Fully-sensorized smart-eyewear platform for on-device Machine Learning
This paper presents ARGO, a smart eyewear platform leveraging the STM32N6 microcontroller and its integrated NPU for on-device machine learning, minimizing latency and preserving privacy. Through holistic co-design of hardware, firmware, and AI, an optimized YOLOv11 model is deployed for real-time urban obstacle recognition, introducing Head-wise Parallel Attention (HPA) for efficient NPU execution. The model achieves mAP50-95 of 24 with only 2.483 MB memory footprint. The platform integrates multimodal sensors, runs at 10 FPS, and provides ~113 minutes of autonomy on a 200 mAh battery, demonstrating the feasibility of high-performance, privacy-preserving assistive devices.
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[Submitted on 17 Jun 2026]
Title:Fully-sensorized smart-eyewear platform for on-device Machine Learning
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Abstract:This paper presents ARGO, a smart eyewear platform designed to bridge ergonomic comfort, high computational throughput, and energy efficiency. Unlike cloud-dependent solutions, ARGO leverages the STM32N6 microcontroller and its integrated Neural Processing Unit (NPU) to enable on-device machine learning, minimizing latency and preserving user privacy through local data processing. The primary contribution lies in the holistic co-design of hardware, firmware, and artificial intelligence, centered on the deployment of an optimized YOLOv11 model for real-time urban obstacle recognition. To ensure compatibility with the target NPU, we introduce Head-wise Parallel Attention (HPA), an architectural refinement that enables efficient accelerator execution while preserving the original computational logic. The model is trained on the Walking On The Road (WOTR) dataset, and the final deployed configuration achieves an mAP50-95 of 24 under strict memory constraints, with a memory footprint of only 2.483 MB. The platform integrates a multimodal sensor suite, RGB cameras, Time-of-Flight sensors, microphones, and ambient sensors, and delivers 10 FPS at a continuous autonomy of ~113 minutes on a 200 mAh battery. These results demonstrate the feasibility of a high-performance, privacy-preserving, and socially acceptable assistive device, and highlight how competitive edge AI solutions increasingly demand tightly integrated, multidisciplinary co-design approaches.
Comments: This work was carried out in the EssilorLuxottica "Smart Eyewear Lab", a Joint Research Center between EssilorLuxottica and Politecnico di Milano
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
Cite as: arXiv:2607.16222 [cs.LG]
(or arXiv:2607.16222v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.16222
arXiv-issued DOI via DataCite
Submission history
From: Pietro Bartoli Mr. [view email] [v1] Wed, 17 Jun 2026 17:11:39 UTC (16,555 KB)
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