Study on feature processing schemes for deep-learning-based human activity classification using frequency-modulated continuous-wave radar

Radar is an attractive sensor for classifying human activity because of its invariance to the environment and its ability to operate under low lighting conditions and through obstacles. Classification for human activity finds applications in human-computer interfaces, user-intent understanding and contextual-aware smart homes. Moreover, frequency-modulated continuous-wave technology enables radar systems to retrieve both micro-Doppler and range profiles from humans, which can then be used to recognize target movements. Several feature processing schemes are studied on signatures obtained from a compact, short-range, 60-GHz, frequency-modulated continuous-wave radar for various human activities. The contribution of distinct features to the performance of classification is analysed and novel convolutional- and recurrent-neural-network architectures are consequently presented that efficiently combine range and Doppler information for classifying human activity. In particular, applying multibranch deep learning techniques to high-resolution range profiles and Doppler spectrograms achieves performance similar to that using range-Doppler map videos although it requires less computational and memory resources.