• Journal of the Korea Society of Computer and Information
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• v.28 no.3
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• pp.35-43
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• 2023

Emotion recognition while driving is an essential task to prevent accidents. Furthermore, in the era of autonomous driving, automobiles are the subject of mobility, requiring more emotional communication with drivers, and the emotion recognition market is gradually spreading. Accordingly, in this research plan, the driver's emotions are classified into seven categories using psychological and behavioral data, which are relatively easy to collect. The latent vectors extracted through the auto-encoder model were also used as features in this classification model, confirming that this affected performance improvement. Furthermore, it also confirmed that the performance was improved when using the framework presented in this paper compared to when the existing EEG data were included. Finally, 81% of the driver's emotion classification accuracy and 80% of F1-Score were achieved only through psychological, personal information, and behavioral data.

• Science of Emotion and Sensibility
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• v.21 no.1
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• pp.177-186
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• 2018

The mechanism of emotion is complex and influenced by a variety of factors, so that it is crucial to analyze emotion in broad and diversified perspectives. In this study, we classified neutral and negative emotions(sadness, fear, surprise) using arousal evaluation, which is one of the psychological evaluation scales, as well as physiological signals. We have not only revealed the difference between physiological signals coupled to the emotions, but also assessed how accurate these emotions can be classified by our emotional recognizer based on neural network algorithm. A total of 146 participants(mean age $20.1{\pm}4.0$, male 41%) were emotionally stimulated while their physiological signals of the electrocardiogram, blood flow, and dermal activity were recorded. In addition, the participants evaluated their psychological states on the emotional rating scale in response to the emotional stimuli. Heart rate(HR), standard deviation(SDNN), blood flow(BVP), pulse wave transmission time(PTT), skin conduction level(SCL) and skin conduction response(SCR) were calculated before and after the emotional stimulation. As a result, the difference between physiological responses was verified corresponding to the emotions, and the highest emotion classification performance of 86.9% was obtained using the combined analysis of arousal and physiological features. This study suggests that negative emotion can be categorized by psychological and physiological evaluation along with the application of machine learning algorithm, which can contribute to the science and technology of detecting human emotion.

• Science of Emotion and Sensibility
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• v.16 no.3
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• pp.409-416
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• 2013

Attention is one of important cognitive functions in human affecting on the selectional concentration of relevant events and ignorance of irrelevant events. The discrimination of attentional and inattentional status is the first step to manage human's attentional capability using computer assisted device. In this paper, we newly combine the non-linear recurrence pattern analysis and spectrum analysis to effectively extract features(total number of 13) from the electroencephalographic signal used in the input to classifiers. The performance of diverse types of attention-inattention classifiers, including supporting vector machine, back-propagation algorithm, linear discrimination, gradient decent, and logistic regression classifiers were evaluated. Among them, the support vector machine classifier shows the best performance with the classification accuracy of 81 %. The use of spectral band feature set alone(accuracy of 76 %) shows better performance than that of non-linear recurrence pattern feature set alone(accuracy of 67 %). The support vector machine classifier with hybrid combination of non-linear and spectral analysis can be used in later designing attention-related devices.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.334-342
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• 2022

Nowadays, as we can notice on social media, most users choose to use more than one language in their online postings. Thus, social media analytics needs reviewing as code-switching analytics instead of traditional analytics. This paper aims to present evidence comparable to the accuracy of code-switching analytics techniques in analysing the mood state of social media users. We conducted a systematic literature review (SLR) to study the social media analytics that examined the effectiveness of code-switching analytics techniques. One primary question and three sub-questions have been raised for this purpose. The study investigates the computational models used to detect and measures emotional well-being. The study primarily focuses on online postings text, including the extended text analysis, analysing and predicting using past experiences, and classifying the mood upon analysis. We used thirty-two (32) papers for our evidence synthesis and identified four main task classifications that can be used potentially in code-switching analytics. The tasks include determining analytics algorithms, classification techniques, mood classes, and analytics flow. Results showed that CNN-BiLSTM was the machine learning algorithm that affected code-switching analytics accuracy the most with 83.21%. In addition, the analytics accuracy when using the code-mixing emotion corpus could enhance by about 20% compared to when performing with one language. Our meta-analyses showed that code-mixing emotion corpus was effective in improving the mood analytics accuracy level. This SLR result has pointed to two apparent gaps in the research field: i) lack of studies that focus on Malay-English code-mixing analytics and ii) lack of studies investigating various mood classes via the code-mixing approach.

