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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-25338
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dc.contributor.authorDell'Agnola, Fabio-
dc.contributor.authorJao, Ping-Keng-
dc.contributor.authorArza, Adriana-
dc.contributor.authorChavarriaga, Ricardo-
dc.contributor.authorMillan, Jose Del R.-
dc.contributor.authorFloreano, Dario-
dc.contributor.authorAtienza, David-
dc.date.accessioned2022-07-27T08:07:03Z-
dc.date.available2022-07-27T08:07:03Z-
dc.date.issued2022-
dc.identifier.issn2168-2194de_CH
dc.identifier.issn2168-2208de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/25338-
dc.description.abstractIn 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.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.relation.ispartofIEEE Journal of Biomedical and Health Informaticsde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectCognitive workload monitoringde_CH
dc.subjectSearch and rescue missionde_CH
dc.subjectPhysiological signalsde_CH
dc.subjectMachine learningde_CH
dc.subjectHuman-robot interactionde_CH
dc.subjectWearable systemde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc629: Luftfahrt- und Fahrzeugtechnikde_CH
dc.titleMachine-learning based monitoring of cognitive workload in rescue missions with dronesde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitCentre for Artificial Intelligence (CAI)de_CH
dc.identifier.doi10.1109/JBHI.2022.3186625de_CH
dc.identifier.doi10.21256/zhaw-25338-
dc.identifier.pmid35759604de_CH
zhaw.funding.euNode_CH
zhaw.issue9de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end4762de_CH
zhaw.pages.start4751de_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.volume26de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.funding.snf185543de_CH
zhaw.webfeedDatalabde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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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.


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