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International Conference on Practical Applications of Agents and Multi-Agent Systems

PAAMS 2019: Advances in Practical Applications of Survivable Agents and Multi-Agent Systems: The PAAMS Collection pp 90–102Cite as

A New Deep Hierarchical Neural Network Applied in Human Activity Recognition (HAR) Using Wearable Sensors

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11523))

Abstract

Human Activity Recognition (HAR) using wearable sensors is becoming more practical in the field of security and health care monitoring. Deep and Machine learning techniques have been widely used in this area. Since smartphones and their applications are the parts of daily life, they can be very helpful in data gathering and online learning in HAR problems. In this paper, a new hierarchical neural network structure with the hierarchical learning method is presented and applied to the HAR. The proposed model is a deep learner neural network without using heavy computations that CNN-based deep learners usually suffer from. This makes the suggested model suitable for being embedded in agent and multi-agent based solutions and online learning, especially when they are implemented in small devices such as smartphones. In addition the hidden layer of the first section of the proposed model benefits automatic nonlinear feature extraction. The extracted features are proper for classifications. Handling the dimension of data is one of the challenges in HAR problem. In our model, data dimension reduction is automatically performed in the hidden layers of the different network sections. According to the empirical results, our proposed model yields better performance on the Opportunity data sets, compared to the similar ML algorithms.

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Authors and Affiliations

  1. Purdue University, West Lafayette, IN, 47907, USA

    Zahra Ghorrati & Eric T. Matson

Authors
  1. Zahra Ghorrati
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  2. Eric T. Matson
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Corresponding author

Correspondence to Zahra Ghorrati .

Editor information

Editors and Affiliations

  1. Grenoble Computer Science Laboratory, Grenoble, France

    Yves Demazeau

  2. Purdue University, West Lafayette, IN, USA

    Eric Matson

  3. University of Salamanca, Salamanca, Spain

    Juan Manuel Corchado

  4. University of Salamanca, Salamanca, Spain

    Fernando De la Prieta

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Ghorrati, Z., Matson, E.T. (2019). A New Deep Hierarchical Neural Network Applied in Human Activity Recognition (HAR) Using Wearable Sensors. In: Demazeau, Y., Matson, E., Corchado, J., De la Prieta, F. (eds) Advances in Practical Applications of Survivable Agents and Multi-Agent Systems: The PAAMS Collection. PAAMS 2019. Lecture Notes in Computer Science(), vol 11523. Springer, Cham. https://doi.org/10.1007/978-3-030-24209-1_8

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  • DOI: https://doi.org/10.1007/978-3-030-24209-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24208-4

  • Online ISBN: 978-3-030-24209-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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