Enhancing Human Learning of Motions: An Approach Through Clustering

Author:

Couland Quentin,Hamon Ludovic,George Sébastien

Publisher

Springer International Publishing

Reference7 articles.

1. Hachaj, T., Ogiela, M.R.: Full body movements recognition - unsupervised learning approach with heuristic R-GDL method. Digit. Signal Process. 46, 239–252 (2015)

2. Larboulette, C., Gibet, S.: A review of computable expressive descriptors of human motion. In: Proceedings of the 2nd International Workshop on Movement and Computing, MOCO 2015, pp. 21–28. ACM, New York (2015)

3. Morel, M.: Multidimensional time-series averaging: application to automatic and generic evaluation of sport gestures. Theses, Université Pierre & Marie Curie, November 2017. https://hal.archives-ouvertes.fr/tel-01659753

4. Nunes, J.F., Moreira, P.M.: Handbook of Research on Computational Simulation and Modeling in Engineering (2016)

5. Rand, W.M.: Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66(336), 846–850 (1971)

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1. Technology-Enhanced Learning of Motions Based on a Clustering Approach;Technology Supported Innovations in School Education;2020

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