Low Power Embedded Gesture Recognition Using Novel Short-Range Radar Sensors

Abstract

This work proposes a low-power high-accuracy embedded hand-gesture recognition using low power short-range radar sensors. The hardware and software match the requirements for battery-operated wearable devices. A 2D Convolutional Neural Network (CNN) using range frequency Doppler features is combined with a Temporal Convolutional Neural Network (TCN) for time sequence prediction. The final algorithm has a model size of only 45723 parameters, yielding a memory footprint of only 91kB. Two datasets containing 11 challenging hand gestures performed by 26 different people have been recorded containing a total of 20210 gesture instances. On the 11 hands, gestures and an accuracy of 87% (26 users) and 92% (single user) have been achieved. Furthermore, the prediction algorithm has been implemented in the GAP8 Parallel Ultra-Low-Power processor by GreenWaves Technologies, showing that live-prediction is feasible with only 21mW of power consumption for the full gesture prediction neural network.