Evaluación de la Carga Cognitiva en Teleoperación Robótica Simulada: Medidas Subjetivas y Fisiológicas
DOI:
https://doi.org/10.65011/prismaods.v5.i5.375Palabras clave:
carga cognitiva, carga mental de trabajo, teleoperación robótica, simulación, electroencefalografía, NASA-TLXResumen
La teleoperación robótica se ha consolidado como una herramienta fundamental para operar en entornos donde la presencia humana representa un riesgo, lo que ha incrementado el interés por evaluar la carga cognitiva de los operadores. El objetivo de este trabajo fue describir cómo se ha evaluado la carga cognitiva en tareas de teleoperación robótica desarrolladas en entornos simulados mediante medidas subjetivas y fisiológicas. Se revisaron 14 estudios publicados entre 2014 y 2026, localizados en Scopus, Web of Science, IEEE Xplore, PubMed y Google Scholar, y se analizaron según las técnicas de evaluación, las características de los entornos simulados y el tipo de tarea. El NASA-TLX fue el instrumento subjetivo más empleado (6 de 14 estudios) y la electroencefalografía (EEG), la técnica fisiológica predominante (5 de 14), mientras que la actividad electrodérmica, la variabilidad de la frecuencia cardiaca y la pupilometría se utilizaron con menor frecuencia. Un número creciente de estudios combina medidas subjetivas y fisiológicas en simulaciones de mayor fidelidad, lo que permite detectar variaciones en la carga cognitiva que no siempre se reflejan en el desempeño del operador. No obstante, pocos estudios evalúan la carga cognitiva en escenarios simulados de emergencia y desastre, lo que representa una oportunidad de investigación particularmente en aplicaciones de búsqueda y rescate.
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Antonenko, P., Paas, F., Grabner, R., & van Gog, T. (2010). Using electroencephalography to measure cognitive load. Educational Psychology Review, 22, 425–438. https://doi.org/10.1007/s10648-010-9130-y
Araya Carvajal, K., Ruiz Marin, M., & Ramos Marquez, Á. (2025). Robots teleoperados en la industria y los servicios: Una revisión bibliográfica sistematizada. BIOTECH & ENGINEERING Untels, 5(1), 183–184.
Arshaghi, B., Lawson, G., Briganti, R., & Siebers, P.-O. (2026). Human decision-making in crowds in a virtual flood scenario. Natural Hazards and Earth System Sciences, 26, 981–1000. https://doi.org/10.5194/nhess-26-981-2026
Brouwer, A.-M., Hogervorst, M. A., van Erp, J. B. F., Heffelaar, T., Zimmerman, P. H., & Oostenveld, R. (2012). Estimating workload using EEG spectral power and ERPs in the n-back task. Journal of Neural Engineering, 9(4), 045008. https://doi.org/10.1088/1741-2560/9/4/045008
Charles, R. L., & Nixon, J. (2019). Measuring mental workload using physiological measures: A systematic review. Applied Ergonomics, 74, 221–232. https://doi.org/10.1016/j.apergo.2018.08.028
Dell’Agnola, F., Jao, P.-K., Arza, A., Chavarriaga, R., Millán, J. del 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. https://doi.org/10.1109/JBHI.2022.3186625
Diarra, M., Theurel, J., & Paty, B. (2025). Systematic review of neurophysiological assessment techniques and metrics for mental workload evaluation in real-world settings. Frontiers in neuroergonomics, 6, 1584736. https://doi.org/10.3389/fnrgo.2025.1584736
Fernandez Rojas, R., Debie, E., Fidock, J., Barlow, M., Kasmarik, K., Anavatti, S., Garratt, M., & Abbass, H. (2020). Electroencephalographic workload indicators during teleoperation of an unmanned aerial vehicle shepherding a swarm of unmanned ground vehicles in contested environments. Frontiers in Neuroscience, 14, Article 40. https://doi.org/10.3389/fnins.2020.00040
Ghani, U., Signal, N., Niazi, I., & Taylor, D. (2020). ERP based measures of cognitive workload: A review. Neuroscience & Biobehavioral Reviews. https://doi.org/10.1016/j.neubiorev.2020.07.020
Glaros, Z., Carvalho, R. E., & Flynn-Evans, E. E. (2021). An evaluation of sleepiness, performance, and workload among operators during a real-time reactive telerobotic lunar mission simulation. Human Factors. https://doi.org/10.1177/00187208211056756
Hart, S. G. (2006). NASA-Task Load Index (NASA-TLX); 20 years later. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 50(9), 904–908. https://doi.org/10.1177/154193120605000909
Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In P. A. Hancock & N. Meshkati (Eds.), Human mental workload (pp. 139–183). Elsevier Science Publishers B.V.
