Digitizing Coaching Intelligence: An Agentic Framework for Holistic Athlete Profiling using VLM and RAG
arXiv:2606.28570v1 Announce Type: cross Abstract: Athlete assessment is a critical process for tracking physical progress and identifying elite talent. However, during mass recruitment drives, traditional methods rely on manual observation, which is inherently subjective and unscalable, or basic computer vision (CV) systems limited to quantitative repetition counting. These standard approaches lack the "coaching intelligence" required to evaluate qualitative physiological markers such as form degradation, spinal articulation, and fatigue. This paper presents a novel, LLM-based hybrid agentic f
The convergence of advanced large language models (LLMs) with vision-language models (VLMs) and Retrieval Augmented Generation (RAG) capabilities now enables sophisticated, real-time qualitative analysis that surpasses previous computer vision limitations.
This paper demonstrates a significant leap in AI's ability to interpret complex human movement and physiological markers, moving beyond quantitative data to qualitative assessments crucial for high-stakes applications like elite athlete training.
Athlete assessment can now incorporate nuanced qualitative 'coaching intelligence' at scale, moving past subjective human observation and basic numerical tracking to an agentic, AI-driven evaluation of form and fatigue.
- · Elite Sports Organizations
- · Sports Technology Companies
- · Physical Therapy & Rehabilitation
- · AI Agent Developers
- · Traditional Sports Scouts (relying solely on subjective observation)
- · Basic Computer Vision Providers (limited to counting)
Mass recruitment drives and athlete development programs will integrate AI systems for more objective and scalable talent identification and progress tracking.
The methodology could extend to other complex human performance domains, such as surgical training, industrial safety, or dance choreography assessment, where subtle qualitative feedback is critical.
This qualitative AI assessment capability might necessitate new ethical frameworks for AI interpretation of human 'potential' and 'failure,' especially in high-stakes competitive environments.
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