This repository's public/default build uses a stub inference backend. Local
real Edge Impulse builds are supported through prj.local.conf and a
compatible deployment under third_party/edge_impulse/embr.
A compatible classification model must emit these exact labels:
embr_glowembr_sleepembr_flipunknownnoise
The app is currently aligned to:
16000 Hzaudio input1000 msinference window250 mswindow increase / stride
In repo terms, this means:
16000samples per inference window4000samples per slice4slices per inference window
If your model uses different labels or windowing parameters, update the app code to match before using it.
embr selects the highest-scoring label after each inference window. unknown
is the default outcome, and ties for the best score resolve to UNKNOWN.
embr_glow, embr_sleep, and embr_flip are actionable commands and are
forwarded to the transport layer. unknown and noise are valid no-op
outcomes; they do not produce transport messages.