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Human Activity Classification With Transmission and Reflection Coefficients of On-Body Antennas Through Deep Convolutional Neural Networks

Kim, Youngwook ; Li, Yang

IEEE transactions on antennas and propagation, 2017-05, Vol.65 (5), p.2764-2768 [Periódico revisado por pares]

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Título:
Human Activity Classification With Transmission and Reflection Coefficients of On-Body Antennas Through Deep Convolutional Neural Networks
Autor: Kim, Youngwook ; Li, Yang
Assuntos: Convolutional neural network (CNN) ; deep learning ; human activity classification ; joint time-frequency transform ; Machine learning ; on-body channel ; Spectrogram ; Time-frequency analysis ; Transmitting antennas ; Wrist
É parte de: IEEE transactions on antennas and propagation, 2017-05, Vol.65 (5), p.2764-2768
Descrição: We propose to classify human activities based on transmission coefficient (S 21 ) and reflection coefficient (S 11 ) of on-body antennas with deep convolutional neural networks (DCNNs). It is shown that spectrograms of S 21 and S 11 exhibit unique time-varying signatures for different body motion activities that can be used for classification purposes. DCNN, a deep learning approach, is applied to spectrograms to learn the necessary features and classification boundaries. It is found that DCNN can achieve classification accuracies of 98.8% using S 21 and 97.1% using S 11 . The effects of operating frequency and antenna location on the accuracy have been investigated.
Editor: IEEE
Idioma: Inglês

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