• Science of Emotion and Sensibility
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• v.25 no.4
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• pp.63-76
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• 2022

Emotional labor, characterized by a dysfunctional type of emotional regulation called surface acting, has detrimental psychological consequences on employees, including depression and social anxiety. Because such disorders exhibit psychological characteristics manifested through brain activation, previous studies have succeeded in distinguishing individuals with depression and social anxiety from healthy controls using their functional connectivity characteristics. However, it has not been established whether the functional connectivity characteristics associated with emotional labor are distinguishable. Thus, we obtained resting-state fMRI data from participants in the emotion labor (EL) group and control (CTRL) group, and we subjected their whole-brain functional connectivity matrices to a linear support vector machine classifier. Our analysis revealed that the EL and CTRL groups could be successfully distinguished on the basis of individuals' connectivity patterns, and confidence in the classification was correlated with the scores on the depression and social anxiety scales. These results are expected to provide insight on the neurobiological characteristics of emotional labor and enable the sorting of employees undergoing adverse emotional labor utilizing neurobiological observations.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2001.05a
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• pp.20-25
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• 2001

In recent years, the concern about a user center design in increasing, and it's needed to task a user's visual cognitive characteristics for information presentation. Then this study aims to grasp user's cognitive characteristics about the information presentation by analyzing the fixation points. In the experiment, actually subject operated a copy machine. Recorded the fixation point movement of the operation panel by the eye mark recorder. Analysis examined the screen interface of the operation panel from the field of a fixation point trace. The top down type fixation oder by experience or the context became clear as a result. Furthermore, the difference of the fixation order by skill level was also examined. In this study, it was assumed that to grasp the visual cognitive characteristics becomes the key of efficient information.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.9
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• pp.825-831
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• 2007

In this paper, an interface between a human and a computer is presented. The human and computer interface(HCI) serves as another area of human and machine interfaces. Methods for the HCI we used are voice recognition and image recognition for detecting human's emotional feelings. The idea is that the computer can recognize the present emotional state of the human operator, and amuses him/her in various ways such as turning on musics, searching webs, and talking. For the image recognition process, the human face is captured, and eye and mouth are selected from the facial image for recognition. To train images of the mouth, we use the Hopfield Net. The results show 88%$\sim$92% recognition of the emotion. For the vocal recognition, neural network shows 80%$\sim$98% recognition of voice.

• Science of Emotion and Sensibility
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• v.10 no.3
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• pp.367-372
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• 2007

This study surveys the silk sensitivity technology of nylon filament using fluid system. For this purpose, taslan texturing ATY m/c is modified and 4 kinds of nylon filaments with 40d/12f are made using functional chemicals on the modified ATY texturing machine. Using these yarns, 4 kinds of fabrics are woven and processed on the dyeing and finishing. The various physical properties of these fabrics such as water contents, UV-cut, fabric hand are measured and discussed with ATY texturing and functional chemical treatment conditions.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.30-33
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• 2002

There are several methods to implement the logical machine reasoning such as a frame theory and a production system of artificial intelligence. And these algorithms can explain the obtained result through the inference processes. However, emotional (KANSEI) patterns are not so easily implement. One of reason is that some emotional expression is the result of process from unconscious level to conscious level, and not easily identified the original unconscious causes. Therefore, a function of KANSEI database needs to structuralize unconscious level. Our approach is to develop the computerized counseling support system which can structuralize the unconscious brain functions from the view point of the psychology with focusing physiological and emotional responses. Especially, development of the algorithm that can form the network from unconscious to conscious using the image recollection is the application of the structured association technique (SAT). The developed system was implemented on the Web using CGI and emotional network database.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2008.10a
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• pp.42-45
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• 2008

Derivation of modern products from the by gone age has contributed much for the new modern living. It has been generally recognized that the various ethnicities in Borneo with different backgrounds had made Borneo a place of cultural diversity. However as time passed, most indigenous products are no longer in used or are stored in poor condition. Most products nearly aged over a century are still in a good condition with invaluable sentiments. Indigenous product is an artifact that had been designed and used by certain community of people such as tools, clothing, crafts and goods. Each design may have its own identity to the community. Some of the indigenous products which are no longer in use at present are kept by the community as their collections. The research reveals similarities in the interests of indigenous products, concerns and realities of indigenous communities from the different regions. The study suggests that learning about indigenous materials, such as hand-made products and machine-made products is necessary for the local industry to develop a product identity that is distinctly local. Most indigenous products show evidence of connections to old traditions, yet are new to the design market.