Human Emotion Recognition Based On Galvanic Skin Response signal Feature Selection and SVM
This work addresses emotion recognition for applications like healthcare or human-computer interaction, but it is incremental as it builds on existing GSR and SVM techniques.
The paper tackled human emotion recognition by proposing a method that uses automatically selected Galvanic Skin Response (GSR) signal features with SVM, achieving a recognition accuracy of over 66.67%.
A novel human emotion recognition method based on automatically selected Galvanic Skin Response (GSR) signal features and SVM is proposed in this paper. GSR signals were acquired by e-Health Sensor Platform V2.0. Then, the data is de-noised by wavelet function and normalized to get rid of the individual difference. 30 features are extracted from the normalized data, however, directly using of these features will lead to a low recognition rate. In order to gain the optimized features, a covariance based feature selection is employed in our method. Finally, a SVM with input of the optimized features is utilized to achieve the human emotion recognition. The experimental results indicate that the proposed method leads to good human emotion recognition, and the recognition accuracy is more than 66.67%.