This paper presents an Edge AI-based system for detecting sleep and wake states in non-stationary mobile environments using resource-constrained embedded hardware.
Conventional approaches relying on accelerometer-based activity metrics are highly susceptible to motion and vibration artifacts and are limited by strict compute and energy budgets of wearable and IoT devices.
To address these challenges, a multimodal pipeline is designed and implemented on an ESP32-S3 microcontroller.
The system combines inertial sensing for head movement analysis and visual pose classification.
A dual-core architecture with FreeRTOS enables parallel execution of real-time data acquisition and on-device inference.
Sleep detection follows a two-stage strategy: low-movement detection over a temporal window, followed by visual validation of poses.
Experimental results show accuracies of 96.5% for motion-based detection and 89% for pose classification, yielding robust binary sleep-wake classification.
Field tests confirmed feasibility in representative mobile scenarios.
The results demonstrate that privacy-preserving, local sleep detection is achievable on edge hardware through careful co-design, while highlighting limitations in sensing intrusiveness, dataset scale, and system integration.