A deeply embedded radar based hand gesture recognition application
Stephan SCHOENFELDT, Lead Principle System Architect,
Contactless interaction with machines (elevators, vending machines, ticket machines, information terminals, etc.) is an effective way to avoid the spread Covid-19 or other virus by machines, regularly used by many different people. The demand for such solutions is already there and will persist. Regular disinfection of the interfaces can help in this situation, but is not practical as it has to happen regularly and the number of entities is just too large.
Radar Sensors are well suited to detect and classify different gestures. Compared to solution using RGB cameras, infrared or ultrasound sensors, they have advantages when it comes to overall sensitivity, maximum range and robustness towards disturbers. Additionally they are superior in different aspects of industrial design, as product designers can place them behind different types of material and they do not require openings in the housing. This makes radar sensors very robust against dust and vapor. Finally yet importantly, radar sensors provide intrinsic privacy protection.
This talk is about how we implement a radar based hand gesture recognition application on an M4 Microcontroller running at 150 MHz and comprising a total RAM footprint of below 300 kBytes (Cypress PSOC6). I cover the required preprocessing/feature extraction algorithm, the neural network design and training strategy. Furthermore, I discuss the approach to network quantization and how we use tensor flow light micro as an inference runtime.
I elaborate on execution timing and resource consumption in on the embedded platform. Finally, I show a video of the final application running on the microcontroller.