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Human Activity Recognition Using Deep Models and Its Analysis from Domain Adaptation Perspective

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Software Technology: Methods and Tools (TOOLS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11771))

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Abstract

Human activity recognition (HAR) is a broad area of research which solves the problem of determining a user’s activity from a set of observations recorded on video or low-level sensors (accelerometer, gyroscope, etc.) HAR has important applications in medical care and entertainment. In this paper, we address sensor-based HAR, because it could be deployed on a smartphone and eliminates the need to use additional equipment. Using machine learning methods for HAR is common. However, such, methods are vulnerable to changes in the domain of training and test data. More specifically, a model trained on data collected by one user loses accuracy when utilised by another user, because of the domain gap (differences in devices and movement pattern results in differences in sensors’ readings.) Despite significant results achieved in HAR, it is not well-investigated from domain adaptation (DA) perspective. In this paper, we implement a CNN-LSTM based architecture along with several classical machine learning methods for HAR and conduct a series of cross-domain tests. The result of this work is a collection of statistics on the performance of our model under DA task. We believe that our findings will serve as a foundation for future research in solving DA problem for HAR.

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Notes

  1. 1.

    Sussex-Huawei Locomotion Dataset, http://www.shl-dataset.org/dataset/.

  2. 2.

    Sensors Activity Recognition Dataset, https://www.researchgate.net/publication/266384007_Sensors_Activity_Recognition_DataSet.

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Author information

Authors and Affiliations

  1. Innopolis University, Innopolis, Russia

    Nikita Gurov, Adil Khan & Rasheed Hussain

  2. College of Technological Innovation, Zayed University, Abu Dhabi, United Arab Emirates

    Asad Khattak

Authors
  1. Nikita Gurov
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  2. Adil Khan
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  3. Rasheed Hussain
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  4. Asad Khattak
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Corresponding author

Correspondence to Nikita Gurov .

Editor information

Editors and Affiliations

  1. Innopolis University, Innopolis, Russia

    Manuel Mazzara

  2. IUT de Blagnac , Blagnac, France

    Jean-Michel Bruel

  3. Innopolis University, Innopolis, Russia

    Bertrand Meyer

  4. Ivannikov Institute for System Programming, Russian Academy of Sciences, Moscow, Russia

    Alexander Petrenko

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Gurov, N., Khan, A., Hussain, R., Khattak, A. (2019). Human Activity Recognition Using Deep Models and Its Analysis from Domain Adaptation Perspective. In: Mazzara, M., Bruel, JM., Meyer, B., Petrenko, A. (eds) Software Technology: Methods and Tools. TOOLS 2019. Lecture Notes in Computer Science(), vol 11771. Springer, Cham. https://doi.org/10.1007/978-3-030-29852-4_15

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  • DOI: https://doi.org/10.1007/978-3-030-29852-4_15

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  • Online ISBN: 978-3-030-29852-4

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