Emotion recognition based on double tree complex wavelet transform and machine learning in internet of things

Abstract

Corresponding to the continual development of human-computer interaction technology, the use of emotional computing (EC) is gradually emerging in the Internet of Things (IoT). Emotion recognition is considered a highly valuable aspect of EC. Numerous studies have examined emotion recognition based on electroencephalogram (EEG) signals, but the recognition rate is unreliable. In this paper, a feature extraction method is proposed that is based on double tree complex wavelet transform (DTCWT) and machine learning. The emotions of 16 subjects are induced under video stimulation, and the original signal is acquired using a Neuroscan device. Both EEG and electromyography (EMG) signal are then eliminated by band-pass filtering, and the reconstructed signal of each frequency band is obtained by DTCWT. Finally, support vector machine (SVM) is utilized to classify three kinds of emotions: calm, happy, and sad, obtaining a classification accuracy of 90.61%. Results show that the proposed algorithm can effectively extract the feature vector and improve the problem of low accuracy in multiple class recognition.