Unsupervised Work Behavior Pattern Extraction Based on Hierarchical Probabilistic Model

In this study, we address the challenge of analyzing worker behaviors in high‐mix, low‐volume production environments, where traditional supervised learning methods struggle owing to the lack of labeled data and task variability among workers. To overcome these issues, we propose a novel hierarchical approach for unsupervised behavior pattern extraction using the Gaussian process‐hidden semi‐Markov model and hidden semi‐Markov model. Unlike existing studies that focus on clustering human actions, our method segments complex motion data into meaningful action units and tasks, enabling a deeper understanding of worker behaviors. This two‐layer probabilistic generative model performs segmentation without pretraining on labeled datasets, which is advantageous in dynamic industrial contexts. Experiments using six‐dimensional time‐series wrist movement time‐series data from three workers engaged in assembly tasks show that our approach significantly improves segmentation accuracy compared with baseline methods. The results demonstrate its effectiveness in identifying distinct behavior patterns, highlighting the potential of our method to advance machine learning‐based work analysis in industrial environments.