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

Journal: Journal of medical systems
PMID: 32112271

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.

Authors

  • Donya Fozoonmayeh
    Data Science, University of San Francisco, San Francisco, CA, USA.
  • Hai Vu Le
    Data Science, University of San Francisco, San Francisco, CA, USA.
  • Ekaterina Wittfoth
    Data Science, University of San Francisco, San Francisco, CA, USA.
  • Chong Geng
    Data Science, University of San Francisco, San Francisco, CA, USA.
  • Natalie Ha
    Data Science, University of San Francisco, San Francisco, CA, USA.
  • Jingjue Wang
    Data Science, University of San Francisco, San Francisco, CA, USA.
  • Maria Vasilenko
    Data Science, University of San Francisco, San Francisco, CA, USA.
  • Yewon Ahn
    University of California, San Diego, CA, USA.
  • Diane Myung-Kyung Woodbridge

Keywords

External Resources

Popular Topics
Recent Journals