WiWave: WiFi-based Human Activity Recognition Using the Wavelet Integrated CNN

Nowadays, WiFi-based human activity recognition (HAR), as a key enabler of building smart home, has gained tremendous attention because of its superior properties such as privacy protection and low-cost deployment. Since each human motion within the signal coverage would cause different wireless channel disturbances, it is possible to identify and interpret these activity-induced signal changes for human behavior recognition. Although many approaches attempt to extract distinct patterns from WiFi measurements corresponding to user activities, the signals can be easily attenuated due to environmental variations in the real settings, so that their recognition accuracy may be severely deteriorated. In order to extract the key features in a more distinguished way, in this paper, we propose WiWave, a WiFi-based device-free HAR system leveraging wavelet integrated convolutional neural network (CNN). Instead of utilizing pooling operations, our proposed network has introduced discrete wavelet transform (DWT) into the convolutional architectures, which can combine the good time-frequency local characteristics of the wavelet transform with the self-learning ability of the neural network. Consequently, not only high-level features from low-frequency components can be obtained automatically, but also the the size of feature map can be reduced. The experiment results demonstrate that WiWave achieves average 94.87% accuracy for distinguishing ten actions in real-world home environment.