Unsupervised Work Behavior Analysis Using Hierarchical Probabilistic Segmentation

Workers' behavior should be analyzed to improve their efficiency and that of cell production systems. However, traditional approaches and supervised learning methods are time-consuming and require abundant labeled data, respectively. Therefore, this study proposes a novel unsupervised behavior analysis model, namely the Gaussian process-hidden semi-Markov model-based behavior analyzer (GP-HSMM-BA), which is a two-layered model consisting of GP-HSMM and HSMM. This model can efficiently capture the motion element and unit motion, which are the smallest unit motions, and their compositional motions. The first layer is GP-HSMM, which divides and classifies continuous motion into motion elements. The second layer is HSMM, which divides and classifies the sequence of motion elements into unit motions. Furthermore, more accurate motion elements and unit motions can be obtained by training the two models mutually. The proposed model was applied to a cell production environment, indicating that motion elements can be extracted more accurately using GP-HSMM-BA. Additionally, an analysis using the trained parameters and motion elements indicated that habituation can be quantified and behavior changes in repeated tasks are easily understood.