Data-driven operator functional state classification in smart manufacturing

One of the main challenges in the industry is having trained and efficient operators in manufacturing lines. Smart adaptive guidance systems are developed that offer assistance to the operator during assembly. Depending on the operator’s level of execution, the system should be able to serve a different guidance response. This paper investigates the assessment and classification of the operator’s functional state using observed task execution times. Five different classifiers are studied for operator functional state classification on task execution time series. The experiments are based on an industry case and the ground truth is provided by an expert rule-based system. Three classification scenarios are defined that segment the problem on the level of the task, the individual, or the team. Furthermore, the investigation includes the evaluation of four distinct window-size configurations. The examination of how these scenarios and window-sizes influence the studied dataset across diverse classifiers reveals that achieving enhanced accuracy necessitates a larger input dimension. In this context, Convolutional Neural Networks predominantly exhibit superior performance compared to alternative classifiers. Careful attention needs to be paid to performance over classes and skills, but results confirm the validity of the approach for data-driven operator functional state classification.