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Real-Time Monitoring of Personal Protective Equipment Adherence Using On-Device Artificial Intelligence Models.

Yam Horesh1, Renana Oz Rokach1, Yotam Kolben1,2

  • 1Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9112001, Israel.

Sensors (Basel, Switzerland)
|April 12, 2025
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Summary

This study introduces an AI computer vision system for real-time monitoring of personal protective equipment (PPE) adherence among healthcare workers. The on-device system offers a cost-effective solution to improve compliance and enhance patient safety.

Keywords:
adherence monitoringcomputer visionedge computingpersonal protective equipmentsingle-board computer

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Area of Science:

  • Medical Informatics
  • Computer Vision
  • Infection Control

Background:

  • Personal protective equipment (PPE) is critical for preventing infections in healthcare settings.
  • Current methods for monitoring PPE compliance, such as education and inspections, are often costly and have limited effectiveness.
  • Consistent and correct use of PPE is essential for its efficacy.

Purpose of the Study:

  • To develop and evaluate a novel, on-device, AI-based computer vision system for real-time monitoring of healthcare worker PPE adherence.
  • To optimize the system for edge computing using a Raspberry Pi 5 for efficient, server-less operation.
  • To assess the system's accuracy and practical viability in a real-world clinical environment.

Main Methods:

  • A custom image dataset of 7142 images featuring various PPE combinations (mask, gloves, gown) was created.
  • Binary classifiers for individual PPE items were trained using a lightweight MobileNetV3 model.
  • The system was deployed on a Raspberry Pi 5 for edge computing and evaluated in a cardiac intensive care unit with unseen medical staff.

Main Results:

  • The AI models achieved high accuracy in identifying individual PPE items, ranging from 93% to 97%.
  • Overall accuracy for correct classification of all PPE items was 85.58 ± 0.82%.
  • Real-time evaluation demonstrated per-item accuracy between 87% and 89% in a clinical setting.

Conclusions:

  • AI-driven computer vision offers a promising, cost-effective, and efficient tool for improving PPE compliance in healthcare.
  • The developed on-device system demonstrates practical viability for real-time monitoring, enhancing patient safety and reducing infection risks.
  • This technology has the potential to significantly bolster infection prevention strategies in healthcare facilities.