OPTIMIZING FEEDBACK IN DIGITAL FITNESS ENVIRONMENTS: CAN THE INTEGRATION OF REAL-TIME POSE ESTIMATION AND RETRIEVAL-AUGMENTED GENERATION ARCHITECTURES IMPROVE USER ACCURACY?

Arce L.C.V.; Junco J.P.; Tapia B.J.R.; Contreras R.D.O. · Journal of Theoretical and Applied Information Technology (2026) · DOI: 10.5281/zenodo.20497466

Resumen

Physical inactivity and the lack of technical supervision in home-based training lead to increased injury risks and reduced exercise effectiveness. This study addresses this problem by developing FitPoseTracker, an intelligent platform that integrates computer vision and conversational artificial intelligence to optimize physical training. The proposed system, FitPoseTracker, performs real-time posture analysis and automatic repetition counting using human pose estimation and vectorial geometry, while a specialized conversational assistant based on a Retrieval-Augmented Generation (RAG) architecture provides contextualized technical guidance in natural language. The posture analysis module leverages MediaPipe to detect anatomical keypoints and compute joint angles, enabling accurate movement state classification. System performance was evaluated using a two-factor ANOVA to analyze the effects of exercise type and user experience level on repetition-counting accuracy. Results indicate that exercise type significantly influences accuracy (F(2,141)=166.98, p<2e−16), whereas user experience level shows no significant effect, demonstrating consistent performance across different skill levels. Squats achieved the highest accuracy, followed by situps and push-ups. Additionally, the conversational assistant was evaluated through automated testing on 50 representative queries, achieving high accuracy (0.92), recall (0.78), and F1-score (0.84), with strong contextual coherence. Overall, the results confirm that integrating real-time pose analysis with Large Language Models (LLMs) effectively bridges the gap between movement quantification and specialized technical guidance. © Little Lion Scientific