Abstract
As the time is progressing the number of wireless devices around us is increasing, making Wi-Fi availability more and more vibrant in our surroundings. Wi-Fi sensing is becoming more and more popular as it does not raise privacy concerns in compare to a camera based approach and also our subject (human) doesn’t have to be in any special environment or wear any special devices (sensors).
Our goal is to use Wi-Fi signal data obtained using commodity Wi-Fi for human activity recognition. Our method for addressing this problem involves capturing Wi-Fi signals data and using different digital signal processing techniques. First we do noise reduction of our sample data by using Hampel filter then we convert our data from frequency domain into time domain for temporal analysis. After this we use the scalogram representation and apply the above mentioned steps to all our data in terms of sub carriers. Finally we use those sub carriers in combined for one activity sample as all the sub carriers combined form up an activity so we shall use the combined signal in the form of power spectrum image as input for the neural network.
We choose Alexnet for classification of our data. Before feeding our data into pre-trained CNN for training we first divided the data into two portions first for training which is 85% secondly for validation which is 15%. It took almost 18 h on single CPU and finally achieved an accuracy of above 90%.
This work is supported by NSFC Grants No. 61802299, 61772413, 61672424.
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Li, Y., Hussain, H., Yang, C., Hu, S., Zhao, J. (2019). High-Resolution Image Reconstruction Array of Based on Low-Resolution Infrared Sensor. In: Li, Q., Song, S., Li, R., Xu, Y., Xi, W., Gao, H. (eds) Broadband Communications, Networks, and Systems. Broadnets 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 303. Springer, Cham. https://doi.org/10.1007/978-3-030-36442-7_8
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