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Unsupervised Factory Activity Recognition with Wearable Sensors Using Process Instruction Information

Authors:

Joseph Korpela,

Takuya Maekawa,

Yasuo NamiokaAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 3, Issue 2

Article No.: 60, Pages 1 - 23

https://doi.org/10.1145/3328931

Published: 21 June 2019 Publication History

Abstract

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).

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Cited By

Arakawa RLehman JGoel M(2024)PrISM-Q&A: Step-Aware Voice Assistant on a Smartwatch Enabled by Multimodal Procedure Tracking and Large Language ModelsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997598:4(1-26)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699759
Hong ZLi ZZhong SLyu WWang HDing YHe TZhang D(2024)CrossHAR: Generalizing Cross-dataset Human Activity Recognition via Hierarchical Self-Supervised PretrainingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36595978:2(1-26)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659597
Xia QMaekawa TMorales JHara TOshima HFukuda MNamioka Y(2024)Preliminary Investigation of SSL for Complex Work Activity Recognition in Industrial Domain via MoIL2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)10.1109/PerComWorkshops59983.2024.10503195(465-468)Online publication date: 11-Mar-2024
https://doi.org/10.1109/PerComWorkshops59983.2024.10503195
Show More Cited By

Index Terms

Unsupervised Factory Activity Recognition with Wearable Sensors Using Process Instruction Information
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing theory, concepts and paradigms
2. Mathematics of computing
  1. Probability and statistics
    1. Probabilistic reasoning algorithms
      1. Sequential Monte Carlo methods
    2. Statistical paradigms
      1. Time series analysis

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Published In

cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 3, Issue 2

June 2019

802 pages

EISSN:2474-9567

DOI:10.1145/3341982

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Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 June 2019

Accepted: 01 June 2019

Received: 01 February 2019

Published in IMWUT Volume 3, Issue 2

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Cited By

Arakawa RLehman JGoel M(2024)PrISM-Q&A: Step-Aware Voice Assistant on a Smartwatch Enabled by Multimodal Procedure Tracking and Large Language ModelsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997598:4(1-26)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699759
Hong ZLi ZZhong SLyu WWang HDing YHe TZhang D(2024)CrossHAR: Generalizing Cross-dataset Human Activity Recognition via Hierarchical Self-Supervised PretrainingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36595978:2(1-26)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659597
Xia QMaekawa TMorales JHara TOshima HFukuda MNamioka Y(2024)Preliminary Investigation of SSL for Complex Work Activity Recognition in Industrial Domain via MoIL2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)10.1109/PerComWorkshops59983.2024.10503195(465-468)Online publication date: 11-Mar-2024
https://doi.org/10.1109/PerComWorkshops59983.2024.10503195
Yoshimura NMorales JMaekawa THara T(2024)OpenPack: A Large-Scale Dataset for Recognizing Packaging Works in IoT-Enabled Logistic Environments2024 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PerCom59722.2024.10494448(90-97)Online publication date: 11-Mar-2024
https://doi.org/10.1109/PerCom59722.2024.10494448
Suh SRey VBian SHuang YRožanec JGhinani HZhou BLukowicz P(2024)Worker Activity Recognition in Manufacturing Line Using Near-Body Electric FieldIEEE Internet of Things Journal10.1109/JIOT.2023.333037211:7(11554-11565)Online publication date: 1-Apr-2024
https://doi.org/10.1109/JIOT.2023.3330372
Li YZhang ZYue XZheng L(2024)An unsupervised embedding method based on streaming videos for process monitoring in repetitive production systemsIISE Transactions10.1080/24725854.2024.2386415(1-22)Online publication date: 6-Aug-2024
https://doi.org/10.1080/24725854.2024.2386415
Selvaraj VNagaraj AWhiffen BMin S(2024)Development of a wireless smart sensor system and case study on lifting risk assessmentManufacturing Letters10.1016/j.mfglet.2024.09.02741(229-240)Online publication date: Oct-2024
https://doi.org/10.1016/j.mfglet.2024.09.027
Wang ALiu HZheng CChen HChang C(2024)Fusion of kinematic and physiological sensors for hand gesture recognitionMultimedia Tools and Applications10.1007/s11042-024-18283-z83:26(68013-68040)Online publication date: 29-Jan-2024
https://doi.org/10.1007/s11042-024-18283-z
Selvaraj VMin S(2023)AI-assisted Monitoring of Human-centered Assembly: A Comprehensive ReviewInternational Journal of Precision Engineering and Manufacturing-Smart Technology10.57062/ijpem-st.2023.00731:2(201-218)Online publication date: 1-Jul-2023
https://doi.org/10.57062/ijpem-st.2023.0073
Manouchehri NBouguila N(2023)Human Activity Recognition with an HMM-Based Generative ModelSensors10.3390/s2303139023:3(1390)Online publication date: 26-Jan-2023
https://doi.org/10.3390/s23031390
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