HUMANISE: Human-inspired smart management, towards a healthy and safe industrial collaborative robotics
Keywords: 
Cobot
Industrial health and safety
Machine Learning
Risk management
Human
Robot behaviour
Ageing population
Workers' diseases
Issue Date: 
19-Jan-2023
Publisher: 
MDPI
ISSN: 
1424-8220
Note: 
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Citation: 
Lopez-de-Ipina, K., Iradi, J., Fernandez, E., Calvo, P. M., Salle, D., Poologaindran, A., ... & Suckling, J. (2023). "HUMANISE: Human-Inspired Smart Management, towards a Healthy and Safe Industrial Collaborative Robotics". Sensors, 23(3), 1170.
Abstract
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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