Wearable devices generate continuous, long-term measurements of individual lifestyle, offering potential for actionable insights in precision healthcare. However, from a clinical researcher’s perspective, challenges such as data heterogeneity, difficulties in access, standardization, and interpretation hinder effective processing and model training for intelligent sensor data analysis. These challenges are further exacerbated by access restrictions and inconsistent data formats across devices, often leading to bias in studies and ungeneralizable machine learning models. To address these issues, we present TechnoHealth, a modular software framework designed to address the significant challenges of data fragmentation and heterogeneity inherent in utilizing data from diverse wearable devices for precision healthcare research. Our architecture utilizes a hybrid data model that combines structured metadata (for context and provenance) with unstructured sensor data, thereby preserving the raw signal characteristics and enabling flexible, model-ready transformations. A pilot study is described to validate the framework’s end-to-end pipeline, focusing on the ingestion of heterogeneous signals and their transformation for rhythmicity detection models. The platform’s design adheres to software engineering principles, including modularity, for enhanced transparency and extensibility. Technohealth supports broader and more scalable health studies, which in turn can lead to more reproducible science and improved understanding of drivers of health outcomes.