C. Y. Wong and G. Seet, Workload, awareness and automation in multiple-robot supervision, Int. J. Adv. Robot. Syst, vol.14, 2017.

R. J. Lysaght, S. G. Hill, A. O. Dick, B. D. Plamondon, P. M. Linton et al., Comprehensive Review and Evaluation of Operator Workload Methodologies

N. Moray, Mental Workload: Its Theory and Measurement, vol.8, 2013.

S. G. Hart and L. E. Staveland, Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research, Adv. Psychol, vol.52, pp.139-183, 1988.

M. R. Endsley, Design and evaluation for situation awareness enhancement, Proceedings of the Human Factors Society Annual Meeting, vol.32, pp.97-101, 1988.

J. J. Roldán, E. Peña-tapia, A. Martín-barrio, M. A. Olivares-méndez, J. Del-cerro et al., Multi-robot interfaces and operator situational awareness: Study of the impact of immersion and prediction, vol.17, 1720.

M. Hou, H. Zhu, M. Zhou, and G. R. Arrabito, Optimizing operator-agent interaction in intelligent adaptive interface design: A conceptual framework, IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.), vol.41, pp.161-178, 2011.

J. J. Roldán, M. A. Olivares-méndez, J. Del-cerro, and A. Barrientos, Analyzing and improving multi-robot missions by using process mining, Auton. Robots, vol.42, pp.1187-1205, 2018.

H. A. Yanco, A. Norton, W. Ober, D. Shane, A. Skinner et al., Analysis of human-robot interaction at the darpa robotics challenge trials, J. Field Robot, vol.32, pp.420-444, 2015.

M. L. Cummings, C. E. Nehme, J. Crandall, and P. Mitchell, Predicting operator capacity for supervisory control of multiple UAVs, Innovations in Intelligent Machines, vol.1, pp.11-37, 2007.

A. Hocraffer and C. S. Nam, A meta-analysis of human-system interfaces in unmanned aerial vehicle (UAV) swarm management, Appl. Ergon, vol.58, pp.66-80, 2017.

J. J. Roldán, E. Peña-tapia, P. Garcia-aunon, J. Del-cerro, and A. Barrientos, Bringing adaptive & immersive interfaces to real-world multi-robot scenarios: Application to surveillance and intervention in infrastructures, IEEE Access, 2019.

C. S. Loh, Y. Sheng, and D. Ifenthaler, Serious Games Analytics: Methodologies for Performance Measurement, Assessment, and Improvement, 2015.

T. W. Van-ruijven, Serious games as experiments for emergency management research: A review, Proceedings of the 8th International Conference on Information Systems for Crisis Response and Management, pp.8-11, 2011.

R. T. Wood, M. D. Griffiths, and V. Eatough, Online data collection from video game players, Methodological issues. CyberPsychology Behav, vol.7, pp.511-518, 2004.

V. Rodriguez-fernandez, C. Ramirez-atencia, and D. Camacho, A multi-uav mission planning videogame-based framework for player analysis, Proceedings of the 2015 IEEE Congress on Evolutionary Computation (CEC), pp.1490-1497, 2015.

P. Jer?i?, J. Hagelbäck, and C. Lindley, Physiological Affect and Performance in a Collaborative Serious Game Between Humans and an Autonomous Robot, International Conference on Entertainment Computing, pp.127-138, 2018.

W. M. Ijgosse, H. Van-goor, and J. M. Luursema, Saving robots improves laparoscopic performance: Transfer of skills from a serious game to a virtual reality simulator, Surg. Endosc, vol.32, pp.3192-3199, 2018.

K. D. Bailey, Typologies and Taxonomies: An Introduction to Classification Techniques, Sage, vol.102, 1994.

M. B. Eisen, P. T. Spellman, P. O. Brown, and D. Botstein, Cluster analysis and display of genome-wide expression patterns, Proc. Natl. Acad. Sci, vol.95, pp.14863-14868, 1988.

A. A. Rupp and S. J. Sweet, Analysis of Multivariate Social Science Data, p.20, 2011.

P. Haldar, I. D. Pavord, D. E. Shaw, M. A. Berry, M. Thomas et al., Cluster analysis and clinical asthma phenotypes, Am. J. Respir. Crit. Care Med, vol.178, pp.218-224, 2008.

A. M. Bensaid, L. O. Hall, J. C. Bezdek, and L. P. Clarke, Partially supervised clustering for image segmentation, Pattern Recognit, vol.29, pp.859-871, 1996.

J. Billieux, G. Thorens, Y. Khazaal, D. Zullino, S. Achab et al., Problematic involvement in online games: A cluster analytic approach, Comput. Hum. Behav, vol.43, pp.242-250, 2015.

V. Rodríguez-fernández, H. D. Menéndez, and D. Camacho, A study on performance metrics and clustering methods for analyzing behavior in UAV operations, J. Intell. Fuzzy Syst, vol.32, pp.1307-1319, 2017.

V. Rodríguez-fernández, H. D. Menéndez, and D. Camacho, Automatic profile generation for uav operators using a simulation-based training environment, Prog. Artif. Intell, vol.5, pp.37-46, 2016.

V. Rodríguez-fernández, H. D. Menéndez, and D. Camacho, Analysing temporal performance profiles of UAV operators using time series clustering, Expert Syst. Appl, vol.70, pp.103-118, 2017.

A. Fahad, N. Alshatri, Z. Tari, A. Alamri, I. Khalil et al., A survey of clustering algorithms for big data: Taxonomy and empirical analysis, IEEE Trans. Emerg. Top. Comput, vol.2, pp.267-279, 2014.
URL : https://hal.archives-ouvertes.fr/hal-02009335

J. A. Hartigan and M. A. Wong, Algorithm AS 136: A k-means clustering algorithm, J. R. Stat. Soc. Ser. C (Appl. Stat.), vol.28, pp.100-108, 1979.

T. Kohonen, The self-organizing map, Proc. IEEE, vol.78, pp.1464-1480, 1990.

L. Kaufman and P. J. Rousseeuw, Partitioning around medoids (program pam). Finding Groups in Data: An Introduction to Cluster Analysis

R. K. Blashfield, Mixture model tests of cluster analysis: Accuracy of four agglomerative hierarchical methods, Psychol. Bull, vol.83, pp.377-388, 1976.

T. Calinski and J. Harabasz, A dendrite method for cluster analysis, Commun. Stat.-Theory Methods, vol.3, pp.1-27, 1974.

D. L. Davies and D. W. Bouldin, A cluster separation measure, IEEE Trans. Pattern Anal. Mach. Intell, vol.2, pp.224-227, 1979.

P. J. Rousseeuw, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, J. Comput. Appl. Math, vol.20, pp.53-65, 1987.

C. Bishop, Pattern Recognition and Machine Learning

J. Casper and R. R. Murphy, Human-robot interactions during the robot-assisted urban search and rescue response at the world trade center, IEEE Trans. Syst. Man Cybern. Part B (Cybern.), vol.33, pp.367-385, 2003.

D. Peña, ;. Análisis-de-datos-multivariante, and . Mcgraw-hill, This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution, Licensee MDPI, vol.24, 2002.