Real-Time Edge AI Sign Language Recognition via Motion History Images and Lightweight CNNs on Embedded Hardware

Project Title: Real-Time Edge AI Sign Language Recognition on Embedded Systems Hardware Stack: Raspberry Pi 4 Model B, OV5647 Camera Module, 3.5" LCD Core Algorithms: Motion History Image (MHI), Custom 4-Layer CNN (1.8M parameters) Key Metrics: 30 FPS live inference speed, 99.50% offline accuracy (6 gesture classes). Note: The offline split was performed at the frame level; this score does not establish accuracy on new sessions or participants. Live testing showed frame-to-frame prediction instability. Key Focus: Spatial-temporal feature extraction, embedded vision optimization, real-time prediction stability, Pearson correlation diagnostic. This document is the undergraduate graduation thesis (HCMUT, 2021) formatted as a conference paper. Code and dataset available from the authors upon reasonable request.