Real-time PPE Monitoring on the Edge saves cost and protect lives

The world’s first edge compute solution for Personal Protective Equipment ingress and real-time PPW monitoring


Personal Protective Equipment (PPE) is the equipment that protects the user against health or safety risks. Not wearing PPE dramatically increases the chances of injuries and in many situations also of financial losses due to fines for injuries and death of workers as well as contamination caused by not wearing gloves, hairnets, shoe covers, etc. This post describes the world’s first edge compute solution for PPE ingress and real-time PPE compliance monitoring.


AAEON partnering with Cortexica allows us to identify numerous real-world problems that can be addressed by applying machine vision methods. has been at the forefront of the machine vision revolution. We have reverse-engineered parts of the human visual cortex, which allowed us to develop a powerful image search engine nowadays widely used to solve many real-world problems. Some of the solutions to these problems are often very specific while others are widely applicable and have the potential to save lives. Probably the best example of this is our family of solutions designed for PPE ingress and real-time PPE compliance monitoring.

PPE is designed to protect users from serious injuries or illnesses resulting from physical, mechanical, electrical, chemical or radiological contact. The importance of PPE is paramount because it serves as the last line of defense against an injury or death. Unfortunately, studies have demonstrated that 98% of workers said they have seen others not wearing PPE when they should have been and 30% of those said this happens regularly. Head injuries, constituting 9% of all injuries, can be fatal and yet 84% of these were caused by not wearing a helmet [1]. Most of these injuries could have been prevented had there been a system in place that would continuously monitor for PPE compliance.

Recent advancements in edge computing gave rise to novel applications designed to process data right at its source effectively minimizing latency and allowing real-time processing. UP ! Bridge, the gap with CORTEXICA, presents a solution for PPE ingress and compliance monitoring that runs in real-time and entirely on the edge.

Check out a Martin Peniak interview at AAEON stand at IoT World Congress in Barcelona about our latest product developed in partnership with UP ! Bridge the gap and Cortexica Vision Systems designed to prevent injuries and save lives by automatically detecting when someone is missing Personal Protective Equipment (PPE).

Development Kit Overview

It takes more than algorithms to deliver an AI-driven product that solves a real-life problem. Working in partnership UP with Cortexica, we have developed UP Squared AI Edge-PPE monitoring, a development kit for health and safety professionals to create proof of concepts (POC) to full-blown AI applications ready for live deployment. The development kit offers the following:

•Real-time video analysis with advanced algorithms and machine learning to ensure employees are wearing the correct PPE for their working environment

•Parallel detection of PPE, Face, Person, and Body parts leveraging CPU, GPU, and VPU processors

•Powered by the latest ultra-low-power high-performance Intel Myriad X VPU

•Single image mode

•Real-time mode

•Made for POCs

The PPE monitoring development kit is available for purchase as an off-the-shelf solution, complete with hardware and software configuration, and detailed step-by-step guides to help you start prototyping your next AI monitoring or video surveillance project. For health and safety professions, this development kit assists the creation of a PPE monitoring system, and for industries at large, an AI-driven surveillance application.

If you are interested in trying out this kit, please visit UP Shop where you can order it!



[1] U.S. Department of Labor, Bureau of Labor Statistics, Accidents Involving Head Injuries, Report 605, (Washington, D.C., Government Printing Office, July 1980) p. 1