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Dynamic monitoring for improving worker safety at the workplace: use case from a manufacturing shop floor

Published: 24 June 2015 Publication History

Abstract

Manufacturing is one of ever more important domains for applying IoT and real-time processing. Despite a big trend in optimizing production processes based on the real-time shop floor data (e.g. improving the quality control or the resource consumption), there is a huge need in making the workspaces more ergonomic, user friendly and safe. This is especially relevant for modern manufacturing facilities which are equipped with modern cameras and other personal or environmental monitoring systems. However, the task of personal monitoring in manufacturing is a rather a challenging task from the real-time processing point of view, starting from the quality of data that can be acquired from the monitoring system, till the complexity of the situations that should be considered from the safety point of view (their detection and the reaction on them).
In this paper we present a novel approach for improving worker safety at the workplace in the manufacturing shop floor. The approach is based on a dynamic and scalable real-time processing pipeline derived from Storm architecture that satisfies complex requirements for realizing this challenging task.
The system has been designed and implemented for a manufacturing line in TRW, one of the largest suppliers for the automotive industry. It uses the Kinect-based monitoring system installed in the shop floor and it is part of the risk intervention and communication process, led by safety and prevention managers.
In this paper we present the requirements analysis, the conceptual architecture and the implementation and initial testing.

References

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Jagadish, H. V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J. M., Ramakrishnan, Cyrus R., and Shahabi, C. 2014. Big Data and Its Technical Challenges. Communications of the ACM, Vol. 57 No. 7, Pages 86--94, 10.1145/2611567
[2]
González, A., Lázaro, Ó., Sola, J. 2014. D3.2: FITMAN Trials Business Cases. FITMAN deliverable
[3]
Artikis, A., Etzion, O., Feldman. Z., Fournier, F. 2012. Event processing under uncertainty. In Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems (DEBS '12). ACM, New York, NY, USA, 32--43. DOI=10.1145/2335484.2335488
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Stojanovic, L., et al., 2014. D1.3: Conceptual Architecture and data formats and protocols. ProaSense deliverable
[5]
Fischer, K., et al. 2015. D12.2: Extended Smart Factory trials first experimentation. FITMAN deliverable

Cited By

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  • (2024)A Systematic Review of Biometric Monitoring in the Workplace: Analyzing Socio-technical Harms in Development, Deployment and UseProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658945(920-932)Online publication date: 3-Jun-2024
  • (2021)Internet of Things and occupational well-being in industry 4.0Computers and Industrial Engineering10.1016/j.cie.2021.107670161:COnline publication date: 1-Nov-2021
  • (2021)Systematic review of Kinect-based solutions for physical risk assessment in manual materials handling in industrial and laboratory environmentsComputers & Industrial Engineering10.1016/j.cie.2021.107660162(107660)Online publication date: Dec-2021
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  1. Dynamic monitoring for improving worker safety at the workplace: use case from a manufacturing shop floor

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    cover image ACM Conferences
    DEBS '15: Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems
    June 2015
    385 pages
    ISBN:9781450332866
    DOI:10.1145/2675743
    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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    New York, NY, United States

    Publication History

    Published: 24 June 2015

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    Author Tags

    1. big data
    2. manufacturing
    3. real-time processing
    4. storm architecture
    5. worker safety

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    • European Commission

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    DEBS '15

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    Overall Acceptance Rate 145 of 583 submissions, 25%

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    View all
    • (2024)A Systematic Review of Biometric Monitoring in the Workplace: Analyzing Socio-technical Harms in Development, Deployment and UseProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658945(920-932)Online publication date: 3-Jun-2024
    • (2021)Internet of Things and occupational well-being in industry 4.0Computers and Industrial Engineering10.1016/j.cie.2021.107670161:COnline publication date: 1-Nov-2021
    • (2021)Systematic review of Kinect-based solutions for physical risk assessment in manual materials handling in industrial and laboratory environmentsComputers & Industrial Engineering10.1016/j.cie.2021.107660162(107660)Online publication date: Dec-2021
    • (2019)Development of a Curriculum to Teach Electronics to Workers of Garments Industry in Bangladesh: A Visual Literacy Approach2019 IEEE International Conference on Engineering, Technology and Education (TALE)10.1109/TALE48000.2019.9225873(1-8)Online publication date: Dec-2019
    • (2017)An Event-based Capture-and-Compare Approach to Support the Evolution of Systems of SystemsProceedings of the 11th ACM International Conference on Distributed and Event-based Systems10.1145/3093742.3093909(261-270)Online publication date: 8-Jun-2017

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