Contactless Low Power Air-Writing Based on FMCW Radar Networks Using Spiking Neural Networks

Abstract

Contactless detection of hand gestures with radar has gained a lot of attention as an intuitive form of human-computer interface. In this paper, we propose an air-writing system, writing of linguistic characters or words in free space by hand gesture movements using a network of milli-meter wave radars. Most of the works reported in the literature are based on deep learning approaches, which in some cases can involve prohibitively large computational/energy costs making them undesirable for edge IoT devices, where energy efficiency is the prime concern. We propose a highly energy-efficient air-writing system using spiking neural networks, where the trajectory of the character created by fine range estimates together with trilateration from a network of radars are recognized and classified by a spiking neural network (SNN). The proposed system achieves a similar level of classification accuracy (98.6%) compared to the state-of-the-art deep learning methods for 15 characters containing 10 alphabets (A to J) and 5 numerals (1 to 5). Additionally, the proposed SNN model is of 3.7 MB in size making it memory efficient in terms of storage. We demonstrated the proposed method in real-time using a network of 60-GHz frequency-modulated continuous wave radar chipset.