An Intelligent Real-Time Skill Assessment System Based on Computer Vision and Convolutional Neural Networks

Chapter 1 Chapter 1 Introduction 1.1 Motivation With the boom of AI and CV technologies, it becomes difficult to objectively and scalably assess the skill through traditional skill assessment techniques such as written tests or manual observations. These problems are especially significant in tasks that involve detailed action assessment, e.g. in manufacturing and vocational training. To tackle these challenges, we present an intelligent real-time skill assessment system that employs high-resolution RGB cameras, 3D depth sensors, and motion capturing equipment to capture the execution of tasks. The multimodal data streams are processed with a convolutional neural network (CNN)-based deep learning model to detect and assess main operational events. In an industrial case study the system reached 92% in recognizing key production steps such as component placement, screw tightening, and error correction. The proposed system provides objective, real-time, accurate, and scalable evaluations transcending those by traditional manual analyses. This work illustrates how computer vision and AI approaches can transform manually intensive skill assessment, enabling more affordable and consistent training methodologies across many domains.