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HUMANISE: Human-Inspired Smart Management, towards a Healthy and Safe Industrial Collaborative Robotics
The workplace is evolving towards scenarios where humans are acquiring a more active and dynamic role alongside increasingly intelligent machines. Moreover, the active population is ageing and consequently emerging risks could appear due to health disorders of workers, which requires intelligent int...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920065/ https://www.ncbi.nlm.nih.gov/pubmed/36772209 http://dx.doi.org/10.3390/s23031170 |
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author | Lopez-de-Ipina, Karmele Iradi, Jon Fernandez, Elsa Calvo, Pilar M. Salle, Damien Poologaindran, Anujan Villaverde, Ivan Daelman, Paul Sanchez, Emilio Requejo, Catalina Suckling, John |
author_facet | Lopez-de-Ipina, Karmele Iradi, Jon Fernandez, Elsa Calvo, Pilar M. Salle, Damien Poologaindran, Anujan Villaverde, Ivan Daelman, Paul Sanchez, Emilio Requejo, Catalina Suckling, John |
author_sort | Lopez-de-Ipina, Karmele |
collection | PubMed |
description | The workplace is evolving towards scenarios where humans are acquiring a more active and dynamic role alongside increasingly intelligent machines. Moreover, the active population is ageing and consequently emerging risks could appear due to health disorders of workers, which requires intelligent intervention both for production management and workers’ support. In this sense, the innovative and smart systems oriented towards monitoring and regulating workers’ well-being will become essential. This work presents HUMANISE, a novel proposal of an intelligent system for risk management, oriented to workers suffering from disease conditions. The developed support system is based on Computer Vision, Machine Learning and Intelligent Agents. Results: The system was applied to a two-arm Cobot scenario during a Learning from Demonstration task for collaborative parts transportation, where risk management is critical. In this environment with a worker suffering from a mental disorder, safety is successfully controlled by means of human/robot coordination, and risk levels are managed through the integration of human/robot behaviour models and worker’s models based on the workplace model of the World Health Organization. The results show a promising real-time support tool to coordinate and monitoring these scenarios by integrating workers’ health information towards a successful risk management strategy for safe industrial Cobot environments. |
format | Online Article Text |
id | pubmed-9920065 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99200652023-02-12 HUMANISE: Human-Inspired Smart Management, towards a Healthy and Safe Industrial Collaborative Robotics Lopez-de-Ipina, Karmele Iradi, Jon Fernandez, Elsa Calvo, Pilar M. Salle, Damien Poologaindran, Anujan Villaverde, Ivan Daelman, Paul Sanchez, Emilio Requejo, Catalina Suckling, John Sensors (Basel) Article The workplace is evolving towards scenarios where humans are acquiring a more active and dynamic role alongside increasingly intelligent machines. Moreover, the active population is ageing and consequently emerging risks could appear due to health disorders of workers, which requires intelligent intervention both for production management and workers’ support. In this sense, the innovative and smart systems oriented towards monitoring and regulating workers’ well-being will become essential. This work presents HUMANISE, a novel proposal of an intelligent system for risk management, oriented to workers suffering from disease conditions. The developed support system is based on Computer Vision, Machine Learning and Intelligent Agents. Results: The system was applied to a two-arm Cobot scenario during a Learning from Demonstration task for collaborative parts transportation, where risk management is critical. In this environment with a worker suffering from a mental disorder, safety is successfully controlled by means of human/robot coordination, and risk levels are managed through the integration of human/robot behaviour models and worker’s models based on the workplace model of the World Health Organization. The results show a promising real-time support tool to coordinate and monitoring these scenarios by integrating workers’ health information towards a successful risk management strategy for safe industrial Cobot environments. MDPI 2023-01-19 /pmc/articles/PMC9920065/ /pubmed/36772209 http://dx.doi.org/10.3390/s23031170 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lopez-de-Ipina, Karmele Iradi, Jon Fernandez, Elsa Calvo, Pilar M. Salle, Damien Poologaindran, Anujan Villaverde, Ivan Daelman, Paul Sanchez, Emilio Requejo, Catalina Suckling, John HUMANISE: Human-Inspired Smart Management, towards a Healthy and Safe Industrial Collaborative Robotics |
title | HUMANISE: Human-Inspired Smart Management, towards a Healthy and Safe Industrial Collaborative Robotics |
title_full | HUMANISE: Human-Inspired Smart Management, towards a Healthy and Safe Industrial Collaborative Robotics |
title_fullStr | HUMANISE: Human-Inspired Smart Management, towards a Healthy and Safe Industrial Collaborative Robotics |
title_full_unstemmed | HUMANISE: Human-Inspired Smart Management, towards a Healthy and Safe Industrial Collaborative Robotics |
title_short | HUMANISE: Human-Inspired Smart Management, towards a Healthy and Safe Industrial Collaborative Robotics |
title_sort | humanise: human-inspired smart management, towards a healthy and safe industrial collaborative robotics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920065/ https://www.ncbi.nlm.nih.gov/pubmed/36772209 http://dx.doi.org/10.3390/s23031170 |
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