Teamwork efficiency and safety are inextricably linked. The capability of having online insights and access to objective information regarding cognitive and emotional aspects of the team members using neurophysiological measures (brain activity, skin conductance, heart rate) will endow a tool which can support Instructors during the assessment and management of teams. Such neurophysiological measures can be seen as the physical interface that will enable for gathering insights about all the aspects relating to Human Factors (HFs) of the operators. The study aimed at developing and validating a methodology able to objectively measure the teamwork dynamics and efficiency. This objective has been performed in a real surgery-related context. A data-driven approach based on machine - learning (ML) and multivariate autoregressive (MVAR) models has been employed to develop the Neurometrics - based teamwork model. Such a model considered the co-variations both within each HF (e.g., Low vs High Stress) and between different HFs (e.g., Attention vs Workload) to consider their simultaneous coexistence. The results of this preliminary study demonstrated that it is possible to quantify the teamwork of operators while dealing with real tasks and endow additional information for a more accurate teams assessment and management.

Teamwork objective assessment through neurophysiological data analysis: a preliminary multimodal data validation / Borghini, Gianluca; Ronca, Vincenzo; Aricò, Pietro; Di Flumeri, Gianluca; Giorgi, Andrea; Bonelli, Stefano; Moens, Laura; Castagneto Gissey, Lidia; Irene Bellini, Maria; Casella, Giovanni; Babiloni, Fabio. - 102:(2023), pp. 103-112. (Intervento presentato al convegno 2023 International Conference on Applied Human Factors and Ergonomics tenutosi a San Francisco; USA) [10.54941/ahfe1003010].

Teamwork objective assessment through neurophysiological data analysis: a preliminary multimodal data validation

Borghini, Gianluca
;
Ronca, Vincenzo
;
Aricò, Pietro
;
Di Flumeri, Gianluca
;
Castagneto Gissey, Lidia
;
Irene Bellini, Maria
;
Casella, Giovanni
;
Babiloni, Fabio
2023

Abstract

Teamwork efficiency and safety are inextricably linked. The capability of having online insights and access to objective information regarding cognitive and emotional aspects of the team members using neurophysiological measures (brain activity, skin conductance, heart rate) will endow a tool which can support Instructors during the assessment and management of teams. Such neurophysiological measures can be seen as the physical interface that will enable for gathering insights about all the aspects relating to Human Factors (HFs) of the operators. The study aimed at developing and validating a methodology able to objectively measure the teamwork dynamics and efficiency. This objective has been performed in a real surgery-related context. A data-driven approach based on machine - learning (ML) and multivariate autoregressive (MVAR) models has been employed to develop the Neurometrics - based teamwork model. Such a model considered the co-variations both within each HF (e.g., Low vs High Stress) and between different HFs (e.g., Attention vs Workload) to consider their simultaneous coexistence. The results of this preliminary study demonstrated that it is possible to quantify the teamwork of operators while dealing with real tasks and endow additional information for a more accurate teams assessment and management.
2023
2023 International Conference on Applied Human Factors and Ergonomics
teamwork assessment; neurophysiological data processing; human factors; neuroergonomics; multimodal data analysis; mutual information; surgery; machine – learning; MVAR
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Teamwork objective assessment through neurophysiological data analysis: a preliminary multimodal data validation / Borghini, Gianluca; Ronca, Vincenzo; Aricò, Pietro; Di Flumeri, Gianluca; Giorgi, Andrea; Bonelli, Stefano; Moens, Laura; Castagneto Gissey, Lidia; Irene Bellini, Maria; Casella, Giovanni; Babiloni, Fabio. - 102:(2023), pp. 103-112. (Intervento presentato al convegno 2023 International Conference on Applied Human Factors and Ergonomics tenutosi a San Francisco; USA) [10.54941/ahfe1003010].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1694711
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