Human motion recognition exploiting radar with stacked recurrent neural network |
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Affiliation: | 1. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731 China;2. Department of Electrical and Computer Engineering, Temple University, Philadelphia, PA, 19132 USA |
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Abstract: | We develop a novel radar-based human motion recognition technique that exploits the temporal sequentiality of human motions. The stacked recurrent neural network (RNN) with long short-term memory (LSTM) units is employed to extract sequential features for automatic motion classification. The spectrogram of raw radar data is used as the network input to utilize the time-varying Doppler and micro-Doppler signatures for human motion characterization. Based on experimental data, we verified that a stacked RNN with two 36-cell LSTM layers successfully classifies six different types of human motions. |
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Keywords: | Human motion recognition Radar Deep learning Recurrent neural network Long short-term memory |
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