Machine-learning based monitoring of cognitive workload in rescue missions with drones

Dell'Agnola, Fabio; Jao, Ping-Keng; Arza, Adriana; Chavarriaga, Ricardo; Millan, Jose Del R.; Floreano, Dario; Atienza, David

doi:10.1109/JBHI.2022.3186625

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-25338

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Machine-learning based monitoring of cognitive workload in rescue missions with drones
Authors:	Dell'Agnola, Fabio Jao, Ping-Keng Arza, Adriana Chavarriaga, Ricardo Millan, Jose Del R. Floreano, Dario Atienza, David
et. al:	No
DOI:	10.1109/JBHI.2022.3186625 10.21256/zhaw-25338
Published in:	IEEE Journal of Biomedical and Health Informatics
Volume(Issue):	26
Issue:	9
Page(s):	4751
Pages to:	4762
Issue Date:	2022
Publisher / Ed. Institution:	IEEE
ISSN:	2168-2194 2168-2208
Language:	English
Subjects:	Cognitive workload monitoring; Search and rescue mission; Physiological signals; Machine learning; Human-robot interaction; Wearable system
Subject (DDC):	006: Special computer methods 629: Aeronautical, automotive engineering
Abstract:	In search and rescue missions, drone operations are challenging and cognitively demanding. High levels of cognitive workload can affect rescuers' performance, leading to failure with catastrophic outcomes. To face this problem, we propose a machine learning algorithm for real-time cognitive workload monitoring to understand if a search and rescue operator has to be replaced or if more resources are required. Our multimodal cognitive workload monitoring model combines the information of 25 features extracted from physiological signals, such as respiration, electrocardiogram, photoplethysmogram, and skin temperature, acquired in a noninvasive way. To reduce both subject and day inter-variability of the signals, we explore different feature normalization techniques, and introduce a novel weighted-learning method based on support vector machines suitable for subject-specific optimizations. On an unseen test set acquired from 34 volunteers, our proposed subject-specific model is able to distinguish between low and high cognitive workloads with an average accuracy of 87.3% and 91.2% while controlling a drone simulator using both a traditional controller and a new-generation controller, respectively.
URI:	https://digitalcollection.zhaw.ch/handle/11475/25338
Fulltext version:	Accepted version
License (according to publishing contract):	CC BY 4.0: Attribution 4.0 International
Departement:	School of Engineering
Organisational Unit:	Centre for Artificial Intelligence (CAI)
Appears in collections:	Publikationen School of Engineering

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Dell’Agnola, F., Jao, P.-K., Arza, A., Chavarriaga, R., Millan, J. D. R., Floreano, D., & Atienza, D. (2022). Machine-learning based monitoring of cognitive workload in rescue missions with drones. IEEE Journal of Biomedical and Health Informatics, 26(9), 4751–4762. https://doi.org/10.1109/JBHI.2022.3186625

Dell’Agnola, F. et al. (2022) ‘Machine-learning based monitoring of cognitive workload in rescue missions with drones’, IEEE Journal of Biomedical and Health Informatics, 26(9), pp. 4751–4762. Available at: https://doi.org/10.1109/JBHI.2022.3186625.

F. Dell’Agnola et al., “Machine-learning based monitoring of cognitive workload in rescue missions with drones,” IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 9, pp. 4751–4762, 2022, doi: 10.1109/JBHI.2022.3186625.

DELL’AGNOLA, Fabio, Ping-Keng JAO, Adriana ARZA, Ricardo CHAVARRIAGA, Jose Del R. MILLAN, Dario FLOREANO und David ATIENZA, 2022. Machine-learning based monitoring of cognitive workload in rescue missions with drones. IEEE Journal of Biomedical and Health Informatics. 2022. Bd. 26, Nr. 9, S. 4751–4762. DOI 10.1109/JBHI.2022.3186625

Dell’Agnola, Fabio, Ping-Keng Jao, Adriana Arza, Ricardo Chavarriaga, Jose Del R. Millan, Dario Floreano, and David Atienza. 2022. “Machine-Learning Based Monitoring of Cognitive Workload in Rescue Missions with Drones.” IEEE Journal of Biomedical and Health Informatics 26 (9): 4751–62. https://doi.org/10.1109/JBHI.2022.3186625.

Dell’Agnola, Fabio, et al. “Machine-Learning Based Monitoring of Cognitive Workload in Rescue Missions with Drones.” IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 9, 2022, pp. 4751–62, https://doi.org/10.1109/JBHI.2022.3186625.