Deep Learning plays an increasingly important role in device-free WiFi Sensing for human activity recognition (HAR). Despite its strong potential, significant challenges exist and are associated with the fact that one may require a large amount of samples for training, and the trained network cannot be easily adapted to a new environment. To address these challenges, we develop a novel scheme using Matching Network with enhanced channel state information (MatNet-eCSI) to facilitate one-shot learning HAR. We propose a CSI Correlation Feature Extraction (CCFE) method to improve and condense the activity-related information in input signals. It can also significantly reduce the computational complexity by decreasing the dimensions of input signals. We also propose novel training strategy which effectively utilizes the data set from the previously seen environments (PSE). In the least, the strategy can effectively realize human activity recognition using only one sample for each activity from the testing environment and the data set from one PSE. Numerous experiments are conducted and the results demonstrate that our proposed scheme significantly outperforms state-of-the-art HAR methods, achieving higher recognition accuracy and less training time.

Channel State Information (CSI) is widely used for device free human activity recognition. Feature extraction remains as one of the most challenging tasks in a dynamic and complex environment. In this paper, we propose a human activity recognition scheme using Deep Learning Networks with enhanced Channel State information (DLN-eCSI). We develop a CSI feature enhancement scheme (CFES), including two modules of background reduction and correlation feature enhancement, for preprocessing the data input to the DLN. After cleaning and compressing the signals using CFES, we apply the recurrent neural networking (RNN) to automatically extract deeper features and then the softmax regression algorithm for activity classification. Extensive experiments are conducted to validate the effectiveness of the proposed scheme.

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