Activity recognition of FMCW radar human signatures using tower convolutional neural networks

Human activity recognition has become an obligatory necessity in day to day life and possible solutions can be provided with the technological advancement of sensing field. Radar based sensing with its unbeatable unique features has been a promising solution for identifying and distinguishing human activities in recent years. The ascent of loss of life among elderly people in care homes during COVID-19 is mainly due to poor monitoring services, that was not able to track their daily life activities. This has even more emphasized the need for savvy activity monitoring and tracking system. In this work, we have used a dataset that has captured six daily life activities of people from different locations during different times under realistic environments, unlike an regular controlled data collection environment. We have proposed a novel tower based convolutional neural network architecture that has employed parallel input layers with individual color channel images sent as inputs to the model. We have concatenated all the unique signature features from each channel to have better and robust feature representation to the model. We have analyzed the proposed model with different color spaces like RGB, LAB, HSV as inputs and found that our chosen input type performs better with the proposed model with significant test accuracy results. We have also compared our proposed model with other existing state of art architectures for radar based human activity recognition.