• Child Studies in Asia-Pacific Contexts
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• v.4 no.1
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• pp.1-12
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• 2014

Emotion is a central feature of social interactions. In this study, we examined age-related changes in emotion expressiveness and emotion knowledge and how young children's emotion expressiveness and knowledge were related. A total of 300 children attending a daycare center contributed data for the study. Observation and interview data relevant to measures of emotion expressiveness and knowledge were collected and analyzed. Both emotion knowledge and expressed positive affect increased with age. Older preschool children expressed positive affect more frequently than did younger preschoolers. Older preschool children also labeled, recognized, and provided plausible causes mores accurately than did younger preschool children. In addition, we tested whether children's errors on the free labeling component conform to the structural model previously suggested by Bullock and Russell (1986) and found that preschool children were using systematic strategies for labeling emotion states. Relations between emotion expressiveness and emotion knowledge generally were not significant, suggesting that emotional competence is only gradually constructed by the child over the preschool years.

• The Korean Journal of Emergency Medical Services
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• v.8 no.1
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• pp.47-55
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• 2004

The purpose of this study were to investigate the effect of meditative respiration training for 6weeks on the changes of ACTH and Cortisol which are stress hormones in 10 male workers. The results of this study were as follows : 1. After meditative respiration training for 6weeks, the changes of heart rate, respiratory rate, SBP, DBP were slightly decreased. 2. After meditative respiration training for 6weeks, the changes of ACTH hormone were significantly decreased(p<.01). 3. After meditative respiration training for 6weeks, the changes of cortisol hormone were significantly decreased(p<.05). The above conclusions suggested that short-term meditative respiration training was an effective training method to changes mental emotional states and physiological stress hormone level affirmatively. Further, the future researches must analyze the physiological and psychological characteristics affecting mental health synthetically and develop meditative respiration program suitable to the various items and classes, especially EMT.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.2010-2012
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• 2008

This study describes a system for human sensibility evaluation using PPG(photoplethysmogram) signal and biofeedback algorithm to respond the bad(negative) mood. For this objective, PPG signals for two emotional states(positive/negative) are collected. To evoke the test emotions, happy(or joyful) and sad(or irritating) movie files are collected and played in subjects' monitor. From the acquired PPG signal, the heart rate variability(HRV) is calculated. Using the HRV and its FFT spectra, the human sensibility is evaluated. Since the heart is a representative organ which is controlled by the autonomic nervous system(ANS), the ANS may reflect the changes in emotion. The biofeedback algorithm is designed with motion image player interacting with the results of the sensibility evaluation. It was shown that HRV was changed according to the subject's emotions. Accordingly, the sensibility evaluation test showed feasibility of the our method.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.340-342
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• 2001

현재 인터넷 환경에서 텍스트는 다루기 쉽고 부하가 적어 가장 많이 사용되는 통신 수단이다. 그러나 화상 채팅과는 달리 자신의 표정이나 체스춰를 전달할 수 있는 방법이 없기 때문에 표현상의 한계가 있다. 이 글은 일상 대화를 텍스트로 입력받아, naive Bayes 알고리즘을 사용해 미리 정의된 감정 범주, 즉 울기, 웃기, 화내기 등으로 분류해 주는 방법에 관해 다루고 있다. 채팅사이트에서 수집된 학습데이터는 사람에 의해 해당 감정 범주로 태깅되고, 이렇게 태깅된 데이터가 학습엔진에 의해 통계 정보로 구축되면, 실제 채팅사이트에서 감정인식 엔진은 입력된 데이터를 분석해 해당 감정으로 분류한다. 연령별로 5개의 그룹으로 나눈 대화방에서 각각 1000문장씩 테스트해본 결과 평균 91.6%의 정확도를 얻을 수 있었다.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1998.04a
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• pp.63-69
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• 1998

Korean constitutional type(Sasang Chejil) was considered with anxiety level and personality type in order variability frequently encountered in EEG research. Sasang Chejil type seems a very important factor to be controled to minimize the variabilty since Sasang Cheji subgroups showed distinct differences in EEG relative power ao more recording sites and frequency bands than the subgroups of anxiety level and personality type, well-reconized variability-contribution factors. It is likely that Tae-Eum Chejil type is predispositioned to be more relaxed and feel happier and less unhappy than So-Yang type since the former showed greater alpha relative powers and left-hemisphere activations at both of the resting and emotional states.

