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Showing 2 results for Emotions

Atefeh Goshvarpour, Atalollah Abbasi, Ateke Goshvarpour,
Volume 19, Issue 7 (10-2016)
Abstract

Background: Individual differences, especially gender, have an important role on individuals responds to the emotions. In cognitive science investigations, the analysis of biological signals has been introduced as a confident way to evaluate such responses. In this paper, by adopting a comprehensive approach on biomedical signal processing techniques, a precise examination on women and men differences in affective responses has been provided into different emotional stimuli, including fear, sadness, happiness, and peacefulness.

Materials and Methods: Accordingly, signal processing methods were divided into three general categories, linear, wavelet, and non-linear based techniques. In the proposed method, different features from each of three categories and from three autonomic signals, including electrocardiogram (ECG), finger pulse, and galvanic skin response (GSR), were extracted. To induce emotions in participants, validated emotional pieces of music were broadcast in four affective classes.

Results: The results indicate the different patterns of responses into affective incentives in women and men. The differences were more noticeable in the features of pulse signal than those of the other signals. Among emotional classes, fear resulted in the highest rate of distinction between men and women emotional responses.

Conclusion: By the comprehensive evaluation of autonomic signals and different signal processing techniques, this study has tried to offer a new insight for better understanding of gender differences in emotional responses. In addition, it will help the researchers to adopt appropriate decisions in identifying efficient processing approach to deal with large amount of information achieved from signal analysis.


Seyed Sadegh Hosseini, Mohammad Reza Yamaghani,
Volume 27, Issue 4 (10-2024)
Abstract

Introduction: Nowadays, the use of artificial intelligence and machine learning has impacted all fields of study. Utilizing these methods for identifying individuals' emotions through integrating audio, text, and image data has shown higher accuracy than conventional methods, presenting various applications for psychologists and human-machine interaction. Identifying human emotions and individuals' reactions is crucial in psychology and psychotherapy. Emotional identification has traditionally been conducted individually and by analyzing facial expressions, speech patterns, or handwritten responses to stimuli and events. However, depending on the subject's conditions or the analyst's circumstances, this approach may lack the required accuracy. This paper aimed to achieve high-precision emotional recognition from audio, text, and image data using artificial intelligence and machine learning methods.
Methods: This research employs a correlation-based approach between emotions and input data, utilizing machine learning methods and regression analysis to predict a criterion variable based on multiple predictor variables (the emotional category as the criterion variable and the features, audio, image, and text variables as predictors). The statistical population of this study is the IEMOCAP dataset, and the data type of this research is a mixed quantitative-qualitative approach.
Results: The results indicated that combining audio, image, and text data for multi-modal emotional recognition significantly outperformed the recognition of emotions from each data alone, exhibiting a precision of 82.9% in the baseline dataset.
Conclusions: The results demonstrate a considerably acceptable precision in identifying human emotions through audio integration, text, and image data compared to individual data when using machine learning and artificial intelligence methods.

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