STMicroelectronics and Schneider Electric unveil STM32MCUs based AI Human flow monitoring solution

     STMicroelectronics has joined forces with Schneider Electric, a market leader in the digital transformation of energy management and industrial automation, to launch an iot sensor prototype. By monitoring occupancy and utilization of buildings, the solution enables new types of property management services to improve the energy management efficiency of buildings. The two companies have partnered to integrate artificial intelligence (AI) technology within high-performance headcount sensors to overcome the challenges of monitoring the flow of people in large venues with multiple entrances.

     As building and habitat management becomes more digital, Schneider is committed to being a digital partner for sustainable development and energy efficiency, providing innovative and valuable insights, such as queue monitoring for smart building management, while fully respecting personal privacy in design. This advanced iot sensor solution combines the advanced expertise of ST's AI Products group with Schneider's deep sensor application expertise to embed high-performance object-detection neural networks into cost-effective microcontrollers (MCUS).

     Schneider Electric has increased design efficiency by using the STM32Cube.AI toolchain, which has proven capabilities to develop AI applications for a variety of STM32 MCUS. The engineering resources, advanced technology, and ease of use of the STM32Cube software development ecosystem provide Schneider Electric with valuable hardware design flexibility and efficiency.

     The statistical prototype sensor integrates Schneider Electric's exclusive ultra-low power LYNRED ThermEyeTM series of thermal imaging sensors, the recently launched high-performance STM32H723 MCU from ST, and a neural network model based on Yolo.

     Maxime Loidreau, project Manager for iot Sensors at Schneider Electric, said: "This promising technology unlocks a new solution for building occupancy monitoring and footfall statistics, which can be used to monitor queues, building utilization, social distance and many other applications. The innovative demonstration prototype we developed in partnership with ST is not only suitable for hotels, offices, department stores and retail, but also for any scenario that requires understanding the flow of people and space usage. This will redefine the future of architecture!"

     Miguel Castro, business line Manager for AI Solutions at ST, added: "This project proves that deep learning can improve embedded data processing performance and shows how to run high-value applications on a system platform based on an economical microcontroller. Our STM32Cube.AI ecosystem enables users to develop flexible solutions in a short time, overcome engineering challenges with the support services of our technical team, and customers can enjoy greater design efficiency."