Human Activity Recognition Using Wearable Sensors Based on Image Classification

Two-Dimensional Fast Fourier Transform (2-D FFT) and Wigner-Ville Transform (WVT) are two popular transforms applied to find frequency and time-frequency representations, respectively. By using them, signals acquired from different axes or sensors are mapped to these representations, considered 2D images. Based on this opinion, three novel methods are presented in this paper. The first two methods are called basic methods. The 2D images based on the magnitude of the 2-D FFT are constructed in method 1 and a fine-tuned CNN is also applied for classifying. WVT is used for constructing 2D compressed images in method 2, and classifying is done like method 1. Fusing two basic methods is presented as the proposed method. It attains the accuracies of 93.45%, 96.47%, 99.00%, and 98.20% for UCI HAR, MOTIONSENSE, MHEALTH, and WISDM datasets, respectively. Achieved results show the superiority of the proposed method compared with the state-of-the-art approaches.