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

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.