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Pulse rhythm01:30

Pulse rhythm

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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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Real-Time Risk Assessment Detection for Weak People by Parallel Training Logical Execution of a Supervised Learning

Minh Long Hoang1, Armel Asongu Nkembi1, Phuong Ly Pham2

  • 1Department of Engineering and Architecture, University of Parma, 43124 Parma, PR, Italy.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary

This study introduces a Parallel Training Logical Execution (PTLE) system using machine learning and MEMS accelerometers to accurately detect activities like falls and coughs for enhanced safety monitoring.

Keywords:
COVID-19IoTaccelerometeractivity recognitionmachine learningrandom forest classificationwearable device

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Area of Science:

  • * Computer Science
  • * Biomedical Engineering
  • * Wearable Technology

Background:

  • * Activity monitoring is crucial for the safety of vulnerable individuals.
  • * Existing machine learning models struggle with high-category activity prediction accuracy.
  • * Microelectromechanical system (MEMS) accelerometers offer a viable sensing solution.

Purpose of the Study:

  • * To develop a Parallel Training Logical Execution (PTLE) system for improved activity recognition.
  • * To enhance the accuracy of detecting diverse human activities using machine learning.
  • * To provide a reliable safety monitoring solution for vulnerable populations.

Main Methods:

  • * Implementation of a PTLE system utilizing multiple, specialized machine learning models trained in parallel.
  • * Integration of six additional accelerometer-derived parameters as input features for enhanced model performance.
  • * Validation using Random Forest (RF) as the primary classification algorithm and real-time data from an M5stickC wearable device.

Main Results:

  • * The PTLE system achieved an overall accuracy of 98% in real-time activity detection.
  • * Demonstrated significant improvements in precision, recall, and F1-score compared to regular machine learning models.
  • * Successful real-time data transmission via Wi-Fi and Message Queue Telemetry Transport (MQTT).

Conclusions:

  • * The proposed PTLE system significantly enhances the accuracy and reliability of activity monitoring.
  • * The system effectively addresses the challenge of distinguishing between multiple activities for safety applications.
  • * Integration with Internet of Things (IoT) communication enables remote health monitoring and timely alerts.