Robust Unsupervised Factory Activity Recognition with Body-worn Accelerometer Using Temporal Structure of Multiple Sensor Data Motifs
Robust Unsupervised Factory Activity Recognition with Body-worn Accelerometer Using Temporal Structure of Multiple Sensor Data Motifs
复制标题
使用多个传感器数据基序的时间结构，通过穿戴式加速度计进行鲁棒的无监督工厂活动识别
DOI：
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复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
T. Maekawa
中科院分区：
文献类型：
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作者：
Qingxin Xia;Joseph Korpela;Y. Namioka;T. Maekawa
本文提出了一种鲁棒的无监督方法来识别工厂工作，该方法使用了来自体载加速度传感器的传感器数据。在流水线生产系统中，每个工厂工人重复执行一个预定义的工作过程，每个过程由一系列操作组成。由于难以从每个工厂工人身上收集有标签的传感器数据，无监督的工厂活动识别已经引起了ubicomp社区的关注。然而，先前的无监督工厂活动识别方法可能受到工人进行的任何异常活动的不利影响。在本研究中，我们提出了一种鲁棒的工厂活动识别方法，该方法可以跟踪频繁的传感器数据基，这些数据基可以对应于工人执行的特定动作，这些动作出现在工作流程的每次迭代中。具体而言，本研究提出在无监督识别过程中跟踪两种类型的母题：时期母题和动作母题。周期主题是在每个工作周期（整个工作过程的一次迭代）中只出现一次的唯一数据段。动作母题是在每个工作周期中出现多次的数据段，对应于在每个工作周期中执行多次的动作。跟踪多个周期主题使我们能够大致捕获工作周期的时间结构和持续时间，即使在异常活动发生时也是如此。动作图案分布在整个工作期间，使我们能够精确地检测到每个操作的开始时间。我们使用从实际工厂工人那里收集的传感器数据来评估所提出的方法，并取得了最先进的性能。
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.
影响因子：
6.1
作者：
Kristina Yordanova
通讯作者：
Kristina Yordanova
影响因子：
3.7
作者：
Krüger F;Nyolt M;Yordanova K;Hein A;Kirste T
通讯作者：
Kirste T
DOI：
10.1145/2971763.2971764
发表时间：
2016-09
期刊：
Proceedings of the 2016 ACM International Symposium on Wearable Computers
影响因子：
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作者：
Francisco Javier Ordonez;D. Roggen
通讯作者：
Francisco Javier Ordonez;D. Roggen