Toward Practical Factory Activity Recognition: Unsupervised Understanding Of Repetitive Assembly Work In A Factory

In a line production system of a factory, a worker repetitively performs predefined operation processes. This paper tries to recognize work by factory workers in an unsupervised manner. Specifically, we propose an unsupervised measurement method for estimating lead time (duration) of each period of an operation process using a wrist-worn accelerometer because the lead time greatly affects productivity of the line production system. Our proposed method automatically finds a frequent sensor data segment as a "motif" that occurs once in each operation period using only prior knowledge about predefined standard lead time of the operation process, and uses the occurrence intervals of the motif to estimate the lead time. We evaluated Our method using real factory data and the estimation error was only about 3.5%.