Gesture Recognition for FMCW Radar on the Edge
It addresses gesture recognition for edge devices with tight constraints on memory, compute, and power, representing an incremental improvement by optimizing existing methods for embedded platforms.
This paper tackles gesture recognition for embedded systems by introducing a lightweight system using 60 GHz FMCW radar, achieving an F1 score of 98.4% on a test dataset while running on an Arm Cortex-M4 microcontroller with low resource usage.
This paper introduces a lightweight gesture recognition system based on 60 GHz frequency modulated continuous wave (FMCW) radar. We show that gestures can be characterized efficiently by a set of five features, and propose a slim radar processing algorithm to extract these features. In contrast to previous approaches, we avoid heavy 2D processing, i.e. range-Doppler imaging, and perform instead an early target detection - this allows us to port the system to fully embedded platforms with tight constraints on memory, compute and power consumption. A recurrent neural network (RNN) based architecture exploits these features to jointly detect and classify five different gestures. The proposed system recognizes gestures with an F1 score of 98.4% on our hold-out test dataset, it runs on an Arm Cortex-M4 microcontroller requiring less than 280 kB of flash memory, 120 kB of RAM, and consuming 75 mW of power.