| Summary: | The movements of the human body and limbs result in unique micro-Doppler signatures, which can be exploited for classifying human activities. In this work, the authors propose a Convolutional Gated Recurrent Units Neural Network (CNN-GRU) to classify human activities of varying duration based on micro-Doppler spectrogram. Unlike conventional deep learning approaches which often treat the micro-Doppler spectrogram the same way as natural image, the authors extract local feature of micro-Doppler signatures via convolutional layer and encode temporal information with gated recurrent units. Through this unified framework, the temporal evolution of body motions within a short time can be better utilised. It avoids the resolution limitation caused by the fixed-size time window of input data and identifies human activity of duration shorter than the time window length. The experiment shows that CNN-GRU model is capable of recognising and temporally localising activity sequence contained in the spectrogram.
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