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%.
This paper presents an unsupervised method for recognizing assembly work done by factory workers by using wearable sensor data. Such assembly work is a common part of line production systems and typically involves the factory workers performing a repetitive work process made up of a sequence of manual operations, such as setting a board on a workbench and screwing parts onto the board. This study aims to recognize the starting and ending times for individual operations in such work processes through analysis of sensor data collected from the workers along with analysis of the process instructions that detail and describe the flow of operations for each work process. We propose a particle-filter-based factory activity recognition method that leverages (i) trend changes in the sensor data detected by a nonparametric Bayesian hidden Markov model, (ii) semantic similarities between operations discovered in the process instructions, (iii) sensor-data similarities between consecutive repetitions of individual operations, and (iv) frequent sensor-data patterns (motifs) discovered in the overall assembly work processes. We evaluated the proposed method using sensor data from six workers collected in actual factories, achieving a recognition accuracy of 80% (macro-averaged F-measure).ACM acknowledges that this contribution was authored or co-authored by an employee, contractor, or affiliate of the United States government. As such, the United States government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for government purposes only.
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. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing theory, concepts and paradigms; • Mathematics of computing → Sequential Monte Carlo methods; Time series analysis;
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