• Fire Science and Engineering
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• v.33 no.6
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• pp.132-141
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• 2019

This study was conducted to investigate the effects of a forest therapy program on post-traumatic stress disorder (PTSD) and mood states of fire fighters. A total of 293 participants completed two psychological questionnaires before and after the program was conducted: the Post Traumatic Stress Disorder Checklist (PCL) and the Profile of Mood States (POMS). Data were analyzed with paired t-test and ANCOVA using SPSS 24.0. The PTSD results showed a significant decrease from 11.38 ± 12.58 points before the program to 6.91 ± 10.50 points after the program. Results of the POMS questionnaire revealed an increase in positive factors and a decrease in negative factors, with a significant overall decrease in POMS results from 8.58 ± 18.47 points before the program to -0.63 ± 15.83 points after the program. As a result of analyzing the differences in stress reduction effects according to the amount of sleep participants had, PTSD showed improvement at 6-8 hours of sleep. These results are expected to be utilized as a basis for stress management and relief in fire fighters.

• Korean Journal of Cognitive Science
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• v.12 no.1_2
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• pp.77-89
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• 2001

Applying perceptual hierarchy of facial feature points, a neural network model for recognizing facial expressions was designed. Input data were convolution values of 150 facial expression pictures by Gabor-filters of 5 different sizes and 8 different orientations for each of 39 mesh points defined by MPEG-4 SNHC (Synthetic/Natural Hybrid Coding). A set of multiple regression analyses was performed with the rating value of the affective states for each facial expression and the Gabor-filtered values of 39 feature points. The results show that the pleasure-displeasure dimension of affective states is mainly related to the feature points around the mouth and the eyebrows, while a arousal-sleep dimension is closely related to the feature points around eyes. For the filter sizes. the affective states were found to be mostly related to the low spatial frequency. and for the filter orientations. the oblique orientations. An optimized neural network model was designed on the basis of these results by reducing original 1560(39x5x8) input elements to 400(25x2x8) The optimized model could predict human affective rating values. up to the correlation value of 0.886 for the pleasure-displeasure, and 0.631 for the arousal-sleep. Mapping the results of the optimized model to the six basic emotional categories (happy, sad, fear, angry, surprised, disgusted) fit 74% of human responses. Results of this study imply that, using human principles of recognizing facial expressions, a system for recognizing facial expressions can be optimized even with a a relatively little amount of information.

• Journal of Korean Academy of Nursing
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• v.43 no.5
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• pp.579-586
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• 2013

Purpose: This article provides an update and overview of a nursing research program focused on understanding the pathophysiology and management of irritable bowel syndrome (IBS). Methods: This review includes English language papers from the United States, Europe, and Asia (e.g., South Korea) from 1999 to 2013. We addressed IBS as a health problem, emerging etiologies, diagnostic and treatment approaches and the importance of a biopsychosocial model. Results: IBS is a chronic, functional gastrointestinal disorder characterized by recurrent episodes of abdominal pain and alterations in bowel habit (diarrhea, constipation, mixed). It is a condition for which adults, particularly women ages 20-45, seek health care services in both the United States and South Korea. Clinically, nurses play key roles in symptom prevention and management including designing and implementing approaches to enhance the patients' self-management strategies. Multiple mechanisms are believed to participate in the development and maintenance of IBS symptoms including autonomic nervous system dysregulation, intestinal inflammation, intestinal dysbiosis, dietary intolerances, alterations in emotion regulation, heightened visceral pain sensitivity, hypothalamic-pituitary-adrenal dysregulation, and dysmotility. Because IBS tends to occur in families, genetic factors may also contribute to the pathophysiology. Patients with IBS often report a number of co-morbid disorders and/or symptoms including poor sleep. Conclusion: The key to planning effective management strategies is to understand the heterogeneity of this disorder. Interventions for IBS include non-pharmacological strategies such as cognitive behavior therapy, relaxation strategies, and exclusion diets.

• ETRI Journal
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• v.44 no.3
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• pp.426-437
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• 2022

To understand the multilateral characteristics of human behavior and physiological markers related to physical, emotional, and environmental states, extensive lifelog data collection in a real-world environment is essential. Here, we propose a data collection method using multimodal mobile sensing and present a long-term dataset from 22 subjects and 616 days of experimental sessions. The dataset contains over 10 000 hours of data, including physiological, data such as photoplethysmography, electrodermal activity, and skin temperature in addition to the multivariate behavioral data. Furthermore, it consists of 10 372 user labels with emotional states and 590 days of sleep quality data. To demonstrate feasibility, human activity recognition was applied on the sensor data using a convolutional neural network-based deep learning model with 92.78% recognition accuracy. From the activity recognition result, we extracted the daily behavior pattern and discovered five representative models by applying spectral clustering. This demonstrates that the dataset contributed toward understanding human behavior using multimodal data accumulated throughout daily lives under natural conditions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.4
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• pp.722-727
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• 2009

The technology of distinguishing human motion states is required in the areas of measuring and analyzing biosignals changing according to physical activities, diagnosing sleep disorder, screening the effect of treatment, examining chronic patients' kinetic state, prescribing exercise therapy, etc. The present study implemented a fuzzy inference system based on fuzzy rules that distinguish human motion states (tying, sitting, walking, and running) by acquiring and processing data of LAA, TAA, L-MAD, and T-MAD using ADXL202AE of Analog Devices embedded in an armband. The membership degree and fuzzy rules in each area of input (LAA, TAA, L-MAD, and T-MAD) and output (tying, sitting, walking, and running) data used here were determined using numeric data obtained from experiment. In the results of analyzing data for simulation generated in order of tying$\rightarrow$walking$\rightarrow$running$\rightarrow$tying, the sorting rate for motion states tying, sitting, walking, and running was 100% for each motion.

