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

Donya Fozoonmayeh1, Hai Vu Le1, Ekaterina Wittfoth1

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

Journal of Medical Systems
|March 1, 2020
PubMed
Summary
This summary is machine-generated.

Poor medication adherence leads to high healthcare costs. This study introduces a smartwatch app and cloud system using sensor data and machine learning to monitor medication intake, achieving high accuracy in identifying user activities.

Keywords:
Cloud computingDistributed computingDistributed databases.Distributed information systemsHealth monitoringInternet of thingsMachine learningMedication adherenceSmartwatchWearable

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Area of Science:

  • Biomedical Engineering
  • Health Informatics
  • Machine Learning

Background:

  • Poor medication adherence significantly increases healthcare expenditures, including hospital readmissions and visits.
  • Effective monitoring systems are needed to improve patient compliance with prescribed treatments.

Purpose of the Study:

  • To develop a user-friendly medication intake monitoring system using a smartwatch application and a cloud-based data pipeline.
  • To leverage sensor data and machine learning for accurate activity identification to support medication adherence.

Main Methods:

  • An Android smartwatch application was developed to collect activity sensor data (accelerometer, gyroscope).
  • A cloud-based data pipeline was implemented, featuring distributed storage, database management, and computing frameworks.
  • Machine learning algorithms were employed for sensor data extraction, preprocessing, and activity type identification.

Main Results:

  • The system achieved a high F1 score of 0.977 for activity identification.
  • The machine learning model demonstrated efficient training (13.313 seconds) and testing (0.139 seconds) times.

Conclusions:

  • The developed system effectively utilizes smartwatch sensor data and machine learning for activity recognition.
  • This technology shows promise in creating a user-friendly system to improve medication adherence and reduce associated healthcare costs.