A Data Fusion Method Combining Image, Sensor, and Survey Data for Efficiency and Usability Analysis of Electric Power Tools in Industrial Environments

Abstract

The increasing integration of advanced technologies and automation in industrial production has heightened the importance of operational efficiency and safety. Among the critical components influencing workforce performance and product quality is the effective use of electric hand tools. However, the limited availability of comprehensive datasets and the absence of robust labeling methodologies present significant challenges for accurate data analysis and predictive modeling. This study addresses these limitations by incorporating field-collected data and multiple data acquisition techniques to identify relevant features for machine learning applications. An initial dataset comprising 51 attributes was systematically reduced to 16 through feature selection processes, enhancing its suitability for subsequent computational modeling. Several classification algorithms were evaluated for data labeling, with the Decision Tree method demonstrating superior performance in terms of accuracy. Despite these promising results, the dataset’s limited sample size (64 individuals) restricts the generalizability and reliability of machine learning outcomes. To mitigate this constraint, data augmentation techniques will be employed to generate synthetic instances, thereby expanding the dataset. Upon achieving a sufficient sample size, machine learning models will be developed to predict individuals’ proficiency with electric hand tools. This research contributes to the foundational knowledge required for efficient data collection, accurate labeling, and the development of predictive models in industrial settings.