UWB Impulse Radar for People Counting with Convolutional Neural Network on Microcontrollers

We present an exploration of the advancements in Ultra-wideband (UWB) radar technology applied to people counting in Internet of Things (IoT) applications. We introduce a novel lightweight convolutional neural network (CNN) model specifically designed for implementation on microcontrollers with low-power consumptions. Our proposed model successfully overcomes the limitations of existing methods, achieving an impressive accuracy rate of 99.38% for counting to 10 people while randomly walking in a small area of 5 m × 5 m. In addition, its lightweight architecture enables effortless integration with resource-constrained microcontrollers, enabling efficient execution of intelligent IoT tasks. Notably, our method maintains accuracy even after quantization, with the model retaining 98.22% accuracy while reducing its size by more than half. Benchmarking results demonstrate the model's efficiency, with inference times of less than 48 ms on a wide range of STM32 microcontrollers. Furthermore, the model with the most compact size specification achieves a remarkable inference time of only 3.8 milliseconds per prediction on an STM32 microcontroller. The proposed model is 47× faster with 16× fewer parameters compared to the ResNet architecture-based model. These results emphasize the significant potential of utilizing UWB Impulse Radar systems for practical people counting applications in real-world IoT scenarios, whether in static setups for indoor use or in a dynamic state mounted on an unmanned aerial vehicle.