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A Scalable Smartwatch-Based Medication Intake Detection System Using Distributed Machine Learning

  • Mobile & Wireless Health
  • Published:
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Abstract

Poor Medication adherence causes significant economic impact resulting in hospital readmission, hospital visits and other healthcare costs. The authors developed a smartwatch application and a cloud based data pipeline for developing a user-friendly medication intake monitoring system that can contribute to improving medication adherence. The developed Android smartwatch application collects activity sensor data using accelerometer and gyroscope. The cloud-based data pipeline includes distributed data storage, distributed database management system and distributed computing frameworks in order to build a machine learning model which identifies activity types using sensor data. With the proposed sensor data extraction, preprocessing and machine learning algorithms, this study successfully achieved a high F1 score of 0.977 with 13.313 seconds of training time and 0.139 seconds for testing.

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Acknowledgements

This work was supported by Jesuit Foundation Grant, University of San Francisco Faculty Development Fund, and Systers Pass-it-on Award by Anita Borg Institute for Women and Technology. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding organizations.

Funding

This work was funded by 1) Spring 2017 Jesuit Foundation Grant, 2) University of San Francisco Faculty Development Fund, and 3) 2018 Systers Pass-it-on Award by Anita Borg Institute for Women and Technology.

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    Authors and Affiliations

    1. Data Science, University of San Francisco, San Francisco, CA, USA

      Donya Fozoonmayeh, Hai Vu Le, Ekaterina Wittfoth, Chong Geng, Natalie Ha, Jingjue Wang, Maria Vasilenko & Diane Myung-kyung Woodbridge

    2. University of California, San Diego, CA, USA

      Yewon Ahn

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    Correspondence to Diane Myung-kyung Woodbridge.

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    Diane Woodbridge has received research grants from Jesuit Foundation, University of San Francisco and Anita Borg Institute and has no conflict of interest.

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    Donya Fozoonmayeh, Hai Vu Le and Ekaterina Wittfoth have contributed equally.

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    Fozoonmayeh, D., Le, H.V., Wittfoth, E. et al. A Scalable Smartwatch-Based Medication Intake Detection System Using Distributed Machine Learning. J Med Syst 44, 76 (2020). https://doi.org/10.1007/s10916-019-1518-8

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