Jain, M. (2024). Teleoperated robots: Bridging the gap between human intelligence and remote operations. International Journal of Innovative Research in Science, Engineering and Technology, 13(9), 16314–16327. https://doi.org/10.15680/IJIRSET.2024.1309049
Jiang, H., Mizobuchi, S., & Chignell, M. (2023). Scenario fidelity and perceived driver mental workload: Can workload assessment be crowdsourced? In Proceedings of the 13th International Conference on Advances in Information Technology (IAIT 2023). Association for Computing Machinery. https://doi.org/10.1145/3628454.3628458
Kaonain, T. E., Abdul Rahman, M. A., Mohammed Ariff, M. H., & Mondal, K. (2020). A simulated risk assessment of human-robot interaction in the domestic environment. International Journal of Robotics and Automation, 9(4), 300–310. https://doi.org/10.11591/ijra.v9i4.pp300-310
Khedr, M., Le, Q. D., & Yang, E. (2025). Teleoperation control for robotic systems in hazardous environments: Overview and challenges. In 2025 30th International Conference on Automation and Computing (ICAC). IEEE. https://doi.org/10.1109/ICAC65379.2025.11196301
Lee, S., & Lee, B. (2025). Electrodermal activity as an indicator of cognitive load for human-robot collaboration in construction. In Advances in Information Technology in Civil and Building Engineering (pp. 374–382). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-87364-5_31
Lopez Pulgarin, E. J., Tokatli, O., Burroughes, G., & Herrmann, G. (2022). Assessing tele-manipulation systems using task performance for glovebox operations. Frontiers in Robotics and AI, 9, 932538. https://doi.org/10.3389/frobt.2022.932538
Mohanavelu, K., Poonguzhali, S., Ravi, D., Singh, P. K., Mahajabin, M., Ramachandran, K., Singh, U. K., & Jayaraman, S. (2020). Cognitive workload analysis of fighter aircraft pilots in flight simulator environment. Defence Science Journal, 70(2), 131–139. https://doi.org/10.14429/dsj.70.14539
Moya, V., Slawiñski, E., Mut, V., & Couto, E. H. M. (2017). Workload detection based on EEG device for teleoperation of a mobile robot. In 2017 XVII Workshop on Information Processing and Control (RPIC) (pp. 1–6). IEEE. https://doi.org/10.23919/RPIC.2017.8211636
Musicant, O., Botzer, A., & Richmond-Hacham, B. (2024). Safety, efficiency, and mental workload in simulated teledriving of a vehicle as functions of camera viewpoint. Sensors, 24(18), 6134. https://doi.org/10.3390/s24186134
Nenna, F., Zanardi, D., & Gamberini, L. (2023). Enhanced interactivity in VR-based telerobotics: An eye-tracking investigation of human performance and workload. International Journal of Human-Computer Studies, 177, 103079. https://doi.org/10.1016/j.ijhcs.2023.103079
Odoh, G., Landowska, A., Crowe, E. M., Benali, K., Cobb, S., Wilson, M. L., Maior, H. A., & Kucukyilmaz, A. (2024). Performance metrics outperform physiological indicators in robotic teleoperation workload assessment. Scientific Reports, 14. https://doi.org/10.1038/s41598-024-82112-4
Paas, F., Tuovinen, J. E., Tabbers, H., & Van Gerven, P. W. M. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38(1), 63–71.
