Small CSI Samples-based Activity Recognition

With the wide application of WiFi technology,human activity recognition technology based on device-free has been applied to many fields such as smart home,elderly monitoring and public safety.Human activity recognition can be divided into wearable devices-based and device-free-based recognition technologies.The former requires the target to be equipped with wearable devices,which is inconvenient and increases the cost.The latter does not require the target to carry any devices.Therefore,the device-free activity recognition technology has become the main research direction in this field.At present,CSI-based human activity recognition technology can effectively recognize coarse-grained activities and finer-grained activities,such as breath detection.However,the existing CSI-based activity recognition algorithms need a large number of training samples to obtain the ideal recognition accuracy.To solve the problem,an attention-based bidirectional LSTM method using multidimensional features(called MF-ABLSTM method)is proposed.In this method,the signal preprocessing and continuous wavelet transform algorithms are used to construct the time-frequency matrix,and the sample entropy is used to characterize the statistical feature of CSI amplitudes,and the energy difference at a fixed time interval is used to characterize the time-domain feature of activities,and the energy distribution of different frequency components is used to characterize the frequency-domain feature of activities.By expanding the training samples with the proposed tensor prediction algorithm,the accurate activity recognition can be realized with only a few samples.A large number of experiments verify the good performance of MF-ABLSTM method.