Visual analysis of fatigue in Industry 4.0
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http://hdl.handle.net/10045/138963
Título: | Visual analysis of fatigue in Industry 4.0 |
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Autor/es: | Alfavo-Viquez, David | Zamora Hernández, Mauricio Andrés | Azorin-Lopez, Jorge | Garcia-Rodriguez, Jose |
Grupo/s de investigación o GITE: | Arquitecturas Inteligentes Aplicadas (AIA) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Tecnología Informática y Computación |
Palabras clave: | Automatic control | Computer Vision | Deep Learning | Fatigue recognition |
Fecha de publicación: | 2-dic-2023 |
Editor: | Springer Nature |
Cita bibliográfica: | The International Journal of Advanced Manufacturing Technology. 2023. https://doi.org/10.1007/s00170-023-12506-7 |
Resumen: | The performance of manufacturing operations relies heavily on the operators’ performance. When operators begin to exhibit signs of fatigue, both their individual performance and the overall performance of the manufacturing plant tend to decline. This research presents a methodology for analyzing fatigue in assembly operations, considering indicators such as the EAR (Eye Aspect Ratio) indicator, operator pose, and elapsed operating time. To facilitate the analysis, a dataset of assembly operations was generated and recorded from three different perspectives: frontal, lateral, and top views. The top view enables the analysis of the operator’s face and posture to identify hand positions. By labeling the actions in our dataset, we train a deep learning system to recognize the sequence of operator actions required to complete the operation. Additionally, we propose a model for determining the level of fatigue by processing multimodal information acquired from various sources, including eye blink rate, operator pose, and task duration during assembly operations. |
Patrocinador/es: | Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. “A way of making Europe” European Regional Development Fund (ERDF) and MCIN/AEI/10.13039/501100011033 for supporting this work under the MoDeaAS project (grant PID2019-104818RB-I00). |
URI: | http://hdl.handle.net/10045/138963 |
ISSN: | 0268-3768 (Print) | 1433-3015 (Online) |
DOI: | 10.1007/s00170-023-12506-7 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1007/s00170-023-12506-7 |
Aparece en las colecciones: | INV - AIA - Artículos de Revistas |
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Archivo | Descripción | Tamaño | Formato | |
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Alfavo-Viquez_etal_2023_IntJAdvManufTechnol.pdf | 4,19 MB | Adobe PDF | Abrir Vista previa | |
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