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International Conference on Case-Based Reasoning

ICCBR 2018: Case-Based Reasoning Research and Development pp 415–430Cite as

Modelling Similarity for Comparing Physical Activity Profiles - A Data-Driven Approach

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11156))

Abstract

Objective measurements of physical behaviour are an interesting research field from the public health and computer science perspective. While for public health research, measurements with a high quality and feasible setup is important, the analysis of and reasoning about the data is what we will present in this work. Our focus in this work is the comprehensive representation of physical behaviour throughout consecutive days and allowing to find subgroups in the population with similar physical activity levels.

We have a unique data set of 4628 participants wearing tri-axial accelerometers for six days and will present a case-based reasoning (CBR) system that can find and compare similar activity profiles. In this work, we focus on creating a CBR model using myCBR and do initial experiments with the resulting system. We will introduce a data-driven approach for modelling local similarity measures. Eventually, in the experiments we will show that for the given data set, the CBR system outperforms a k-Nearest Neighbor regressor in finding most similar participants.

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Notes

  1. 1.

    https://www.biology-online.org/dictionary/Phenotype.

  2. 2.

    https://wwwn.cdc.gov/nchs/nhanes/default.aspx.

  3. 3.

    http://who.int/features/factfiles/physical_activity/en/.

  4. 4.

    https://www.ntnu.no/hunt4/.

  5. 5.

    https://www.ntnu.no/hunt/.

  6. 6.

    Since the study is ongoing, we have used the data available by March, 12 2018.

  7. 7.

    https://axivity.com/downloads/ax3.

  8. 8.

    https://github.com/kerstinbach/mycbr-rest-example.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway

    Deepika Verma & Kerstin Bach

  2. Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway

    Paul Jarle Mork

Authors
  1. Deepika Verma
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  2. Kerstin Bach
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  3. Paul Jarle Mork
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Corresponding author

Correspondence to Deepika Verma .

Editor information

Editors and Affiliations

  1. Wright State University, Dayton, OH, USA

    Michael T. Cox

  2. Mälardalen University, Västeras, Sweden

    Peter Funk

  3. Mälardalen University, Västeras, Sweden

    Shahina Begum

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Verma, D., Bach, K., Mork, P.J. (2018). Modelling Similarity for Comparing Physical Activity Profiles - A Data-Driven Approach. In: Cox, M., Funk, P., Begum, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2018. Lecture Notes in Computer Science(), vol 11156. Springer, Cham. https://doi.org/10.1007/978-3-030-01081-2_28

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  • DOI: https://doi.org/10.1007/978-3-030-01081-2_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01080-5

  • Online ISBN: 978-3-030-01081-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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