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
Data quality guides
Learn to handle missing wearable measurements, timestamps, source context, duplicates, and model input quality.
Data quality is about whether a measurement is fit for the feature using it. A record may be present but late, valid but incomplete, or useful for a daily summary while unsuitable for a live display. These articles explore how to preserve that context instead of hiding it behind a polished chart.
Begin by separating missing data from zero, observation time from arrival time, and measured values from inferred scores. Then define a consistent rule for corrections and overlapping sources. The same care matters when those records become inputs to an AI model: an inference should retain its window and version context. Use the guides to build a small collection of difficult examples, and make the product’s behavior for each one an explicit decision. Coverage and provenance often explain a confusing result better than a more elaborate visualization.
Articles in data quality
Wearable data quality: handle gaps, time zones, and duplicates
A practical model for timestamps, provenance, missing observations, duplicate delivery, and revised records that keeps wearable charts explainable.
Smart ring APIs: make sleep data useful without overclaiming
Separate daily sleep scores from recorded sleep periods, then design a sync process and interface that preserve what the ring actually reports.
Smart watch APIs: from sensors to a useful workout
Move from raw sensor access to a useful watch workout with explicit capabilities, session ownership, durable records, and recovery tests.
Wearable API integration: a practical starting guide
Choose the right access route, define a usable data contract, and build a sync process that survives delayed, missing, and repeated records.