Expert-novice level classification using engineers’ motion data in subway tunnel inspections - Introduction of explainable graph convolutional network -

Abstract

Skill transfer to young engineers from senior engineers is a very important task in infrastructure equipment inspection. To support the skill transfer, an analysis method of the key factors of senior engineers skill is needed. However, conventional research has been limited to skill-level classification or analysis of the relationship between the skill level and biological data such as eye gaze and motion obtained from the engineers. This paper presents a method of classifying the skill level and visualization of its key factors to support the skill transfer. The proposed method employs a graph convolutional network introducing a novel attention mechanism for the classification and visualization.