GAN-based Radar Micro-Doppler Augmentation for High Accuracy Fall Detection System

Convolution Neural Network (CNN) is one of the powerful deep learning tools used in many computer vision tasks; however, it is still in a premature state while dealing with sensor data due to the unavailability of a large data set. Here in this paper, we present a CNN-based fall detection system. Deep convolution generative adversarial network (DCGAN) is used to synthesize more fall data before analysis. Sensor data along with synthesized data is prepossessed, and time-varying spectrograms are derived. We apply CNN on raw spectrogram images of different activities to categorize a fall from other activities. As a result, we successfully attain a classification accuracy of 97.2%, surpassing the conventional machine learning methods and previous works on the same data by a large margin.