Robust Unsupervised Factory Activity Recognition with Body-worn Accelerometer Using Temporal Structure of Multiple Sensor Data Motifs

This paper presents a robust unsupervised method for recognizing factory work using sensor data from body-worn acceleration sensors. In line-production systems, each factory worker repetitively performs a predefined work process with each process consisting of a sequence of operations. Because of the difficulty in collecting labeled sensor data from each factory worker, unsupervised factory activity recognition has been attracting attention in the ubicomp community. However, prior unsupervised factory activity recognition methods can be adversely affected by any outlier activities performed by the workers. In this study, we propose a robust factory activity recognition method that tracks frequent sensor data motifs, which can correspond to particular actions performed by the workers, that appear in each iteration of the work processes. Specifically, this study proposes tracking two types of motifs: period motifs and action motifs, during the unsupervised recognition process. A period motif is a unique data segment that occurs only once in each work period (one iteration of an overall work process). An action motif is a data segment that occurs several times in each work period, corresponding to an action that is performed several times in each period. Tracking multiple period motifs enables us to roughly capture the temporal structure and duration of the work period even when outlier activities occur. Action motifs, which are spread throughout the work period, permit us to precisely detect the start time of each operation. We evaluated the proposed method using sensor data collected from workers in actual factories and achieved state-of-the-art performance.