An efficient 3D convolutional neural network with informative 3D volumes for human activity recognition using wearable sensors‏

Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) are two popular transforms which can be used to find time‐frequency representations. By using them, one-dimensional signals acquired from different axes or sensors are mapped to time–frequency representations. These representations can construct 3D volumes which include time–frequency information of signals. Recently, the advantage of 3D convolutional neural networks (3D-CNNs) for video classification causes to incorporate them with the 3D volumes. Based on this opinion, a novel method composed of two basic methods is proposed in this paper. The magnitude of the STFT and the CWT are used for constructing 3D volumes in basic methods. Also, a developed 3D-CNN is applied for classifying. Two streams of these 3D volumes are fused in the proposed method. It attains the accuracies of 96.61%, 97.77%, 99.65% and 98.32% for UCI HAR, MOTIONSENSE, MHEALTH and WISDM datasets, respectively. Achieved results demonstrate the superiority of the proposed method compared with state-of-the-art approaches.