Abstract
At present, most Wi-Fi based sensing researches aim at single target scene, due to the difficulties in separation of mixed signals. In this paper, a Wi-Fi based model for multi-target activity recognition is proposed. A diverse dataset of sufficient volume for multi-target activity recognition is first collected in our paper. After blind source separation algorithm (FastICA) processing, the dataset is input to the proposed signal sort algorithm named CC-ICA for efficient and accurate signal sort according to CSI correlation coefficient. Experimental results show that CC-ICA algorithm can effectively solve the problem of random order caused by FastICA. Separated CSI data is input into a neural network consisting of ABiGRU and TCN for training and multi-target recognition evaluation. The experiments demonstrate that accuracy of WiMTAR is improved by 26% after CSI data is processed by CC-ICA for multi-target recognition, and accuracy of WiMTAR is also more than 2.6% higher than that of other single target recognition schemes.
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Duan, P., Li, C., Jiao, C., Zhang, W., Kong, J. (2022). WiMTAR: A Contactless Multi-target Activity Recognition Model. In: Calafate, C.T., Chen, X., Wu, Y. (eds) Mobile Networks and Management. MONAMI 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-94763-7_13
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DOI: https://doi.org/10.1007/978-3-030-94763-7_13
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