Scale-Gest: Scalable Model-Space Synthesis and Runtime Selection for On-Device Gesture Detection
Scale-Gest presents a runtime-adaptive gesture detection framework that expands the detector space into a family of tiny-YOLO architectures, uses device-calibrated ACE profiles, and a lightweight controller to select optimal modes under battery constraints. It achieves 4x energy reduction per frame while maintaining high performance, and introduces the DSG-18 dataset.
[2605.12506] Scale-Gest: Scalable Model-Space Synthesis and Runtime Selection for On-Device Gesture Detection
[Submitted on 16 Mar 2026]
Title:Scale-Gest: Scalable Model-Space Synthesis and Runtime Selection for On-Device Gesture Detection
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Abstract:Realizing on-device ML-based gesture detection under tight real-time performance, energy and memory constraints is challenging, especially when considering mobile devices with varying battery-power levels. Existing EdgeAI deployments typically rely on a single fixed detector, limiting optimization opportunities. We present Scale-Gest, a novel run-time adaptive gesture detection framework that expands the detector space into a dense family of tiny-YOLO architectures. We introduce multiple novel device-calibrated ACE (Accuracy-Complexity-Energy) profiles by analyzing different model-resolution-stride operating points. A lightweight run-time controller selects an appropriate ACE mode under user-defined and battery constraints, while a motion-aware hand-gesture-tracking ROI gate crops the input for reduced complexity detection. To evaluate performance of our system in real-world car driving scenarios, we introduce a temporally-annotated Driver Simulated Gesture (DSG-18) dataset. Scale-Gest maintains event-level F1 while significantly reducing energy and latency compared to single-detector approaches. On a battery-powered laptop running gesture streams, our ACE controller reduces per-frame energy by 4x (from 6.9 mJ to 1.6 mJ) while maintaining high gesture-detection performance (event-level F1 = 0.8-0.9) and low mean latency (6 ms).
Comments: 7 pages, 11 figures, Accepted to DAC 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Robotics (cs.RO); Image and Video Processing (eess.IV)
ACM classes: I.2.10
Cite as: arXiv:2605.12506 [cs.CV]
(or arXiv:2605.12506v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.12506
arXiv-issued DOI via DataCite
Submission history
From: Abdul Basit [view email] [v1] Mon, 16 Mar 2026 10:12:26 UTC (14,333 KB)
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