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Emotion recognition based on physiological signals has become more and more prevalent in the field of affective computing. Through the collection and analysis of electrocardiography (ECG), a reasonable method about evolution strategies has been improved to feature selection. With the recognition of two emotions on joy and sadness, effective features which can be representative of some emotion are...
Emotion recognition is becoming an increasingly important field for human affective computing. This paper presented a method of emotion recognition using electrocardiography (ECG) signal obtained from multiple subjects. ECG signals were collected when film clips shown to subjects. Through denoising and location of P-QRS-T wave by wavelet transform, ECG features could be extracted effectively. For...
Emotion recognition from electrocardiography (ECG) signal has become an important research topic in the field of affective computing. In the current work, ECG signals from multiple subjects were collected when film clips shown to them, and then feature sets were extracted from precise location of P-QRS-T wave by continuous wavelet transform (CWT). Hybrid particle swarm optimization (HPSO) was utilized...
Electrocardiography (ECG) is one of the most important physiological signals, whose changes can reflect the changes in emotional states in some degree. Raw ECG data were recorded when film clips were used to elicit target emotions (joy and sadness) of multiple subjects. Wavelet transform was applied to accurately detect QRS complex for its advantages on time-frequency localization, in order to extract...
Electrocardiography (ECG) data acquisition, data preprocessing, feature extraction and emotion recognition based on ECG feature classification were effectively implemented. Joy and sad movies were selected and presented to 154 subjects whose ECG data were recorded at the movie presentation time. The automatic location of QRS complex, which is of critical importance for ECG feature extraction by the...
For the problem of emotion recognition of physiological signals, an introduction to adaptive hierarchical genetic algorithm is put forward. With the help of feature selection to the physiological signals, the optimization problem can easily be solved. Itpsilas shown in the simulation that using the features obtained from the experimental simulation to recognize the emotion states is effective.
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