[Paper] OpenGlass: Open-Source Smart Glasses for On-Device Event-Based Gesture Recognition
Source: arXiv - 2606.07431v1
Overview
Smart eyewear enables unobtrusive, context-aware interaction through multimodal sensors and on-device intelligence, but is severely limited by power, memory, and compute constraints in a compact form factor. Open-hardware platforms supporting event-based vision and embedded ML at this scale are rare. This work introduces an open-source smart glasses platform for rapid prototyping of novel sensors and algorithms. Its modular design uses a flexible FPC interposer to support both event-based and frame-based cameras without full PCB redesign. A hardware-software co-designed power management system combines a configurable PMIC with event-driven wake-up via an nRF5340 coordinator, keeping the GAP9 RISC-V SoC powered down between inferences. The prototype achieves up to 11.8 hours of continuous on-device ML from a 200 mAh battery. As a demonstration, an egocentric hand gesture recognition pipeline was evaluated on the LynX dataset using polarity-separated event histograms from a Prophesee GENX320 camera. R(2+1)D achieved the best cross-subject accuracy of 83.94% (macro F1 = 0.781) under leave-two-subjects-out validation, with 33.9 ms end-to-end latency on the GAP9. Temporal augmentation and removal of ambiguous classes provided the largest gains (+8.9 pp). All hardware designs, firmware, and models are released open source.
Key Contributions
This paper presents research in the following areas:
- cs.CV
Methodology
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Practical Implications
This research contributes to the advancement of cs.CV.
Authors
- Pietro Bonazzi
- Julian Moosmann
- Ahmet Celik
- Philipp Mayer
- Michele Magno
Paper Information
- arXiv ID: 2606.07431v1
- Categories: cs.CV
- Published: June 5, 2026
- PDF: Download PDF