AI Wearable Evaluation Protocol

AI Wearable Evaluation Protocol

Begin with the job rather than the device. Define the wearer, task, environment, frequency, success threshold, affected people, current alternative, and consequence of failure.

Evaluate hardware fit, comfort, battery, input, output, connectivity, account, region, prescription or accessibility needs, software lifecycle, support, repair, return policy, and total cost.

Trace the data path from sensor activation through local processing, transmission, account association, model processing, storage, review, sharing, deletion, and downstream action. Record which controls apply to the wearer and which protections depend on bystanders noticing or understanding a signal.

Test representative conditions, including noise, motion, poor lighting, accents, occlusion, weak connectivity, interruption, mistaken activation, incorrect output, unavailable service, and recovery.

A purchase decision should compare accepted work and real burden against the current alternative. Vendor demonstrations, launch prices, and broad accessibility stories do not establish fit for one person.