The paradigm of wearable health monitoring has historically relied on highly specialised, siloed digital health architectures. Traditionally, consumer smartwatches and clinical wearables have employed bespoke machine learning pipelines to detect isolated health metrics: one dedicated model for sleep stage classification, another for computing cardiovascular stress markers and a completely different pipeline for physical exertion.