Pereira Salgado, D., Roque Martins, F., Abreu Rosa de Sá, A., Flynnn, R., Murray, N., & Lázaro Martins Naves, E. (2026). EEG-based cognitive load estimation during the use of a virtual wheelchair simulator. Frontiers in Signal Processing, 6, 1691777. https://doi.org/10.3389/frsip.2026.1691777
Roy, R. N., Charbonnier, S., Campagne, A., & Bonnet, S. (2016). Efficient mental workload estimation using task-independent EEG features. Journal of Neural Engineering, 13(2), 026019. https://doi.org/10.1088/1741-2560/13/2/026019
Shao, S., Zhou, Q., & Liu, Z. (2019). Mental workload characteristics of manipulator teleoperators with different spatial cognitive abilities. International Journal of Advanced Robotic Systems, 16(6), 1–10. https://doi.org/10.1177/1729881419888042
Shao, S., Wang, T., Wang, Y., Su, Y., Song, C., & Yao, C. (2020). Research of HRV as a measure of mental workload in human and dual-arm robot interaction. Electronics, 9(12), 2174. https://doi.org/10.3390/electronics9122174
Shaw, E., Roper, T., Nilsson, T., Lawson, G., Cobb, S. V. G., & Miller, D. (2019). The heat is on: Exploring user behaviour in a multisensory virtual environment for fire evacuation. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Article 626, pp. 1–13). Association for Computing Machinery. https://doi.org/10.1145/3290605.3300856
Sur, S., & Sinha, V. K. (2009). Event-related potential: An overview. Industrial Psychiatry Journal, 18(1), 70–73. https://doi.org/10.4103/0972-6748.57865
Verhagen, R. S., Neerincx, M. A., & Tielman, M. L. (2024). Meaningful human control and variable autonomy in human-robot teams for firefighting. Frontiers in Robotics and AI, 11, 1352601. https://doi.org/10.3389/frobt.2024.1323980
Verkennis, B., van Weelden, E., Marogna, F. L., Alimardani, M., Wiltshire, T. J., & Louwerse, M. M. (2025). Predicting workload in virtual flight simulations using EEG spectral and connectivity features. In 2025 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR). IEEE. https://doi.org/10.1109/AIxVR63409.2025.00019
Wang, J., Stevens, C., Bennett, W., & Yu, D. (2024). Granular estimation of user cognitive workload using multi-modal physiological sensors. Frontiers in Neuroergonomics, 5, 1292627. https://doi.org/10.3389/fnrgo.2024.1292627
Wickens, C. D. (2008). Multiple resources and mental workload. Human Factors, 50(3), 449–455. https://doi.org/10.1518/001872008X288394
Woehrle, H., & Kirchner, E. A. (2014). Online detection of P300 related target recognition processes during a demanding teleoperation task: Classifier transfer for the detection of missed targets. In Proceedings of the International Conference on Physiological Computing Systems (PhyCS-2014) (pp. 13–19). SCITEPRESS. https://doi.org/10.5220/0004724600130019
Wu, M. (2023). Robotics applications in natural hazards. Highlights in Science, Engineering and Technology, 43, 273–279.
Yang, J., Barragan, J. A., Farrow, J. M., Sundaram, C. P., Wachs, J. P., & Yu, D. (2024). An adaptive human-robotic interaction architecture for augmenting surgery performance using real-time workload sensing—Demonstration of a semi-autonomous suction tool. Human Factors, 66(4), 1081–1102.https://doi.org/10.1177/00187208221129940
Zahabi, S. J. N., Islam, M. S., Kim, S., Lau, N., Nussbaum, M. A., & Lim, S. (2025). Task difficulty in virtual reality forklift training can be assessed using normalized EEG measurements. International Journal of Industrial Ergonomics, 107, 103718. https://doi.org/10.1016/j.ergon.2025.103718
Zoric, F., Suarez, A., Vasiljevic, G., Orsag, M., Kovacic, Z., & Ollero, A. (2023). Performance comparison of teleoperation interfaces for ultra-lightweight anthropomorphic arms. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE.
Zunjic, A. (2015). Performance and workload of operators in a human-telerobot system. International Journal of Behavioral Research & Psychology, 3(2e), 1–3. https://doi.org/10.19070/2332-3000-150004e
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Derechos de autor 2026 Guilebaldo Vargas Solis, Raúl Juárez Morales, Petra Baldivia Noyola, Hugo Baltazar Palacios Pérez, Juan Parra Abarca (Autor/a)
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