Edge AI on Constrained Devices for Binary Sleep-Wake Classification in Dynamic Environments
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Computer Science > Machine Learning
Title:Edge AI on Constrained Devices for Binary Sleep-Wake Classification in Dynamic Environments
Abstract: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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.29163 [cs.LG] |
| (or arXiv:2609.29163v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.29163
arXiv-issued DOI via DataCite (pending registration)
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