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Erschienen in:
2021 | OriginalPaper | Buchkapitel
Human Identification Under Multiple Gait Patterns Based on FMCW Radar and Deep Neural Networks
verfasst von : Shiqi Dong, Weijie Xia, Yi Li, Kejia Chen
Erschienen in: Communications, Signal Processing, and Systems
Verlag: Springer Singapore
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Abstract
Human identification has been the crucial and difficult problem of domestic and foreign scholars for a long time. As a novel identification technology, more and more attention is paid to the gait identification, which has proven to be feasible. In this paper, the authors propose a gait identification method based on micro-Doppler signatures obtained by 77 GHz frequency-modulated continuous wave (FMCW) radar. The obtained signal is represented by time-frequency (T-F) spectrum, and then, deep neural network (DNN) is adopted to deal with the spectrums for human identification. It is shown that the method can identify humans under three different gait patterns (walking; jogging; and walking with books) with 95% accuracy for 50 people. In addition, the method can also identify humans even if the subject is walking under other gait patterns that are not included in the training set.