Fragmented wearable data blocks health products — unified open API + self‑hosted inference

Health-tech companies, hospitals, payers, and AI model builders struggle with highly fragmented wearable data: proprietary SDKs, inconsistent formats, missing labels, and varying signal quality make assembling longitudinal, structured inputs expensive and error‑prone. That fragmentation costs engineering teams time, reduces model reliability, and creates privacy and regulatory hurdles for enterprises that increasingly require on‑prem or private‑cloud deployment. You could build a unified open API and connector layer that normalizes signals from major wearables and emerging biosensors into a common longitudinal schema, paired with an optional self‑hosted inference runtime for running certified ML models on‑prem. Prioritize FHIR/HL7 compatibility, data‑quality metrics, labeling pipelines, and containerized model bundles so organizations can meet compliance, audit, and latency requirements; start by integrating the top 10 OEMs that together represent more than 70% of active wearable installs and offer SDKs for digital therapeutics and life‑science partners. The market is attractive now: a $95.0B total addressable market by 2028 ($50B wearable device market + $45B digital health platform/API market), a Market Score of 95/100 and Revenue Potential of 88/100, driven by richer sensors, AI needs for structured longitudinal inputs, and growing demand for on‑prem privacy. This approach can stand out by combining open interoperability with self‑hosted inference and enterprise compliance, but expect hard engineering work on device integrations, slow standards adoption, medium competition from incumbents, and the long sales cycles typical in regulated healthcare.