Multi-channel Emotion Recognition Based On GSR

Sentiment and personality play an important role in people’s daily life such as cognition,creativity,attention,decision-making,mental health and so on.Positive emotions can promote individuals to speed up learning and work efficiency,and make interpersonal communication more harmonious,while negative emotions are not conducive to individual development.With the acceleration of the pace of life and the increase in the cost of living,the individual’s living pressure and workplace pressure continue to rise.Researchers have made significant progress in emotion recognition through text,image,and language modeling.Physiological signals are the carrier of information transmitted by human tissues and organs to the brain.Physiological signals contain a large amount of personal physiological and psychological information,which is not concealable or deceivable.Deep learning modeling based on physiological signal data is a popular direction in the field of computer research.This article introduces sentiment calculation and personality detection of physiological signals,including concepts,research background and significance,research topics and challenges,and research status.Based on the multimodal data set,deep learning emotion classification modeling and personality detection modeling are performed.Main tasks are as follows:(1)Physiological signal data set: A real-time emotional personality multi-modal data set including skin resistance,pulse,image,voice and otherchannels was collected based on emotionally excited video.(2)For physiological signals,a method for converting skin resistance data intoa spectrogram is proposed.(3)For physiological signals,apply data augmentation techniques tophysiological signals and spectrograms.(4)Oriented to affective computing,the traditional machine learning modelsbased on skin resistance and convolutional neural network models,long-term and short-term memory network models,and hybrid spatio-temporalnetwork models are established.(5)A multimodal deep learning model based on skin resistance and pulse isestablished.(6)For personality detection,a skin resistance-based machine learning modeland a deep learning model are established.Through data modeling,the skin resistance signal proposed in this paper has achieved good results.The deep learning model based on skin resistance has improved the emotion classification results.And found that data augmentation technology and attention mechanism play a positive role in sentiment classification and personality detection.Through the analysis of the experimental results,the personality regression and classification based on skin resistance proposed in this paper achieved good results.Deep learning models based on skin resistance and pulse can effectively classify user emotions,and provide a research basis for judicial interrogation,smart home,and emotional guidance.Personality classification and regression models based on skin resistance have made progress in experimental results,which can help in mental health diagnosis,aerospace,autism and elderly care.