM and SportsRAG, confirming that SportsGPT surpasses general LLMs in diagnostic accuracy and professionalism.