DGRU based human activity recognition using channel state information

In this work, we have proposed a Deep Gated Recurrent Unit (DGRU) model for non-obtrusive human activity recognition using Channel State Information (CSI). Empirical model decomposition is used for de-noising, whereas discrete wavelet transforms and linear discriminant analysis are used for feature extraction and dimensionality reduction, respectively. For extensive experimental evaluation and comparative analysis, a Software Defined Radio (SDR) platform is used by implementing IEEE 802.11a on National Instruments’ Universal Software Radio Peripheral (USRP). The physical layer CSI is collected in an indoor environment to evaluate the performance for seven activities. 30 volunteers including both genders and of different age groups were involved in the data collection process. As demonstrated through experiments, the proposed scheme achieves promising results with an accuracy of 95–99% for all activities, outperforming the traditional benchmark approaches in the literature that use random forest and more advanced deep learning techniques, such as Long-Short Term Memory (LSTM).