Among the main features of Industry 4.0, digitization and the evolution of the human-machine interaction occupy a central role. These concepts are transferring even in the health domain, moving toward Healthcare 4.0. The new concept of Industry 5.0 further promotes the human-centric perspective focusing on the consideration of human factors. In this context, training for workers, both in the industry and in the healthcare sectors, needs to be strongly human-centred to be efficient and effective. This paper refers to simulation-based training and aims to provide a transdisciplinary framework for the simulation assessment from the learners’ perspective. The final scope is to outline a set of data-driven guidelines for the simulation optimization and redesign, throughout a human-centred approach, aiming to improve the workers’ performance and the overall learning process, considering the physical, cognitive, and emotional conditions. The proposed method is suitable for each kind of training (both traditional and with the use of virtual reality/augmented reality systems) and relevant for every sector. Two different use cases are presented, respectively referring to the healthcare and industry fields, proposing a unique assessment protocol. The healthcare use case considered the low-fidelity simulation of lumbar puncture, while the industrial use case referred to the replacement of the engine oil filter on tractors. Although the great differences between the content of the use cases, the results obtained about performance as well as cognitive and emotional states are close enough to define a common set of guidelines to redesign and optimize the simulation-based training.

BRUNZINI, A., PERUZZINI, M., BARBADORO, P. (2023). Human-centred data-driven redesign of simulation-based training: a qualitative study applied on two use cases of the healthcare and industrial domains. JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION, 35, 1-11 [10.1016/j.jii.2023.100505].

Human-centred data-driven redesign of simulation-based training: a qualitative study applied on two use cases of the healthcare and industrial domains

PERUZZINI, Margherita;
2023

Abstract

Among the main features of Industry 4.0, digitization and the evolution of the human-machine interaction occupy a central role. These concepts are transferring even in the health domain, moving toward Healthcare 4.0. The new concept of Industry 5.0 further promotes the human-centric perspective focusing on the consideration of human factors. In this context, training for workers, both in the industry and in the healthcare sectors, needs to be strongly human-centred to be efficient and effective. This paper refers to simulation-based training and aims to provide a transdisciplinary framework for the simulation assessment from the learners’ perspective. The final scope is to outline a set of data-driven guidelines for the simulation optimization and redesign, throughout a human-centred approach, aiming to improve the workers’ performance and the overall learning process, considering the physical, cognitive, and emotional conditions. The proposed method is suitable for each kind of training (both traditional and with the use of virtual reality/augmented reality systems) and relevant for every sector. Two different use cases are presented, respectively referring to the healthcare and industry fields, proposing a unique assessment protocol. The healthcare use case considered the low-fidelity simulation of lumbar puncture, while the industrial use case referred to the replacement of the engine oil filter on tractors. Although the great differences between the content of the use cases, the results obtained about performance as well as cognitive and emotional states are close enough to define a common set of guidelines to redesign and optimize the simulation-based training.
2023
BRUNZINI, A., PERUZZINI, M., BARBADORO, P. (2023). Human-centred data-driven redesign of simulation-based training: a qualitative study applied on two use cases of the healthcare and industrial domains. JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION, 35, 1-11 [10.1016/j.jii.2023.100505].
BRUNZINI, Agnese; PERUZZINI, Margherita; BARBADORO, Pamela
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/949298
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