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...
Ausführliche Beschreibung
Autor*in: |
Karmele Lopez-de-Ipina [verfasserIn] Jon Iradi [verfasserIn] Elsa Fernandez [verfasserIn] Pilar M. Calvo [verfasserIn] Damien Salle [verfasserIn] Anujan Poologaindran [verfasserIn] Ivan Villaverde [verfasserIn] Paul Daelman [verfasserIn] Emilio Sanchez [verfasserIn] Catalina Requejo [verfasserIn] John Suckling [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2023 |
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Übergeordnetes Werk: |
In: Sensors - MDPI AG, 2003, 23(2023), 3, p 1170 |
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Übergeordnetes Werk: |
volume:23 ; year:2023 ; number:3, p 1170 |
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DOI / URN: |
10.3390/s23031170 |
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Katalog-ID: |
DOAJ080594980 |
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10.3390/s23031170 doi (DE-627)DOAJ080594980 (DE-599)DOAJ72d7f7eb56914105a149a4c359363152 DE-627 ger DE-627 rakwb eng TP1-1185 Karmele Lopez-de-Ipina verfasserin aut HUMANISE: Human-Inspired Smart Management, towards a Healthy and Safe Industrial Collaborative Robotics 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Cobot Machine Learning risk management human/robot behaviour ageing population workers’ diseases Chemical technology Jon Iradi verfasserin aut Elsa Fernandez verfasserin aut Pilar M. Calvo verfasserin aut Damien Salle verfasserin aut Anujan Poologaindran verfasserin aut Ivan Villaverde verfasserin aut Paul Daelman verfasserin aut Emilio Sanchez verfasserin aut Catalina Requejo verfasserin aut John Suckling verfasserin aut In Sensors MDPI AG, 2003 23(2023), 3, p 1170 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:3, p 1170 https://doi.org/10.3390/s23031170 kostenfrei https://doaj.org/article/72d7f7eb56914105a149a4c359363152 kostenfrei https://www.mdpi.com/1424-8220/23/3/1170 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 3, p 1170 |
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10.3390/s23031170 doi (DE-627)DOAJ080594980 (DE-599)DOAJ72d7f7eb56914105a149a4c359363152 DE-627 ger DE-627 rakwb eng TP1-1185 Karmele Lopez-de-Ipina verfasserin aut HUMANISE: Human-Inspired Smart Management, towards a Healthy and Safe Industrial Collaborative Robotics 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Cobot Machine Learning risk management human/robot behaviour ageing population workers’ diseases Chemical technology Jon Iradi verfasserin aut Elsa Fernandez verfasserin aut Pilar M. Calvo verfasserin aut Damien Salle verfasserin aut Anujan Poologaindran verfasserin aut Ivan Villaverde verfasserin aut Paul Daelman verfasserin aut Emilio Sanchez verfasserin aut Catalina Requejo verfasserin aut John Suckling verfasserin aut In Sensors MDPI AG, 2003 23(2023), 3, p 1170 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:3, p 1170 https://doi.org/10.3390/s23031170 kostenfrei https://doaj.org/article/72d7f7eb56914105a149a4c359363152 kostenfrei https://www.mdpi.com/1424-8220/23/3/1170 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 3, p 1170 |
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10.3390/s23031170 doi (DE-627)DOAJ080594980 (DE-599)DOAJ72d7f7eb56914105a149a4c359363152 DE-627 ger DE-627 rakwb eng TP1-1185 Karmele Lopez-de-Ipina verfasserin aut HUMANISE: Human-Inspired Smart Management, towards a Healthy and Safe Industrial Collaborative Robotics 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Cobot Machine Learning risk management human/robot behaviour ageing population workers’ diseases Chemical technology Jon Iradi verfasserin aut Elsa Fernandez verfasserin aut Pilar M. Calvo verfasserin aut Damien Salle verfasserin aut Anujan Poologaindran verfasserin aut Ivan Villaverde verfasserin aut Paul Daelman verfasserin aut Emilio Sanchez verfasserin aut Catalina Requejo verfasserin aut John Suckling verfasserin aut In Sensors MDPI AG, 2003 23(2023), 3, p 1170 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:3, p 1170 https://doi.org/10.3390/s23031170 kostenfrei https://doaj.org/article/72d7f7eb56914105a149a4c359363152 kostenfrei https://www.mdpi.com/1424-8220/23/3/1170 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 3, p 1170 |
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10.3390/s23031170 doi (DE-627)DOAJ080594980 (DE-599)DOAJ72d7f7eb56914105a149a4c359363152 DE-627 ger DE-627 rakwb eng TP1-1185 Karmele Lopez-de-Ipina verfasserin aut HUMANISE: Human-Inspired Smart Management, towards a Healthy and Safe Industrial Collaborative Robotics 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Cobot Machine Learning risk management human/robot behaviour ageing population workers’ diseases Chemical technology Jon Iradi verfasserin aut Elsa Fernandez verfasserin aut Pilar M. Calvo verfasserin aut Damien Salle verfasserin aut Anujan Poologaindran verfasserin aut Ivan Villaverde verfasserin aut Paul Daelman verfasserin aut Emilio Sanchez verfasserin aut Catalina Requejo verfasserin aut John Suckling verfasserin aut In Sensors MDPI AG, 2003 23(2023), 3, p 1170 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:23 year:2023 number:3, p 1170 https://doi.org/10.3390/s23031170 kostenfrei https://doaj.org/article/72d7f7eb56914105a149a4c359363152 kostenfrei https://www.mdpi.com/1424-8220/23/3/1170 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 3, p 1170 |
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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. |
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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. |
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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. |
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