Automated worker skill evaluation for improving productivity based on labeled LDA

This paper proposed automated systems for analyzing elemental processes and for evaluating work skills. The systems use labeled latent Dirichlet allocation (L-LDA) to classify worker motions obtained from sensors into four elemental processes. L-LDA automatically learns characteristic motions, so there is no need to define and identify motion features. The proposed system predicts elemental processes with over 86.9% recall in experiments using the assembly process data. Analyst burden is greatly reduced as compared to systems requiring manual analysis of elemental processes from recorded task data. The system evaluates worker skills based on analyzed time series data for elemental processes in four categories, namely, correctness, stability, speed, and rhythm. As a result, the evaluation system clarifies workers’ strong and weak points in tasks performed in experiments, providing new knowledge that would be unobtainable under conventional evaluation methods. Manufacturing efficiency can be improved by allocating workers based on their strengths, and training efficiency will be improved when workers’ weak areas are revealed.