Wearable AI Device:
Put useful intelligence closer to the signal.

Choose a specific, testable task

Start with an observable outcome: identifying a movement pattern, detecting a wearing state, or classifying a short sensor window. Define what success means for the people and conditions your product will encounter. A model that separates examples in a development dataset has not automatically demonstrated useful performance in everyday wear. Keep evaluation users separate from training users when testing generalization to new people.

Budget the whole sensing pipeline

Inference time is only one part of the device workload. Sensing, buffering, feature preparation, radio communication, and wakeups also consume resources. Measure the full pipeline on the target hardware. Select a window length and reporting cadence that fit the task. Local inference can reduce the need to transmit some raw data, but the resulting privacy and battery characteristics still depend on the complete implementation.

Make model outputs interpretable to your app

Store the model version, window boundaries, and supported output labels with a result. Define an unknown or insufficient-quality state rather than forcing every input into a confident answer. Keep model updates separate from changes to the application’s data schema where possible. If a device cannot complete inference, decide whether the product can use a phone-side path or should simply explain that the feature is unavailable.

Make your first integration plan

Use the Docs and illustrative record examples to turn these questions into a small data contract. Then review the integration cost checklist before expanding device coverage. The goal is a first implementation whose assumptions are visible and testable.