Computer vision in health care

Computer vision in Health Care

Computer vision technology can be used to monitor PPE violations in healthcare settings by providing real-time monitoring, using PPE detection algorithms, and automating compliance tracking. Real-time monitoring involves placing cameras in strategic locations that are connected to a computer system using computer vision algorithms to analyze the video feed. These algorithms can be trained to detect the presence or absence of PPE items, such as masks, gloves, or gowns, and identify PPE violations by comparing the detected PPE to a predefined standard.

5 ways Computer Vision used in Healthcare

Real-time monitoring

  • Computer vision systems can be used to analyze live video feeds from cameras in healthcare facilities, such as hospitals and clinics, to detect whether Healthcare workers are wearing the appropriate PPE, such as masks and gloves. If a worker is not wearing the required PPE, the system can trigger an alert to remind them to put it on.

Automated compliance checking

  • Computer vision can be used to analyze images or videos of healthcare workers and patients, to check if they are wearing the appropriate PPE. This can help to ensure that healthcare workers are following safety protocols and reduce the risk of infection.

Temperature monitoring

  • Computer vision in healthcare can also be used to detect fever in people by analyzing thermal imaging from cameras. This can help in identifying people with a higher temperature than normal, as fever is one of the symptoms of many infectious diseases.

Social Distancing

  • Computer vision can also be used to monitor social distancing in hospitals, clinics and other healthcare facilities. It can detect if people are standing too close to each other or not respecting physical distancing rules.

Contact tracing

  • Computer vision can be used to track the movement of people in healthcare facilities and identify those who have come into contact with someone who has tested positive for an infectious disease. This can help to quickly isolate those who may be infected and reduce the spread of the disease.

How we helped Health sectors with our PPE Detection solutions?

01. Workplace safety

The intent of this project is to build an accurate Computer Vision Intelligence model that can detect PPE violations (Hard-hat, reflective Vests, and Goggles) at construction sites and other industries to improve workplace safety and compliance with regulations.

02. Machine Learning models

Machine Learning models on TensorFlow or Yolo can be trained using annotated data using ‘Transfer Learning’.

03. on-site safety

This deep learning-based computer Vision solution detects and sends alerts when personnel working in sites are non-compliance and ensures on-site safety.

04. PPE requirements

Safety protocols like PPE requirements are routinely violated and traditional measures like safety training, manual monitoring, and safety bill-boards aren’t very effective

05. Hardware accelerators

These models can be optimized using Hardware accelerators like Intel OpenViNo or Nvidia TensorRT to be able to run in low cost, low power SBCs (Single Board Computers)

06. Cloud

These models can be hosted in the Cloud (AWS, Azure, or Google) to run on video stream from IP cameras to scale easily across any number of regular CCTV cameras


Ensuring compliance with PPE protocols

PPE detection solutions can help ensure that healthcare workers are wearing the appropriate PPE, such as masks, gloves, and gowns. This helps to reduce the risk of transmission of infectious diseases in healthcare settings.

Enhancing safety and protection

By detecting whether healthcare workers are wearing the right PPE, these solutions can enhance the safety and protection of both the healthcare workers and their patients. This is particularly important in settings where there is a high risk of exposure to infectious diseases, such as hospitals and clinics.

Reducing the risk of contamination

PPE detection solutions can help reduce the risk of contamination by alerting healthcare workers if they have touched contaminated surfaces or removed their PPE in a contaminated area.

Increasing efficiency

PPE detection solutions can help healthcare facilities to operate more efficiently by reducing the time and resources needed to monitor PPE compliance. This can also help to reduce the burden on healthcare workers and improve their overall job satisfaction.

Improving data tracking and analysis

PPE detection solutions can provide data on PPE usage and compliance, which can be used to identify areas for improvement and track progress over time. This can help healthcare facilities to make data-driven decisions and improve their overall safety protocols.

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