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In the evolving landscape of digital health, remote rehabilitation emerges as a pivotal strategy to enhance patient recovery outside traditional clinical settings. This conceptual manuscript introduces a novel framework leveraging smartphone-embedded sensors to quantify rehabilitation progress through motion primitives—fundamental movement units that enable interpretable tracking of recovery trajectories. By decomposing complex rehabilitative exercises into atomic motion elements, the proposed system facilitates granular analysis of patient adherence, functional improvements, and potential deviations in remote environments. Drawing on theoretical principles from biomechanics, signal processing, and human-computer interaction, we outline an architectural design that integrates real-time data capture, primitive extraction, and interpretive visualization without relying on empirical validation or machine learning models. The framework emphasizes interpretability by mapping primitives to clinical recovery milestones, thereby supporting clinicians in remote decision-making. Key conceptual elements include hierarchical primitive decomposition, temporal alignment mechanisms, and feedback loops for progress quantification. Formulas are presented to model decision confidence in primitive-based assessments and resource allocation for remote monitoring. This approach addresses gaps in current remote rehabilitation paradigms by prioritizing accessibility via ubiquitous smartphones, reducing dependency on specialized wearables, and enhancing patient empowerment through transparent recovery insights. Ultimately, the framework posits a scalable infrastructure for interpretable recovery tracking, fostering equitable access to rehabilitation analytics in diverse socioeconomic contexts. While theoretical, it lays the groundwork for future implementations in post-surgical, neurological, and musculoskeletal recovery scenarios.