Abstract
In this paper, we investigate device-free fall detection based on wireless channel state information (CSI). Here, we mainly propose a method that uses continuous wavelet transform (CWT) to generate images and then uses transform learning of convolutional networks for classification. In addition, we add a wavelet scattering network to automatically extract features and classify them using a long and short-term memory network (LSTM), which can increase the interpretability and reduce the computational complexity of the system. After applying these methods to wireless sensing technology, both methods have a higher accuracy rate. The first method can cope with the problem of degraded sensing performance when the environment is not exactly the same, and the second method has more stable sensing performance.
Y. Chen and Y. Wei—Contribute equally to this work.
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Acknowledgement
The work is supported by the National Key Research and Development Program of China under grant 2018YFB0204301, the National Natural Science Foundation (NSF) under grant 62072306, Open Fund of Science and Technology on Parallel and Distributed Processing Laboratory under grant 6142110200407.
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Chen, Y., Wei, Y., Pang, D., Xue, G. (2022). A Deep Learning Approach Based on Continuous Wavelet Transform Towards Fall Detection. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13472. Springer, Cham. https://doi.org/10.1007/978-3-031-19214-2_17
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