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arXiv Machine Learning By Stefan Reitmann, Lena Oden

Edge AI on Constrained Devices for Binary Sleep-Wake Classification in Dynamic Environments

Read the original on arXiv Machine Learning →

This paper introduces an Edge AI system that classifies sleep and wake states on constrained devices using a multimodal pipeline on an ESP32‑S3 microcontroller. It fuses inertial head‑movement sensing with visual pose classification, running in parallel under FreeRTOS to meet real‑time constraints. The two‑stage detection achieves 96.5 % accuracy for motion‑based detection and 89 % for pose classification, proving robust binary sleep‑wake classification in mobile scenarios.

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