Human activity recognition based on channel state information (CSI) using commercial WiFi devices plays an increasingly important role in many applications, such as smart home and interactive games. In this paper, we propose a WiFi CSI based human activity recognition approach using deep recurrent neural network (HARNN). HARNN mainly exploits four key techniques to recognize different human activities. HARNN firstly constructs a novel two-level decision tree for using two environment variation statistics efficiently. Meanwhile, a linear regression method is also introduced to seek for the optimal parameter for the designed decision tree. Depending on this, the decision tree is used to sense indoor environment variation, and then detect whether there is any human activity occurring in a target area. In addition, a noise removal mechanism is devised to eliminate the influence of random noise derived from indoor environments. Then, to characterize various human activities, two representative features are extracted from different statistical profiles, including channel power variation (CPV) and time-frequency analysis (TFA). Finally, a recurrent neural network (RNN) model is utilized to recognize different human activities by leveraging the extracted representative features above. According to the above steps, the proposed HARNN could establish a robust relationship between human activities and WiFi CSI compared with most of the existing WiFi CSI based approaches. The proof-of-concept prototype of HARNN is implemented on a set of commercial WiFi devices, and its overall performance is evaluated in several typical indoor environments. The experimental results demonstrate that HARNN can achieve better recognition performance compared with some benchmark approaches.
Human identity identification based on channel state information (CSI) using commercial WiFi devices has drawn increasingly attention, and it can be used in many applications such as smart home, intrusion detection, building monitoring, activity recognition, etc. However, most of the existing identity identification approaches are sensitive to the influence of random noise derived from indoor environments, and thus their identification accuracies are far from satisfactory. In the present paper, a devicefree CSI based human identity identification approach using deep learning (Wihi) is proposed. Wihi mainly utilizes three key techniques to identify different people. Firstly, to eliminate the influence of the random noise, discrete wavelet transform (DWT) strategy is introduced to denoise raw CSI data by leveraging signal decomposition. Secondly, in order to characterize human's gaits profoundly, several representative features are exploited from different statistical profiles, including channel power distribution in time domain (CPD), time-frequency analysis (TFA), and energy distribution in different frequency bands (ED). Thirdly, a recurrent neural network (RNN) model with long short-term memory (LSTM) blocks is employed to learn the representative gait features extracted above and encode temporal information for realizing human identity identification. The proof-of-concept prototype of the proposed Wihi approach is implemented on a set of commercial WiFi devices, and multiple comprehensive experiments have been carried out to evaluate the performance of identity identification. The experimental results confirm that the proposed Wihi can achieve a satisfactory performance compared with some state-of-the-art approaches.INDEX TERMS Human identity identification, commercial wifi devices, channel state information (CSI), recurrent neural network (RNN).
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