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Towards Deep Personal Lifestyle Models Using Multimodal N-of-1 Data

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MultiMedia Modeling (MMM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13833))

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Abstract

The rise of wearable technology has enabled users to collect data about food, exercise, sleep, bio-markers, and other lifestyle parameters continuously and almost unobtrusively. However, there is untapped potential in developing personal models due to challenges in collecting longitudinal data. Therefore, we collect N-of-1 dense multimodal data for an individual over three years that encompasses their food intake, physical activity, sleep, and other physiological parameters. We formulate hypotheses to examine relationships between these parameters and test their validity through a combination of correlation, network mapping, and causality techniques. While we use correlation analysis and GIMME (Group Iterative Multiple Model Estimation) network plots to investigate the association between parameters, we use causal inference to estimate causal effects and check the robustness of causal estimates by performing refutation analysis. Through our experiments, we achieve statistical significance for the causal estimate thereby validating our hypotheses. We hope to motivate individuals to collect and share their long-term multimodal data for building personal models thereby revolutionizing future health approaches.

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Authors and Affiliations

  1. University of California Irvine, Irvine, CA, USA

    Nitish Nagesh, Iman Azimi, Tom Andriola, Amir M. Rahmani & Ramesh Jain

Authors
  1. Nitish Nagesh
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  2. Iman Azimi
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  3. Tom Andriola
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  4. Amir M. Rahmani
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  5. Ramesh Jain
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Corresponding author

Correspondence to Nitish Nagesh .

Editor information

Editors and Affiliations

  1. University of Bergen, Bergen, Norway

    Duc-Tien Dang-Nguyen

  2. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  3. Radboud University Nijmegen, Nijmegen, The Netherlands

    Martha Larson

  4. Dublin City University, Dublin, Ireland

    Alan F. Smeaton

  5. University of Amsterdam, Amsterdam, The Netherlands

    Stevan Rudinac

  6. National Institute of Information and Communications Technology, Tokyo, Japan

    Minh-Son Dao

  7. Department of Information Science and Media Studies, University of Bergen, Bergen, Norway

    Christoph Trattner

  8. La Trobe University, Melbourne, VIC, Australia

    Phoebe Chen

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Nagesh, N., Azimi, I., Andriola, T., Rahmani, A.M., Jain, R. (2023). Towards Deep Personal Lifestyle Models Using Multimodal N-of-1 Data. In: Dang-Nguyen, DT., et al. MultiMedia Modeling. MMM 2023. Lecture Notes in Computer Science, vol 13833. Springer, Cham. https://doi.org/10.1007/978-3-031-27077-2_46

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  • DOI: https://doi.org/10.1007/978-3-031-27077-2_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-27076-5

  • Online ISBN: 978-3-031-27077-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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