On-device AI for wearables: a practical edge pipeline
Build a wearable inference pipeline that connects sensor quality, model evaluation, power use, and versioned results.
WEAR API LAB / TOPIC
Edge AI guides
Wearable edge AI articles about sensor pipelines, on-device inference, model evaluation, and useful confidence states.
Edge AI places some inference work near the sensor. For a wearable, the choice affects memory, power, communication, update strategy, and what the application can explain about a result. The articles here start with a defined task and work outward to the complete pipeline.
A small model is only part of the design. Sampling, buffering, preparation, and radio activity also need measurement on the intended hardware. Decide which users and conditions your evaluation represents, and preserve model versions with results. An unknown or unavailable output can be more appropriate than a forced classification. Read the dedicated on-device guide for the general framework and the connected clothing guide for a case where signal conditions are part of the product itself. Together they support a practical conversation about where computation belongs and how its limits should appear in the interface.
Articles in edge ai
AI clothing APIs: build around the sensor, not the slogan
Turn a smart garment concept into a clear sensor contract, with practical checks for calibration, fit, connectivity, and model provenance.