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Industrial Internet of Things for Assembly Line Worker’s Work Fatigue Recognition

Topics: Analytics, Intelligence and Knowledge Engineering; Artificial Intelligence; Healthcare Services, Health Informatics and Biological Research of the Pandemic; IoT Services and Applications; Machine to Machine Communications ; Sensor Networks, Remote Diagnosis and Development

Authors: Venkata Pabolu 1 ; Divya Shrivastava 1 and Makarand Kulkarni 2

Affiliations: 1 Department of Mechanical Engineering, Shiv Nadar University, UP 201314, India ; 2 Department of Mechanical Engineering, Indian Institute of Technology Bombay, India

Keyword(s): Internet of Things, Machine Learning, Worker’s Work Fatigue, Assembly Line, Sensors.

Abstract: The fourth industrial revolution or Industry 4.0 is based on the Internet of Things (IoT) and other intelligent technologies. IoT is mature enough to make seamless real-time communication between data-grasping sensors and intelligent machines. Recognition and prevention of workers’ work fatigue remain challenging for manufacturing industries. The objective of this research is to develop an IoT-based worker’s work fatigue recognition system to recognize the real-time fatigue status of assembly line workers. A learning-based knowledge model is prepared from the historical worker’s work fatigue status to classify the worker’s work fatigue status (as ‘Yes’ or ‘No’) using the real-time monitoring system. Where a sensor-connected IoT framework is adopted for monitoring the real-time state of an assembly worker. Finally, an intelligent system is proposed to recognize the real-time worker’s fatigue status from the IoT real-time monitored data using the learning-based worker’s work fatigue re cognition model. A use-case illustration is given to demonstrate the research scope for a manual assembly line. (More)

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Paper citation in several formats:
Pabolu, V.; Shrivastava, D. and Kulkarni, M. (2024). Industrial Internet of Things for Assembly Line Worker’s Work Fatigue Recognition. In Proceedings of the 9th International Conference on Internet of Things, Big Data and Security - IoTBDS; ISBN 978-989-758-699-6; ISSN 2184-4976, SciTePress, pages 302-309. DOI: 10.5220/0012726200003705

@conference{iotbds24,
author={Venkata Pabolu. and Divya Shrivastava. and Makarand Kulkarni.},
title={Industrial Internet of Things for Assembly Line Worker’s Work Fatigue Recognition},
booktitle={Proceedings of the 9th International Conference on Internet of Things, Big Data and Security - IoTBDS},
year={2024},
pages={302-309},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012726200003705},
isbn={978-989-758-699-6},
issn={2184-4976},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Internet of Things, Big Data and Security - IoTBDS
TI - Industrial Internet of Things for Assembly Line Worker’s Work Fatigue Recognition
SN - 978-989-758-699-6
IS - 2184-4976
AU - Pabolu, V.
AU - Shrivastava, D.
AU - Kulkarni, M.
PY - 2024
SP - 302
EP - 309
DO - 10.5220/0012726200003705
PB - SciTePress

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