A Framework for Lightweight, Edge-Based Recognition of Dynamic American Sign Language Using Temporal Learning Models

Abstract

Automated recognition of dynamic American Sign Language (ASL) gestures remains a significant challenge for real-time deployment on resource-constrained edge devices. Although recent advances in deep learning have achieved high accuracy in sign language recognition systems, such approaches typically rely on GPU acceleration and substantial computational resources, limiting their feasibility for accessible, real-world applications.This study proposes a theoretical and methodological framework for evaluating lightweight machine learning models for dynamic ASL recognition under CPU-dependent constraints. Grounded in Human-Computer Interaction Theory, Multimodal Communication Theory, and Computational Learning Theory, the framework formalizes the relationship between temporal representation, model complexity, and computational feasibility in edge-based environments.The proposed framework outlines a comparative evaluation strategy using pose-based time-series data extracted from glossed-annotated ASL videos, examining both sequence-preserving models (e.g., Canonical Interval Forest and InceptionTime) and aggregated-feature classifiers (e.g., Random Forest and Logistic Regression). Rather than reporting empirical findings, this paper establishes the conceptual foundations, modeling assumptions, and evaluation criteria necessary to determine whether lightweight classifiers can approximate the performance of deep learning approaches while remaining suitable for edge deployment.By explicitly linking theoretical principles to methodological design choices, this work provides a foundation for future empirical studies and contributes a structured approach for developing accessible, efficient, and scalable sign language recognition systems.