• Journal of IKEEE
• /
• v.24 no.4
• /
• pp.998-1003
• /
• 2020

In this paper, an emotion training method using emotional adaptation is proposed to improve the performance of the existing emotion recognition system. For emotion adaptation, an emotion speech model was created from a speech model without emotion using a small number of training emotion voices and emotion adaptation methods. This method showed superior performance even when using a smaller number of emotional voices than the existing method. Since it is not easy to obtain enough emotional voices for training, it is very practical to use a small number of emotional voices in real situations. In the experimental results using a Korean database containing four emotions, the proposed method using emotional adaptation showed better performance than the existing method.

• Journal of The Korean Society of Integrative Medicine
• /
• v.6 no.2
• /
• pp.69-76
• /
• 2018

Purpose : This study investigated the effects of low-intensity muscle training on neurotransmitters associated with depression and emotion regulation disorders in survivors with stroke who were diagnosed with depression or emotion regulation disorder. Methods : Survivors of chronic stroke diagnosed with depression or emotion regulation disorder (n = ) were included in the study. Concentrations of serotonin, norepinephrine, and epinephrine were measured before and after. Low-intensity muscle training was performed once a day for 50 minutes, 5 times a week for 10 weeks. Result : Levels of serotonin, norepinephrine, and epinephrine improved significantly after intervention (p < .05). Conclusion : These results confirm that low-intensity muscle training has a positive effect on neurotransmitter concentrations in survivors with stroke diagnosed with depression and emotion regulation disorders. Future studies should analyze the changes in neurotransmitters in more detail using various exercise methods and intensities to investigate their effects on depression and emotion regulation disorders.

• Journal of the Korean Society of School Health
• /
• v.30 no.3
• /
• pp.295-305
• /
• 2017

Purpose: This study was conducted to evaluate the effect of Mindfulness-based Emotion Management Training for adolescents immersed in self-harm behaviors. Methods: The participants were 61 middle school students located in Seoul. Thirty students were assigned to the Mindfulness-based Emotion Management Training (M-EMT) group and the other 31 students were assigned to the waitlist group. The students in the M-EMT group participated in a total of 12 sessions, twice a week, for 6 weeks. The modules of the program comprised of mindfulness for emotion, mindfulness for behavior/impulse, mindfulness for thought, and coping with self-harming behavior. The Distress Tolerance Scale, Deliberate Self-Harm Inventory, Cognitive Emotion Regulation Questionnaire, and Scale Suicidal Ideation were assessed at baseline and post-intervention. Results: Compared to the waitlist group, the intervention group exhibited a statistically significant improvement in distress tolerance and cognitive emotion regulation and a statistically significant reduction in self-harming behaviors and suicidal ideation. Conclusion: These results support the hypothesis that mindfulness-based emotional management training has a positive effect on reducing self-harming behaviors and enhancing distress tolerance and cognitive emotional regulation.

• Journal of the Korea Computer Graphics Society
• /
• v.24 no.4
• /
• pp.1-9
• /
• 2018

We propose an emotional training framework that can determine the initial symptom of schizophrenia by using emotional analysis method through facial expression change. We use Emotion API in Microsoft to obtain facial expressions and emotion values at the present time. We analyzed these values and recognized subtle facial expressions that change with time. The emotion states were classified according to the peak analysis-based variance method in order to measure the emotions appearing in facial expressions according to time. The proposed method analyzes the lack of emotional recognition and expressive ability by using characteristics that are different from the emotional state changes classified according to the six basic emotions proposed by Ekman. As a result, the analyzed values are integrated into the image color transfer framework so that users can easily recognize and train their own emotional changes.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
• /
• 2012.05a
• /
• pp.132-133
• /
• 2012

