GestLite: Lightweight Gesture Detection for Edge AI via Event-Driven Model and Input Adaptations
Abdul Basit, Manaal Waseem, Muhammad Abdullah Hanif, Muhammad Shafique · IEEE Access · 2026
Deep neural networks (DNNs) power many edge vision applications, yet their deployment is often constrained by stringent energy and latency budgets. In this work, we present GestLite, a DNN-based gesture detection system optimized for energy efficiency through joint application–system co-design. GestLite introduces three orthogonal optimization knobs: 1) model complexity, via structured width/depth scaling and head pruning, yielding a family of lightweight YOLO variants; 2) spatial resolution ( $r_{l}$ ), which applies input down-sampling (e.g., $640\rightarrow 160$ px), reducing early-layer MACs by up to approximately 93.75%; and 3) frame stride ( $k$ ), enabling adaptive frame skipping. A lightweight detector runs continuously at low resolution and low frame rate, invoking a high-fidelity model only on gesture-bearing frames. This dual-stage pipeline dynamically adapts its energy footprint to gesture activity through event-driven scheduling. Trained on the HaGRID dataset and deployed on Jetson Orin AGX, Gest Lite achieves up to $3\times $ reduction in average power while maintaining mAP $_{50} \!\gt \! 90\%$ . A full Pareto sweep across 900 configurations reveals that only 2% of designs are Pareto-optimal under joint knob optimization, highlighting the necessity of cross-coupled scheduling. The best configurations deliver F $1~{\gt }97.6\%$ at power budgets <0.42 W, with accuracy-per-watt exceeding 2.3 mAP/W. GestLite thus enables practical, energy-efficient gesture detection for mobile XR and automotive interfaces.