¿Puede una monitorización fisiológica detectar carga mental en operadores UAV?

Autores/as

DOI:

https://doi.org/10.17979/ja-cea.2026.47.13714

Palabras clave:

Modelado del rendimiento humano, UAVs, Teleoperación, Fusión de datos sensoriales, Validación de modelos

Resumen

La operación de vehículos aéreos no tripulados (UAV) combina supervisión visual, control manual y toma de decisiones; por lo tanto, la carga mental del operador es crítica para la seguridad. Este trabajo presenta una validación preliminar de un protocolo intrasujeto de medidas repetidas para estudiar dicha carga durante vuelos reales en interior. Se registraron 10 sesiones con un operador certificado UAS A1/A3, incluyendo bloques de 2 min bajo tres condiciones: Estacionario, Fácil y Difícil. La adquisición combinó telemetría del UAV, señales fisiológicas de muñeca y vídeo facial, sincronizados fuera de línea. Los resultados muestran una progresión coherente de la carga percibida y de la exigencia cinemática. Además, una línea base personalizada basada en EDA y PPG discriminó entre Estacionario y Difícil en sesiones no vistas, apoyando la viabilidad del este enfoque.

Referencias

Anders, C., Bhaduri, I., Arnrich, B., 2025. Generalised machine learning models outperform personalised models for cognitive load classification in real-life settings. Frontiers in Digital Health 7, 1650085. DOI: 10.3389/fdgth.2025.1650085

Bainbridge, L., 1983. Ironies of automation. In: Analysis, design and evaluation of man–machine systems. Elsevier, pp. 129–135. DOI: 10.1016/B978-0-08-029348-6.50026-9

Bolpagni, M., Pardini, S., Dianti, M., Gabrielli, S., 2024. Personalized stress detection using biosignals from wearables: A scoping review. Sensors 24 (10), 3221. DOI: 10.3390/s24103221

Das Chakladar, D., Roy, P. P., 2024. Cognitive workload estimation using physiological measures: a review. Cognitive Neurodynamics 18 (4), 1445–1465. DOI: 10.1007/s11571-023-10051-3

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. DOI: 10.1109/JBHI.2022.3186625

European Parliament, Council of the European Union, Apr. 2016. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 (General Data Protection Regulation). OJ L 119, 4 May 2016, pp. 1–88. URL: https://eur-lex.europa.eu/eli/reg/2016/679/oj/eng

Fisher, A. J., Medaglia, J. D., Jeronimus, B. F., 2018. Lack of group-to-individual generalizability is a threat to human subjects research. Proceedings of the National Academy of Sciences 115 (27), E6106–E6115. DOI: 10.1073/pnas.1711978115

Giorgi, A., Ronca, V., Vozzi, A., Sciaraffa, N., Di Florio, A., Tamborra, L., Simonetti, I., Aricò, P., Di Flumeri, G., Rossi, D., et al., 2021. Wearable technologies for mental workload, stress, and emotional state assessment during working-like tasks: A comparison with laboratory technologies. Sensors 21 (7), 2332. DOI: 10.3390/s21072332

Gruden, T., Stojmenova, K., Sodnik, J., Jakus, G., 2019. Assessing drivers’ physiological responses using consumer grade devices. Applied Sciences 9 (24), 5353. DOI: 10.3390/app9245353

Hart, S. G., Staveland, L. E., 1988. Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In: Advances in psychology. Vol. 52. Elsevier, pp. 139–183. DOI: 10.1016/S0166-4115(08)62386-9

Kostenko, A., Rauffet, P., Coppin, G., 2022. Supervised classification of operator functional state based on physiological data: Application to drones swarm piloting. Frontiers in Psychology 12, 770000. DOI: 10.3389/fpsyg.2021.770000

Lillie, E. O., Patay, B., Diamant, J., Issell, B., Topol, E. J., Schork, N. J., 2011. The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? Personalized Medicine 8 (2), 161–173. DOI: 10.2217/pme.11.7

Luzzani, G., Buraioli, I., Guglieri, G., Demarchi, D., 2024. EDA, PPG and skin temperature as predictive signals for mental failure by a statistical analysis on stress and mental workload. IEEE Open Journal of Engineering in Medicine and Biology 6, 248–255. DOI: 10.1109/OJEMB.2024.3515473

Maciejewska, M., 2023. UAV operators’ gaze behavior and workload in simulation flights—a preliminary study. Transportation Research Procedia 75, 134–141. DOI: 10.1016/j.trpro.2023.12.016

Parasuraman, R., Riley, V., 1997. Humans and automation: Use, misuse, disuse, abuse. Human Factors 39 (2), 230–253. DOI: 10.1518/001872097778543886

Rauffet, P., Botzer, A., Kostenko, A., Chauvin, C., Coppin, G., et al., 2016. Eye activity measures as indicators of drone operators’ workload and task completion strategies. In: Proc. Hum. Factors Ergonom. Soc. Eur. Ch., pp. 211–227.

Romano, J., Kromrey, J. D., Coraggio, J., Skowronek, J., 2006. Appropriate statistics for ordinal level data: Should we really be using t-test and cohen’sd for evaluating group differences on the nsse and other surveys. In: annual meeting of the Florida Association of Institutional Research. Vol. 177.

Russo, A. C., Junior, M. C., Villani, E., 2024. Eye-tracking analysis to assess the mental load of unmanned aerial system operators: systematic review and future directions. The Aeronautical Journal, 1–30. DOI: 10.1017/aer.2024.122

Singh, G., Chanel, C. P., Roy, R. N., 2021. Mental workload estimation based on physiological features for pilot-UAV teaming applications. Frontiers in Human Neuroscience 15, 692878. DOI: 10.3389/fnhum.2021.692878

Zijlstra, F. R. H., 1993. Efficiency in work behaviour: A design approach for modern tools. Ph.D. thesis, Delft University of Technology, Delft, The Netherlands.

Descargas

Publicado

01-09-2026

Número

Sección

Bioingeniería