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A Mobile Crowd Sensing Application for Hypertensive Patients.

Slađana Jovanović1, Milan Jovanović2, Tamara Škorić3

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Summary
This summary is machine-generated.

This study introduces a mobile crowd sensing platform for hypertensive patients, using machine learning to provide health feedback. The system addresses data reliability challenges for remote cardiovascular monitoring.

Keywords:
Internet of Everythinghypertensionmachine learningmobile crowd sensingquality of information

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Area of Science:

  • Biomedical Engineering
  • Health Informatics
  • Mobile Health (mHealth)

Background:

  • Mobile crowd sensing (MCS) offers a novel approach to data collection for healthcare applications.
  • Hypertension management requires continuous monitoring, but traditional methods are often resource-intensive.
  • Existing MCS platforms face challenges with data reliability, limited resources, and volunteer motivation.

Purpose of the Study:

  • To develop and evaluate a mobile crowd sensing platform for collecting data from hypertensive patients.
  • To create an experimental database using Platform as a Service (PaaS) for hypertension research.
  • To implement machine learning techniques for patient status feedback and motivation.

Main Methods:

  • Utilized a mobile crowd sensing network to collect data from hypertensive patients.
  • Employed cloud-based Platform as a Service (PaaS) for data management and analysis.
  • Applied machine learning algorithms, including Random Forest, to analyze data and provide feedback.

Main Results:

  • Successfully created an experimental database of patient data without direct blood pressure measurements.
  • The Random Forest algorithm demonstrated superior performance in analyzing patient data.
  • The developed platform provided effective feedback to motivate MCS volunteers.

Conclusions:

  • The proposed MCS platform is a viable solution for remote monitoring of hypertensive patients.
  • Machine learning, particularly Random Forest, can effectively analyze indirect data for health status assessment.
  • The platform's adaptability suggests potential for monitoring other cardiovascular conditions.