Abstract
With the emergence of tools for extracting CSI data from commercial WiFi devices, CSI-based device-free activity recognition technology has developed rapidly and has been widely used in security monitoring, smart home, medical monitoring, and other fields. 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 time-frequency matrix, the sample entropy is used to characterize the statistical feature of CSI amplitudes, 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.
Highlights
For the problem of small sample activity recognition proposed in this paper, firstly, we use the statistical features of CSI amplitude and time-frequency domain features to construct the feature matrix for the input of ABLSTM network, based on the method of literature [13]. us, our multidimensional feature ABLSTM network can extract human target activity features from the more directional data and classify the activities more accurately
When the training samples are 10, the recognition accuracy of MFABLSTM algorithm proposed in this paper is higher than that of ABLSTM(40) and is increased steadily after the proposed tensor prediction algorithm expands the training samples, which verifies the effectiveness of the method proposed in this paper to solve the problem of small samples
The existing research still needs a large number of training samples to obtain the ideal recognition accuracy
Summary
The activity recognition technology has developed rapidly and has been widely used in smart home, medical care, safety monitoring, and other fields. e activity recognition can be divided into wearable device [1] and device-free-based recognition technologies. e former requires the target to be equipped with wearable devices, which is inconvenient and increases the cost. e latter does not require the target to carry any devices. erefore, the devicefree activity recognition technology has become the main research direction in this field. Erefore, how to use a small number of training sample sets (i.e., small samples) to achieve high accuracy human target activity recognition is an urgent problem to be solved in the deep learning method. For the problem of small sample activity recognition proposed in this paper, firstly, we use the statistical features of CSI amplitude and time-frequency domain features to construct the feature matrix for the input of ABLSTM network, based on the method of literature [13]. (1) To reduce the cost of collecting training samples, a method to generate training samples by using tensor prediction is proposed, which can generate a large number of training samples with similar characteristics as a small number of training samples and improve the activity recognition accuracy of deep learning methods.
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