Editing AI for Wearable Health Devices (section)

== <span style="color: #FFFFFF;">Creating</span> ==
Designing a wearable health monitoring AI system: (1) Signal pipeline: sensor driver → bandpass filter → artifact rejection (accelerometer-based motion exclusion) → feature extraction. (2) ML model: 1D CNN or LSTM on sliding windows (30s to 5min depending on application); output: probability score per window. (3) Episode-level decision: aggregate consecutive window scores using voting or HMM to make episode-level diagnosis. (4) Clinical validation: FDA-grade validation study; 500+ participants; diverse demographics; comparison to clinical gold standard. (5) Regulatory: FDA De Novo for novel diagnostic claims; 510(k) for predicate-based devices. (6) User experience: notifications must be actionable, non-alarming for low-risk findings, and always direct to a clinician for confirmation.

[[Category:Artificial Intelligence]]
[[Category:Wearables]]
[[Category:Digital Health]]
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