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IoT enabled health monitoring system using rider optimization algorithm and joint process estimation.

J Prabin Jose1, G Jaffino2, Mohammed Al Awadh3,4

  • 1School of Electronics Engineering, Vellore Institute of Technology , Vellore, India. prabinjose@gmail.com.

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Summary

This study introduces a novel real-time health monitoring system using MAX 30102 and LM35 sensors. The proposed Joint Process Estimator Rider Optimization Algorithm (JPEROA) significantly improves the accuracy, sensitivity, and specificity of health condition detection.

Keywords:
Auto-encoderCloud server & HealthInternet of things (IoT)Rider optimizationThingSpeak

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Area of Science:

  • Biomedical Engineering
  • Sensor Technology
  • Machine Learning for Healthcare

Background:

  • Timely detection of abnormal health conditions is critical for effective medical intervention and improved patient outcomes.
  • Existing real-time health monitoring systems often face limitations in achieving high accuracy, sensitivity, and specificity.
  • There is a need for enhanced performance in real-time sensor data analysis for health monitoring.

Purpose of the Study:

  • To develop an improved real-time health monitoring system utilizing sensor data.
  • To propose and evaluate the Joint Process Estimator Rider Optimization Algorithm (JPEROA) for classification tasks in health monitoring.
  • To compare the performance of the proposed JPEROA method against established machine learning algorithms.

Main Methods:

  • Real-time physiological data (heart rate, blood oxygen, body temperature) collected using MAX 30102 and LM35 sensors.
  • Data transmission and analysis via the ThingSpeak Internet of Things (IoT) cloud platform.
  • Standardization of sensed features for uniform scaling.
  • Implementation of the Joint Process Estimator Rider Optimization Algorithm (JPEROA) with Deep Stack Auto-encoder for classification, estimating line and delay coefficients.
  • Comparative analysis with Support Vector Machine, Random Forest, Gradient Boosting, Naive Bayes, and Multilayer Perceptron.
  • Validation using the PTB Diagnostic dataset.

Main Results:

  • The proposed JPEROA method achieved a maximum accuracy of 0.9625.
  • The system demonstrated a maximum sensitivity of 0.975 and a specificity of 0.95.
  • JPEROA outperformed other evaluated machine learning algorithms in accuracy, sensitivity, and specificity.

Conclusions:

  • The JPEROA algorithm, integrated with a Deep Stack Auto-encoder, offers a significant advancement in real-time health monitoring.
  • The proposed system effectively enhances the accuracy, sensitivity, and specificity of detecting abnormal health conditions.
  • This approach holds promise for improving patient outcomes through more reliable and precise health monitoring.