Human body and limb motion recognition via stacked gated recurrent units network

This study proposes a new radar-based human body and limb motion recognition method that exploited the temporal sequentiality of the motions. A stacked gated recurrent units network (SGRUN) is adopted to extract the dynamic sequential human motion patterns. Since the time-varying Doppler and micro-Doppler signatures can commendably represent such motion patterns, the spectrogram is utilised as the input sequence of the SGRUN. Numerical experiments verify that an SGRUN with two 34-neuron gated recurrent unit layers well classifies and recognises six distinct human body and limb motion types.