Research Of Emotion Recognition Based On Pulse Signal

Emotion Recognition is not only the basis of artificial intelligence, but also a hot research issue in human-computer interaction (HCI). Emotion recognition based on physiological signals is more difficult and complicated to achieve than that based on facial expressions and speech; however, because physiological signals are produced by human body spontaneously, without conscious control of the subject, so the research process is more accurate and reliable. A fairly large physiological signal database is created by our lab and 7 kinds of physiological signals, including electrophysiological signals such as electrocardiogram (ECG), electroencephalogram (EEG), electroneuromyography (EMG), Galvanic Skin Response (GSR) and non-electrophysiological signals such as heart rate, pulse signal, respiration (RSP), under 6 emotion states, namely, happiness, surprise, disgust, grief, anger and fear.For quite a long time, pulse signal has been an important research issue; however, there are few prior research of emotion recognition based on pulse signal. Pulse signal aided with ECG signal are studied in this paper, and important features are extracted from the signals after filtering and reconstruction. The Improved Max-Min Ant System (IMMAS), combined with Fisher classifier is used for emotion recognition, and good effect is obtained.The following work is discussed in this paper:1. Signal preprocessing:wavelet packet transform is used to remove baseline shift and power-line interference of the signal and five-spot triple method is adopted for signal smoothing. 2. Percussion wave crest of pulse signal is positioned accurately through wavelet transform, and then three Gaussian functions are used to fit the percussion wave, tidal wave and dicrotic wave for feature extraction.3. Because there are a large number of original features, so intelligent algorithm is used for feature selection. In order to overcome the disadvantages of MMAS, such as slow rate of convergence and liable to trap in local optimum, Pseudo-random proportion, local search and variance strategy are introduced to MMAS and the improved MMAS (IMMAS) is obtained.4. Firstly, the 104 statistical features are used as the original feature subset, and IMMAS combined with Fisher classifier is adopted one-vs-one emotion recognition and one-vs-rest emotion recognition; then the optimal feature subsets selected by IMMAS combined with some new features are used to recognize happiness, surprise, disgust, grief, anger and fear once again, and better recognition rates are obtained.The experiment and simulation results show that it is feasible to use pulse signal for feature selection.