Explainable AI and Edge AI Integration for Hand Gesture Recognition in Industrial Robotics

Abstract

The integration of Explainable Artificial Intelligence (XAI) and Edge Artificial Intelligence has been investigated to enhance transparency and trust in intelligent systems for industrial robotics. A real-time hand gesture recognition system has been implemented using the YOLO deep learning model on the resource-constrained NVIDIA Jetson Orin Nano platform. To address the need for interpretability, several XAI techniques-SHAP, LIME, and Grad-CAM-have been applied to analyze and visualize decision-making process of the model. Performance and resource utilization metrics have been collected for each XAI method to evaluate trade-offs between interpretability and system efficiency. The limitations imposed by the edge hardware have been identified as a critical factor affecting the feasibility of real-time explanations. Wider conclusions have been devised and future research directions outlined. The study demonstrated the practical viability of deploying interpretable AI models on edge devices, contributing toward more transparent and interactive human-robot collaboration in industrial settings.