A Deep Learning Approach Based on Continuous Wavelet Transform Towards Fall Detection

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.