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Extended Body-Angles Algorithm to recognize activities within intelligent environments

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Abstract

Machine learning methods have been proven to be a useful and powerful mechanism in classification problems and pattern recognition. It is possible to classify into different categories on the basis of a training set of data through a discrete number of characteristics and without knowing the whole intrinsic information. For that, those methods are widely extended to address Activity Recognition. One of the limitations of traditional machine learning methods is the need of a large training dataset to get an effective model with high accuracy levels. This issue is the main motivation of the present work that proposes the named Extended Body-Angles Algorithm, a method able to recognize movements through single samples of modelled movements using Kinect. Their functioning is based on the Body-Angles Algorithm. This manuscript deepens into the detection of postures (Body-Angles Algorithm) that has been generalized to movements (Extended Body-Angles Algorithm), describing also the results of the algorithm evaluation.

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Acknowledgements

This work was conducted in the context of UBIHEALTH project under International Research Staff Exchange Schema (MC-IRSES 316337) and the coordinated Project Grant TIN2013-47152-C3-1-R (FRASE), funded by the Spanish Ministerio de Ciencia e Innovación.

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Authors and Affiliations

  1. MAmI Research Lab, University of Castilla-La Mancha, Paseo de la Universidad 4, 13071, Ciudad Real, Spain

    Carlos Gutiérrez López de la Franca, Ramón Hervás, Esperanza Johnson, Tania Mondéjar & José Bravo

  2. eSmile – Psychology for Children & Adolescents, Calle Toledo 79, 1° E, Ciudad Real, Spain

    Tania Mondéjar

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  1. Carlos Gutiérrez López de la Franca
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  2. Ramón Hervás
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  3. Esperanza Johnson
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  4. Tania Mondéjar
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  5. José Bravo
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Corresponding author

Correspondence to Ramón Hervás.

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Gutiérrez López de la Franca, C., Hervás, R., Johnson, E. et al. Extended Body-Angles Algorithm to recognize activities within intelligent environments. J Ambient Intell Human Comput 8, 531–549 (2017). https://doi.org/10.1007/s12652-017-0463-y

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  • DOI: https://doi.org/10.1007/s12652-017-0463-y

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