• Anxiety and mood
• /
• v.18 no.1
• /
• pp.10-16
• /
• 2022

Objective : The present study examined the effectiveness of the mindful self-compassion (MSC) program on anxiety, depression, self-compassion and emotion regulation. Methods : A total of 29 subjects (mean age 27.5±6.5 years, 15 males and 14 females) participated in a standardized 8-week MSC program. The control group consisted of age- and sex-matched twenty participants (mean age 26.0±2.9 years, 11 males and 9 females). All subjects completed self-report measurements at two weeks before and after the MSC program. Results : MSC training improved self-compassion as demonstrated by the significant group x time interaction effects on the total Self-Compassion Scale scores (F[1, 47]=8.324, p<0.01). Regarding the subscale scores, a significant improvement in self-kindness, isolation and mindfulness components of self-compassion was observed after MSC training. A significant group x time interaction was observed on the self-kindness subscale (F[1, 47]=4.664, p<0.05), with a significant main effect of time (F[1, 47]=23.723, p<0.001). The isolation subscale showed a significant group x time interaction (F[1, 47]=8.698, p<0.001). For the mindfulness subscale, there was a significant group x time interaction (F[1, 47]=6.611, p<0.05) and main effect of time (F[1, 47]=6.611, p<0.05). MSC training also improved the acceptance emotion regulation strategy, as demonstrated by the significant group x time interaction in the acceptance subscale scores of the Cognitive Emotion Regulation Questionnaire (F[1, 47]=6.845, p<0.05). Conclusion : MSC training showed efficacy in fostering self-compassion and improving emotion regulation. Thus, this program might be applicable to improve mental health.

• Korean Journal of Child Studies
• /
• v.33 no.6
• /
• pp.1-19
• /
• 2012

The purpose of this study was to develop an intervention program for the improvement of the emotion regulation of 5-year-olds and to analyze the effects of this program on their emotion regulation and social behavior. The participants consisted of 56 5-year-olds from two day care center located in a metropolitan area of Korea. The experimental group (n=28) participated in the intervention program, and the comparative group (n=28) did not. This intervention program was developed to deal with peer relationships, and consists of activities designed to cover all domains of the Korean National Early Education Standard. The emotion regulation and social behavior were measured before and after the experiment. The data were analyzed making use of ANCOVA. Our results indicate that this program had the effect of improving the emotion regulation and social behavior on the part of 5-year-olds.

• Journal of the Korean Society of Physical Medicine
• /
• v.7 no.1
• /
• pp.125-135
• /
• 2012

Purpose : This study is designed to demonstrate the effects of circuit obstacle group gait training on walking ability and emotion in stroke patients. Methods : Twenty one patients with stroke were participated in this study. The subjects were divided into control group(n=10) and experimental group(n=11). Circuit obstacle group gait training consisted of walking around obstacles, walking over obstacles, walking up and down slopes and walking up and down stairs. Circuit obstacle group gait training was conducted five times per week, 1 hour per session, for 6 consecutive weeks. At pre-test and post-test, subjects were tested with 10 m walking test, timed up and go test, up and down 4 stairs test, depression and self esteem. Results : After 6 weeks of research, the experimental group showed statistically significant difference in all items when comparing prior to training and after training (p<.05), but the control group showed statistically significant difference in items other than depression and self esteem(p<.05). In the comparison between the two groups, the experimental group showed higher improvement than the control group in the 10 m walking test, timed up and go test, and up and down 4 stairs test, and there was statistically significant difference in decrease of degree in depression between the experimental group and control group(p<.05). Conclusion : This study have shown that circuit obstacle group gait training improves walking ability and emotion in stroke patients.

• Journal of IKEEE
• /
• v.22 no.3
• /
• pp.816-821
• /
• 2018

Recently, machine learning algorithms based on artificial neural networks started to be used widely as classifiers in the field of EEG research for emotion analysis and disease diagnosis. When a machine learning model is used to classify EEG data, if training data is composed of only data having similar characteristics, classification performance may be deteriorated when applied to data of another group. In this paper, we propose a method to construct training data set by selecting several groups of data using semi-supervised learning algorithm to improve these problems. We then compared the performance of the two models by training the model with a training data set consisting of data with similar characteristics to the training data set constructed using the proposed method.

• The Journal of the Acoustical Society of Korea
• /
• v.40 no.5
• /
• pp.515-522
• /
• 2021

It is hard to prepare sufficient training data for speech emotion recognition due to the difficulty of emotion labeling. In this paper, we apply transfer learning with large-scale training data for speech recognition on a transformer-based model to improve the performance of speech emotion recognition. In addition, we propose a method to utilize context information without decoding by multi-task learning with speech recognition. According to the speech emotion recognition experiments using the IEMOCAP dataset, our model achieves a weighted accuracy of 70.6 % and an unweighted accuracy of 71.6 %, which shows that the proposed method is effective in improving the performance of speech emotion recognition.