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Active Sensing in Human Activity Recognition

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Advances in Computational Intelligence (IWANN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10305))

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Abstract

This work studies the problem of reducing the energy consumption of wearable sensors in a Human Activity Recognition (HAR) system. A HAR system is implemented using Hidden Markov Models, where decisions over the acquisition of new data are made based on the entropy of the posterior distribution of the activities. This problem is intractable in general, so three different active sensing algorithms are implemented to find numerically the data acquisition events. The performance of these algorithms is evaluated using a HAR database, resulting in a significant reduction on the number of observations acquired, thus reducing the energy consumption, while maintaining the performance of the system.

Alfredo Nazábal is supported by a FPI grant by the Ministerio de Economía y Competitividad of Spain (BES-2013-064825).

This work has been partly supported by MINECO/FEDER (‘ADVENTURE’, id. TEC2015-69868-C2-1-R), MINECO (AID, id. TEC2014-62194-EXP, grant ‘DAMA’, id. TIN2015-70308-REDT), and Comunidad de Madrid (project ‘CASI-CAM-CM’, id. S2013/ICE-2845).

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Notes

  1. 1.

    The dataset is available at http://www.tsc.uc3m.es/dataproy/har/databases.zip.

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Author information

Authors and Affiliations

  1. Department of Signal Theory and Communications and Gregorio Marañón Health Research Institute, Universidad Carlos III de Madrid, Leganés, Spain

    Alfredo Nazábal & Antonio Artés

Authors
  1. Alfredo Nazábal
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  2. Antonio Artés
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Corresponding authors

Correspondence to Alfredo Nazábal or Antonio Artés .

Editor information

Editors and Affiliations

  1. Universidad de Granada , Granada, Spain

    Ignacio Rojas

  2. University of Malaga , Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Vilanova i la Geltrú, Barcelona, Spain

    Andreu Catala

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Nazábal, A., Artés, A. (2017). Active Sensing in Human Activity Recognition. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10305. Springer, Cham. https://doi.org/10.1007/978-3-319-59153-7_14

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  • DOI: https://doi.org/10.1007/978-3-319-59153-7_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59152-0

  • Online ISBN: 978-3-319-59153-7

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