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.

本文提出了用于分析基本过程和评估工作技能的自动化系统。该系统使用标记的潜在狄利克雷分配（L-LDA）将从传感器获得的工人运动分类为四个基本过程。L-LDA自动学习特征运动，因此无需定义和识别运动特征。拟议的系统使用组装过程数据预测实验中的元素过程，召回率超过86.9％。与需要从记录的任务数据中手动分析基本过程的系统相比，大大减轻了分析人员的负担。该系统基于分析的时间序列数据，针对四个类别的要素过程，即正确性，稳定性，速度和节奏，评估工人的技能。结果是，该评估系统阐明了工人在实验中执行任务的优缺点，提供了传统评估方法无法获得的新知识。通过根据工人的优势分配工人可以提高制造效率，而当工人的弱点被发现时，培训效率也将得到提高。