• Journal of Industrial Technology
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• v.25 no.B
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• pp.211-220
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• 2005

The wireless sensor network consists of a number of sensor nodes which have physical constraints. Each sensor node senses surrounding environments and sends the sensed information to Sink. The inherent vulnerability in security of the sensor nodes has promoted the needs for the lightweight security protocol. In this paper, we propose a non-hierarchical sensor network and a security protocol that is suitable for monitoring the man-made objects such as bridges. Furthermore, we present the efficient way of setting the routing path by storing IDs, MAC(message authentication code) and the location information of the nodes, and taking advantage of the two node states, Sleep and Awake. This also will result in the reduced energy consuming rate.

• Korean Journal of Biological Psychiatry
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• v.4 no.2
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• pp.155-161
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• 1997

Serotonin has been implicated in the etiology of many disease states and may be particularly important mental illness, such as depression, anxiety, schizophrenia, sleep disorders, suicide, eating disorders, obsessive compulsive disorders, migraine and others. Many currently used treatments of these disorders are thought to act by modulating serotonergic function. The identification of many serotonin subtypes, most of which have been shown to have functional activity and differential distribution, has stimulated considerable effort into synthesizing selective ligands(drugs) to help understand their significance. This should understand the role of serotonin in mental disorders and these new drugs can be studied alone and in combination with other treatments in order to clarify the parameters of drug use for the clinical effect.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.126-132
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• 2000

This paper presents a facial expression recognition based on Gabor wavelets that uses a fuzzy C-means(FCM) clustering algorithm and neural network. Features of facial expressions are extracted to two steps. In the first step, Gabor wavelet representation can provide edges extraction of major face components using the average value of the image's 2-D Gabor wavelet coefficient histogram. In the next step, we extract sparse features of facial expressions from the extracted edge information using FCM clustering algorithm. The result of facial expression recognition is compared with dimensional values of internal stated derived from semantic ratings of words related to emotion. The dimensional model can recognize not only six facial expressions related to Ekman's basic emotions, but also expressions of various internal states.

• Sleep Medicine and Psychophysiology
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• v.6 no.2
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• pp.133-142
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• 1999

Recently, with increasing the number of patients with head injury and cerebrovascular accident, there has been an increasing need for the useful assessment tools of brain dysfunction and it's localization. With the advances in the neuroscience since the mid-1970s, particularly in the areas of neuroanatomical tracing, neuroimaging, and improved behavioraltest design, it has been possible to develop a more precise understanding and localization of brain dysfunction. However, these equipments are not readily available in the private clinics and too expensive to use as a screening tool to all suspected patients with brain dysfunction. Although several screening tests such as Mini-Mental States Examination(MMSE) or Brief Cognitive Rating Scale(BCRS) are simple in use and useful for the brief assessment of brain dysfunction, these are also limited in using for localization of brain dysfunction because of their simplicity. With increasing need of the assessment tool which is able to localize the dysfunction more precisely in the clinical practice, we planned to develop the new assessment tool, the Bedside Neurocognitive Function Localization Test(BNLT) which is suitable for this purpose. The BNLT was designed to be utilized for localizing brain dysfunction effectively and readily in the clinical practice. We introduced the whole process of designing the BNLT in this manuscript.

• Women's Health Nursing
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• v.20 no.1
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• pp.14-28
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• 2014

Purpose: This study was to identify relationships of maternal psychosocial factors including mother's mood state, childcare stress, social support and sleep satisfaction with breastfeeding adaptation and immune substances in breast milk, especially secretory immunoglobulin A (sIgA) and transforming growth factor-beta 2 (TGF-${\beta}2$). Methods: Data were collected from 84 mothers who delivered full-term infants by natural childbirth. Structured questionnaires and breast milk were collected at 2~4 days and 6 weeks postpartum. Data were analyzed using descriptive statistics, Pearson's correlation, multiple linear regression, and generalized estimating equation (GEE). Results: Scores for the breastfeeding adaptation scale were significantly related with child care stress, mood state and social support. Mother's anger was positively correlated with the level of sIgA in colostrum (p<.01). Immune substances of breastmilk was significantly influenced by time for milk collection (p<.001) and the type of breastfeeding (sIgA, p<.001, TGF-${\beta}2$, p=.003). Regression analysis showed that breastfeeding adaptation could be explained 59.1% by the type of breastfeeding, childcare stress, the Profile of Mood States, emotional support and sleep quality (F=16.67, p<.001). Conclusion: The findings from this study provide important concepts of breastfeeding adaptation program and explanation of psychosocial factors by immune substances in breast milk. Future research, specially, bio-maker research on breast milk should focus on the ways to improve breastfeeding adaptation.