PPE Safety check software development for medical facilities

5 minutes read

The customer wanted to protect their staff and patients from aerosol transmissible diseases (ATD), such as the novel coronavirus, in its medical imaging centers. They needed an intelligent, automated system to monitor wearing face masks in real time, as a prevention measure. The original machine learning model used in the PPE Detector was trained on 10K+ images of doctors, nurses and patients wearing/not wearing a face mask and other types of PPE. The software designed and developed for the customer demonstrated great accuracy of no less than 97% in detecting PPE violations.

About the client

The customer company is a leading provider of diagnostic imaging services in Odessa, Ukraine. The company utilizes state-of-the-art CT and MRI medical scanners to detect a wide range of conditions, to support the health and well-being of the local population.

Value Delivered

The software was integrated with an existing monitoring system, ensuring that violations could be identified and reported without a significant investment in the client’s application.

The software’s high accuracy, flexibility, and user-friendliness allowed the customer to monitor the work environment more efficiently. Building access areas, common facilities, and “safe zones” were under smart camera control. All individuals without a face mask were detected in real-time and immediately reported to the in-house epidemiological team. Fast reaction proved critical to limiting the risk of exposure to SARS-CoV-2.

Before a safe and effective vaccine against COVID-19 becomes available, doctors, nurses, and individuals visiting healthcare facilities remain increasingly vulnerable to the virus. Proper protection with PPE, including face masks, is of major importance. With a new, smart PPE detection system in place, the company safeguards its staff and patients against contracting infection, which allows the company to stay operational and assist communities amid the pandemic.

Technologies used:

java 11

spring boot

postgres

react

ActiveMQ

AWS SES

AWS CloudFormation

docker in AWS Fargate

PPE Safety

The company that enforces PPE compliance ensures the safety of its staff and patients amid COVID-19 pandemic

Business challenge

As nations around the globe confronted the novel coronavirus pandemic in 2020, they realized how vulnerable their healthcare systems were. Governments implemented the best possible public health policies to stop the spread of the virus, yet both public and private care facilities were quickly overrun with patients; doctors and nurses had to work even longer shifts, often without the required personal protective equipment (PPE).

Ukraine was also hit by COVID-19. As of August 2020, there were no less than 120,000 COVID cases officially reported, with 2451 individuals dead. Despite the government’s efforts to enforce mask wearing, social distancing and lockdowns, the virus continued to spread even at a larger scale.
In those settings, businesses had to act quick to stay afloat. Some of them provided masks and sanitizers, others decided to go beyond distributing PPE to take advantage of AI and machine learning to protect both their workers and customers.

With capabilities in place to CT scan lungs of COVID-19 patients, expected a surge in customer traffic. Hence, they needed to come up with an effective and cost-efficient solution to check if doctors, nurses, and patients wear PPE, and specifically face masks. Given how contagious and easily transmissible the novel coronavirus is, the company could not rely on human checkers. Custom software development service was the option they wanted to invest in to create an automated system for their needs.

Project description

The customer approached VITech to design and develop a system that would identify PPE violations in real time and report those violations to the in-house epidemiological safety team. The idea was that AI-driven face mask monitoring in combination with hand-washing and social distancing would significantly reduce the risk of COVID-19 spread among employees and patients in their medical centers, thus enabling the company to stay operational and functional amid the crisis.

The VITech team suggested developing a custom PPE Detector software for laboratory safety that could be customized to meet requirements. At the core of the detector is a machine learning model that processes and analyzes images captured by CCTV cameras to spot individuals not wearing any of four objects: Coat, Glasses, Gloves, Mask. Notifications are dispatched when the absence of PPE is detected.

To begin with, VITech reviewed monitoring solutions and CCTV cameras. For the machine learning model to work efficiently, the cameras must catch video streams in high quality with minimum latency. If so, video streams can be “sliced” frame by frame to push the individual high-resolution images for pixel-by-pixel ML analysis. Otherwise, the model will have low accuracy in image recognition, rendering the entire system is useless.

Then, the infrastructure and monitoring ecosystem was reviewed. It was critical for the client to make sure that a new cloud-hosted software could be technically integrated into the existing system, to minimize spending.

After the initial analysis and review, the following architecture was proposed:

The architecture consists of three parts: User UI, Application backend, and Video streams processing using machine learning.

Here’s how it works:

  • Users access the application’s frontend — the user interface — to manage cameras and to receive video streams and violation alerts
  • Using the application, the user signals the backend app and the workers to start creating new video streams; i.e. switch on the cameras
  • The workers start receiving video streams in real time and pushing them to the PPE model
  • The workers push images from video streams to the Amazon SageMaker model endpoint where the PPE violations are detected
  • Images featuring PPE violations are sent to the backend application to be stored in either Amazon S3 or Amazon RDS
  • The UI gets URLs for every video stream with violations and calls the workers for review
  • Amazon SES is used for user password management and sending reports

The customer agreed to implement the solution at one of its facilities and to scale the system organization-wide if it delivered high accuracy and enabled the epidemiological team to faster identify and react to PPE violations like not wearing a face mask in the building.

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