Bitte benutzen Sie diese Kennung, um auf die Ressource zu verweisen:
https://doi.org/10.21256/zhaw-25338| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Machine-learning based monitoring of cognitive workload in rescue missions with drones |
| Autor/-in: | 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 |
| Erschienen in: | IEEE Journal of Biomedical and Health Informatics |
| Band(Heft): | 26 |
| Heft: | 9 |
| Seite(n): | 4751 |
| Seiten bis: | 4762 |
| Erscheinungsdatum: | 2022 |
| Verlag / Hrsg. Institution: | IEEE |
| ISSN: | 2168-2194 2168-2208 |
| Sprache: | Englisch |
| Schlagwörter: | Cognitive workload monitoring; Search and rescue mission; Physiological signals; Machine learning; Human-robot interaction; Wearable system |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren 629: Luftfahrt- und Fahrzeugtechnik |
| Zusammenfassung: | 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 |
| Volltext Version: | Akzeptierte Version |
| Lizenz (gemäss Verlagsvertrag): | CC BY 4.0: Namensnennung 4.0 International |
| Departement: | School of Engineering |
| Organisationseinheit: | Centre for Artificial Intelligence (CAI) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2022_DellAgnola-etal_Machine-learning-based-monitoring-cognitive-workload.pdf | Accepted Version | 990.57 kB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
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.
Alle Ressourcen in diesem Repository sind urheberrechtlich geschützt, soweit nicht anderweitig angezeigt.