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Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
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Related Experiment Video

Updated: Sep 27, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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A Real-Time Crowd Monitoring and Management System for Social Distance Classification and Healthcare Using Deep

Sangeeta Yadav1, Preeti Gulia1, Nasib Singh Gill1

  • 1Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, India.

Journal of Healthcare Engineering
|April 15, 2022
PubMed
Summary

This research introduces a real-time crowd monitoring system to detect and classify social distancing violations. The system aids in COVID-19 prevention by analyzing surveillance footage for public health safety.

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Area of Science:

  • Computer Vision
  • Public Health Technology
  • Artificial Intelligence

Background:

  • The global spread of Coronavirus disease (COVID-19) necessitates effective public health interventions.
  • Physical contact and close proximity increase disease transmission, highlighting the need for social distancing.
  • Current public spaces lack adequate systems for real-time crowd monitoring and social distancing enforcement.

Purpose of the Study:

  • To propose a real-time crowd monitoring and management system for social distance classification.
  • To develop a system that aids in preventing COVID-19 outbreaks through enhanced public safety measures.
  • To enable early detection and classification of social distancing non-compliance in public areas.

Main Methods:

  • Utilizing YOLO v4 for object detection to segment individuals from backgrounds in surveillance data.
  • Employing Deepsort technique for tracking detected individuals using bounding boxes.
  • Implementing a system for real-time social distance classification in public spaces.

Main Results:

  • The system demonstrated effective people detection and tracking for social distance analysis.
  • Performance was evaluated using mean average precision (mAP) and frames per second (FPS) metrics.
  • Successful deployment on a low-cost embedded system (Jetson Nano) confirmed real-time viability.

Conclusions:

  • The developed system is suitable for real-time deployment in public areas for COVID-19 prevention.
  • Effective social distance monitoring and classification can significantly reduce disease transmission.
  • This technology offers a practical solution for enhancing public safety and healthcare during pandemics.