A novel one-dimensional convolutional neural network-based method for emotion recognition of electric power industry workers

When the workers of electric power industry are going to perform high-risk or dispatching operations, it is important to assess the emotional state of the workers. Emotion recognition is helpful to justify whether the works are suitable to grasp and handle the operation status of the power system in time. In this paper, we develop a simple and efficient automatic emotion recognition method based on deep learning theory using ECG signals. Firstly, since Z-score method which is quite popular for emotion recognition exists high missed outlier detection rate, we define a basic beat and removed the abnormal beats according to the similarities between the truncated beats and the basic beat. Secondly, in order to make it easily for Convolutional Neural Network (CNN) to extract the periodicity features of one-dimensional signal in time domain, we present stacking operation of the truncated segments to increase the dimension of ECG data from one to three. Finally, aiming at decreasing the computation burden, a one-dimensional CNN only consisted of two convolution blocks is designed. The results of applying the proposed approach to the benchmark Wearable Stress and Affect Data set (WESAD) demonstrate that our proposal is capable of classifying four different kinds of emotions with an averaged accuracy of 98.57% and F1 value of 98.73%, respectively. Compared against some literature, the performance of our proposal is superior to them