Neurophysiological approach for psychological safety: enhancing mental health in human-robot collaboration in smart manufacturing setups using neuroimaging

Arif, A., Zakeri, Z. ORCID: 0000-0003-2588-8360, Omurtag, A. ORCID: 0000-0002-3773-8506, Breedon, P. ORCID: 0000-0002-1006-0942 and Khalid, A. ORCID: 0000-0001-5270-6599, 2024. Neurophysiological approach for psychological safety: enhancing mental health in human-robot collaboration in smart manufacturing setups using neuroimaging. Information, 15 (10): 640. ISSN 2078-2489

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Abstract

Human-robot collaboration (HRC) has become increasingly prevalent due to innovative advancements in the automation industry, especially in manufacturing setups. Although HRC increases productivity and efficacy, it exposes human workers to psychological stress while interfacing with collaborative robotic systems as robots may not provide visual or auditory cues. It is crucial to comprehend how HRC impacts mental stress in order to enhance occupational safety and well-being. Though academics and industrial interest in HRC is expanding, safety and mental stress problems are still not adequately studied. In particular, human coworkers' cognitive strain during HRC has not been explored well, although being fundamental to sustaining a secure and constructive workplace environment. This study, therefore, aims to monitor the mental stress of factory workers during HRC using behavioural, physiological and subjective measures. Physiological measures, being objective and more authentic, have the potential to replace conventional measures i.e., behavioural and subjective measures, if they demonstrate a good correlation with traditional measures. Two neuroimaging modalities including electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have been used as physiological measures to track neuronal and hemodynamic activity of the brain, respectively. Here, the correlation between physiological data and behavioural and subjective measurements has been ascertained through the implementation of seven different machine learning algorithms. The results imply that the EEG and fNIRS features combined produced the best results for most of the targets. For subjective measures being the target, linear regression has outperformed all other models, whereas tree and ensemble performed the best for predicting the behavioural measures. The outcomes indicate that physiological measures have the potential to be more informative and often substitute other skewed metrics.

Item Type:	Journal article
Publication Title:	Information
Creators:	Arif, A., Zakeri, Z., Omurtag, A., Breedon, P. and Khalid, A.
Publisher:	MDPI
Date:	15 October 2024
Volume:	15
Number:	10
ISSN:	2078-2489
Identifiers:	Number Type 10.3390/info15100640 DOI 2255263 Other
Rights:	© 2024 by the authors. 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/).
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Ward
Date Added:	16 Oct 2024 16:03
Last Modified:	16 Oct 2024 16:03
Related URLs:	Publisher
URI:	https://irep.ntu.ac.uk/id/eprint/52432

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