Frameworks and Key Concepts for Developing Personalized Reference Models in Human Pose Estimation

This paper positions Personalized Reference Models (PRMs) in human pose estimation (HPE) as a structured research direction rather than as a completed implementation. Based on a focused review of biomechanics, sensing, calibration, adaptive learning, and ethical governance, the paper synthesizes the main frameworks required for building personalized HPE systems and proposes an integrated conceptual architecture for their development. To make the contribution explicit, the paper also outlines illustrative quantitative elements that may support anthropometric scaling, personalized baselines, adaptive tolerance envelopes, deviation scoring, and longitudinal updating. No new dataset, full implementation, or experimental validation is claimed here; instead, the contribution lies in organizing the state of the art into a coherent development logic and in identifying the design steps needed for future implementation and validation in rehabilitation, sports, and fitness applications.