Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/75796
Title: Physical activity classification using resilient backpropagation (RPROP) with multiple outputs
Authors: Maarouf, Mustapha
Galván-González, Blas José 
UNESCO Clasification: 120304 Inteligencia artificial
120317 Informática
Keywords: Resilient backpropagation
Classification
Physical activity monitoring
Activity recognition
Issue Date: 2013
Publisher: Springer
Journal: Lecture Notes in Computer Science 
Conference: 14th International Conference on Computer Aided Systems Theory, EUROCAST 2013 
Abstract: Considerable research has been conducted into the classification of Physical activity monitoring, an important field in computing research. Using artificial neural networks model, this paper explains novel architecture of neural network that can classify physical activity monitoring, recorded from 9 subjects. This work also presents a continuation of benchmarking on various defined tasks, with a high number of activities and personalization, trying to provide better solutions when it comes to face common classification problems. A brief review of the algorithm employed to train the neural network is presented in the first section. We also present and discuss some preliminary results which illustrate the performance and the usefulness of the proposed approach. The last sections are dedicated to present results of many architectures networks. In particular, the experimental section shows that multiple-output approaches represent a competitive choice for classification tasks both for biological purposes, industrial etc.
URI: http://hdl.handle.net/10553/75796
ISBN: 978-3-642-53855-1
ISSN: 0302-9743
DOI: 10.1007/978-3-642-53856-8_10
Source: Moreno-Díaz R., Pichler F., Quesada-Arencibia A. (eds) Computer Aided Systems Theory - EUROCAST 2013. Lecture Notes in Computer Science, [ISSN 0302-9743]. Part I, vol 8111, p. 77-83, (2013). Springer, Berlin, Heidelberg.
Appears in Collections:Actas de congresos
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