Helios 2.0: A Robust, Ultra-Low Power Gesture Recognition System Optimised for Event-Sensor based Wearables

[2503.07825] Helios 2.0: A Robust, Ultra-Low Power Gesture Recognition System Optimised for Event-Sensor based Wearables Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Human-Computer Interaction arXiv:2503.07825 (cs) [Submitted on 10 Mar 2025 ( v1 ), last revised 15 Aug 2026 (this version, v3)] Title: Helios 2.0: A Robust, Ultra-Low Power Gesture Recognition System Optimised for Event-Sensor based Wearables Authors: Prarthana Bhattacharyya , Joshua Mitton , Ryan Page , Owen Morgan , Oliver Powell , Benjamin Menzies , Gabriel Homewood , Kemi Jacobs , Paolo Baesso , Taru Muhonen , Richard Vigars , Louis Berridge View a PDF of the paper titled Helios 2.0: A Robust, Ultra-Low Power Gesture Recognition System Optimised for Event-Sensor based Wearables, by Prarthana Bhattacharyya and 10 other authors View PDF HTML (experimental) Abstract: We present an advance in wearable technology: a mobile-optimized, real-time, ultra-low-power event camera system that enables natural hand gesture control for smart glasses, dramatically improving user experience. While hand gesture recognition in computer vision has advanced significantly, critical challenges remain in creating systems that are intuitive, adaptable across diverse users and environments, and energy-efficient enough for practical wearable applications. Our approach tackles these challenges through carefully selected microgestures: lateral thumb swipes across the index finger (in both directions) and a double pinch between thumb and index fingertips. These human-centered interactions leverage natural hand movements, ensuring intuitive usability without requiring users to learn complex command sequences. To overcome variability in users and environments, we developed a novel simulation methodology that enables comprehensive domain sampling without extensive real-world data collection. Our power-optimised architecture maintains exceptional performance, achieving F1 scores above 80\% on benchmark datasets featuring diverse users and environments. The resulting models operate at just 6-8 mW when exploiting the Qualcomm Snapdragon Hexagon DSP, with our 2-channel implementation exceeding 70\% F1 accuracy and our 6-channel model surpassing 80\% F1 accuracy across all gesture classes in user studies. These results were achieved using only synthetic training data. This improves on the state-of-the-art for F1 accuracy by 20\% with a power reduction 25x when using DSP. This advancement brings deploying ultra-low-power vision systems in wearable devices closer and opens new possibilities for seamless human-computer interaction. Comments: To be presented at ECCV-2026 at the Event-Based Multimodal Vision Workshop. 24 pages, 14 figures. Prarthana Bhattacharyya, Joshua Mitton, Ryan Page, Owen Morgan, and Oliver Powell contributed equally to this paper Subjects: Human-Computer Interaction (cs.HC) ; Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) Cite as: arXiv:2503.07825 [cs.HC] (or arXiv:2503.07825v3 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2503.07825 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Prarthana Bhattacharyya [ view email ] [v1] Mon, 10 Mar 2025 20:12:06 UTC (37,473 KB) [v2] Mon, 2 Feb 2026 18:50:47 UTC (36,211 KB) [v3] Sat, 15 Aug 2026 13:27:48 UTC (36,631 KB) Full-text links: Access Paper: View a PDF of the paper titled Helios 2.0: A Robust, Ultra-Low Power Gesture Recognition System Optimised for Event-Sensor based Wearables, by Prarthana Bhattacharyya and 10 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.HC < prev | next > new | recent | 2025-03 Change to browse by: cs cs.CV cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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