Application of Spiking Neural Networks for Action Recognition from Radar Data

Abstract

In the past two decades, radar-based human sensing has become a topic of intense research. Unlike vision-based techniques which require the use of camera, radars are unobtrusive and privacy preserving in nature. Further, radars are agnostic of the lighting conditions and can be used for through-the-wall imaging thereby making them hugely effective in many situations. Compact, affordable radars have been designed that can be easily integrated with remote monitoring systems. However, the classical machine learning techniques currently used for learning and inferring human actions from radar images are compute intensive, and require large volume of training data, making them unsuitable for deployment on the network edge. In this paper, we propose to use the concepts of neuromorphic computing and Spiking Neural Networks (SNN) to learn human actions from data captured by the radar. To the best our knowledge, this is the first attempt of using SNNs on micro-Doppler data from radars. Our SNN model is capable of learning spatial as well as temporal features from the data and our experiments have resulted in 85% accuracy which is comparable with the classical machine learning approaches that are typically used on similar data. Further, the use of neuromorphic and SNN concepts make our model deployable over evolving neuromorphic edge devices thereby making the entire approach more efficient in terms of data, computation and energy consumption.