Spatial Structure-oriented and Angle-based Human Pose Estimation for Pose Classification

Abstract

This study provides a detailed analysis of the performance of different pose classification models trained using data from the human pose classification model. The approach involves considering both spatial structure-oriented techniques, which incorporate body part coordinates and their relative positions, and angle-based methods that calculate the angles between joints. This combined spatial and angular data play a crucial role in enhancing the precision of pose classification. It is worth noting that while our primary investigation is based on a yoga pose dataset, the versatility and applicability of our approach extend to other pose datasets, showcasing the broad potential of our spatial and angle-based methodology. In summary, this research embarks on the integration of Human Pose Estimation with machine learning for yoga pose classification. The outcomes promise not only to advance the field of pose classification but also to yield practical applications in exercise, fitness, and beyond. This research has practical implications, aiming to integrate the developed model into a project we developed titled “AI-Based Human Pose Detection Tool. “The tool uses real-time video analysis to track users' movements during workouts, with the Blazepose model detecting key landmarks and assessing metrics. This enhances posture and form assessment, making the tool valuable for fitness enthusiasts.