• Annual Conference on Human and Language Technology
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• 2008.10a
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• pp.145-150
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• 2008

본 논문에서는 8개의 문장에 대해서 6명의 화자가 5가지 감정 상태로 발화한 총 240개의 문장을 감정 음성 말뭉치로 활용하여 각 감정 상태에서 특징적으로 나타나는 억양 패턴을 분석하고, 이러한 억양 패턴을 음성 합성 시스템에 적용하는 방법에 대해서 논의한다. 이를 위해 본 논문에서는 감정 상태에 따른 특징적 억양 패턴을 억양구의 길이, 억양구의 구말 경계 성조, 하강 현상에 중점을 두어 분석하고, 기쁨, 슬픔, 화남, 공포의 감정을 구분 지을 수 있는 억양 특징들을 음성 합성 시스템에 적용하는 과정을 보인다. 본 연구를 통해 화남의 감정에서 나타나는 억양의 상승 현상을 확인할 수 있었고, 각 감정에 따른 특징적 억양 패턴을 찾을 수 있었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.391-394
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• 2011

사용자의 감정을 자동으로 인식하는 연구는 사용자 중심의 서비스를 제공할 때 중요한 요소이다. 인간은 하나의 감정을 다양하게 분류하여 인식한다. 그러나 기계학습을 통해 감정을 인식하려고 할 때 감정을 단일값으로 취급하는 방법만으로는 좋은 성능을 기대하기 어렵다. 따라서 본 논문에서는 비감독 학습과 감독학습을 결합한 감정인식 모델을 제시하였다. 제안된 모델의 핵심은 비감독 학습을 이용하여 인간처럼 한 개의 감정을 다양한 하부 감정으로 분류하고, 이렇게 분류된 감정을 감독학습을 통해 성능을 향상 시키는 것이다.

• Smart Media Journal
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• v.13 no.7
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• pp.18-26
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• 2024

One of the newest trends in AI is emotion recognition utilizing keystroke dynamics, which leverages biometric data to identify users and assess emotional states. This work offers a comparison of four datasets that are frequently used to research keystroke dynamics: BB-MAS, Buffalo, Clarkson II, and CMU. The datasets contain different types of data, both behavioral and physiological biometric data that was gathered in a range of environments, from controlled labs to real work environments. Considering the benefits and drawbacks of each dataset, paying particular attention to how well it can be used for tasks like emotion recognition and behavioral analysis. Our findings demonstrate how user attributes, task circumstances, and ambient elements affect typing behavior. This comparative analysis aims to guide future research and development of applications for emotion detection and biometrics, emphasizing the importance of collecting diverse data and the possibility of integrating keystroke dynamics with other biometric measurements.

• 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.

• Sleep Medicine and Psychophysiology
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• v.25 no.2
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• pp.58-67
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• 2018

Objectives: The purpose of this pilot study was to examine the effects of a mindfulness-based Body-Mind Intervention Program using marine resources on the improvement of quality of sleep, mood symptoms, and cognitive function in Korean female emotional labor workers. Methods: Twelve female workers who experienced excess emotional labor participated at the training camp program for five days in Danghangpo-ocean park, Goseung-gun, Gyeongnam Province in South Korea. Participant sleep quality, mood symptoms, and cognitive functioning before, after, and 1.5 months later were evaluated and analyzed. Results: After participating in the marine resource program, participants reported significantly decreased sleep latency. Global sleep quality, cognitive functions (attention, flexibility, and inhibition control), and mood states, including depression, tension, anger, fatigue, were also improved. These effects were generally maintained after 1. 5 months (PSQI t = 2.63, p = 0.02 ; HAM-D t = 5.92, p < 0.001). Conclusion: A Body-Mind Intervention Program using marine resources was effective in relaxing emotion-related tension and improving cognitive function. To advance this pilot study, it is necessary to carry out further research to investigate the use of marine resources in mental health interventions.