• International Journal of Advanced Culture Technology
• /
• v.6 no.3
• /
• pp.12-21
• /
• 2018

This study was a cross-sectional survey of the effects of pregnancy necessity recognition on emotional happiness and mediation effect of health control behavior on it. A total of 200 participants in the study were collected from structured questionnaire online and the data collection was from July $1^{st}$ to July $31^{st}$, 2018. Health control behavior questionnaire was developed by Wallston, K.A., Wallston, B.S. & Devellis, R (1978), Emotional happiness was analyzed by using PANAS (positive and negative affect schedule) developed by Watson, Clark and Tellegen (1988). The collected data were chai-square($X^2$), Pearson correlation, Dummy regression analysis, simple regression analysis, and the mediated effect analysis by SPSS 18.0. As a result, Under statistical significance, there were differences in the recognition of pregnancy necessity were depending on religion, participant's age, number of siblings, thought of optimal marriage age(p<0.05). More siblings, more religious, older age, and more recognized the pregnancy necessity. The analysis of Pearson correlation with the pregnancy necessity, health control behavior, and emotional happiness reveled that it was relevant (p<0.01). Dummy regression analysis showed that people who thought that pregnancy was necessary were 0.700 times more likely to felt emotional happiness that people who thought it was unnecessary (p<0.01). Analysis on the mediation of health control behavior, in which the effects of pregnancy recognition on emotional happiness, showed that it was effect (other people's health control behavior: B:.299, p<0.01, internal health control behavior : B:.217, p<0.05). Based on these results, this study suggested that to promote pregnancy recognition, families with brother and sister should be programmed with recommendations for exercise and alcohol abstinence, religious belief and health control programs.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.12
• /
• pp.128-137
• /
• 2018

Drawing on costly signaling theory and self-enhancement motive, this study examines the relationships among proactive personality, knowledge sharing behavior, and organizational recognition. In addition to the individual characteristic, this study considers job characteristics as conditional factors, and especially proposes the moderated mediation model in which job complexity and variety moderate the relationships among proactive personality, knowledge sharing behavior, and organizational recognition. To prove these hypotheses, empirical analyses are conducted with 166 dyad samples collected from various industries. As predicted, individuals with high proactive personality are more likely to become involved in knowledge sharing behavior, and this behavior increases organizational recognition rated by their supervisors. Moreover, job complexity and variety moderate the positive relationship between proactive personality and organizational recognition is mediated by knowledge sharing behavior. These results reveal the motive in knowledge sharing and the boundary condition that is necessary to increase such behavior. The study findings will ultimately contribute theoretical and empirical implications to the knowledge management literature.

• Korean Journal of Adult Nursing
• /
• v.27 no.1
• /
• pp.83-93
• /
• 2015

Purpose: This study was to investigate the relationship among the symptom recognition, health behavior compliance, and the hospital arrival time to identify factors influencing the hospital arrival time in patient with acute myocardial infarction (AMI). Methods: The subjects of this study were 200 patients with AMI in C hospital in D city. Data were analyzed using descriptive statistics, independent t-test, One way ANOVA, Pearson's correlation coefficients, and stepwise multiple liner regression tests. Results: Level of symptom recognition and health behavior compliance was low. The median value of hospital arrival time was 4.48 hours (ST-segment Elevation Ml was 2.43 hours and Non ST-segment Elevation MI was 7.83 hours). Among the studied factors, only symptom recognition had a statistically significant positive correlation with health behavior compliance (r=0.38, p<.001). Factors influencing the hospital arrival time were MI classification, diabetes mellitus (DM) and transport vehicle to the 1st hospital, and they accounted for 13% of the variance for hospital arrival time in AMI patients. Conclusion: To prevent the delay of hospital arrival time in MI patients, a more robust nursing strategic intervention according to MI classification and DM is necessary; further education on the importance of transportation utilization is also mandated.

• The Journal of Korea Robotics Society
• /
• v.2 no.4
• /
• pp.297-303
• /
• 2007

In this paper, we discuss a service to give alarm in the case of emergency for the old and the weak by human behavior recognition in Intelligent Space. Our Intelligent Space consists of mobile robots, sensors and agents. And these components are connected to network framework. Agent analyzes data acquired from networked sensors and determines task of robots and a space to provide a service for humans. In our emergency alarm service, human behavior recognition service module analyzes accelerometer data obtained from body-attached human behavior sensing platform, and classifies into four basic human behavior such as walking, running, sitting and falling-down. For the old and the weak, falling-down behavior may bring about dangerous situations. On such an occasion, agent executes emergency alarm service immediately. And then a selected mobile robot approaches fallen person and sends images of the person to guardians. In this paper, we set up a scenario to verify the emergency alarm service in Intelligent Space, and show feasibility of the service from our simulation experiments.

• Journal of Broadcast Engineering
• /
• v.23 no.5
• /
• pp.598-605
• /
• 2018

Behavior awareness is a technology that recognizes human behavior through data and can be used in applications such as risk behavior through video surveillance systems. Conventional behavior recognition algorithms have been performed using the 2D camera image device or multi-mode sensor or multi-view or 3D equipment. When two-dimensional data was used, the recognition rate was low in the behavior recognition of the three-dimensional space, and other methods were difficult due to the complicated equipment configuration and the expensive additional equipment. In this paper, we propose a method of recognizing human behavior using only CCTV images without additional equipment using only RGB and depth information. First, the skeleton extraction algorithm is applied to extract points of joints and body parts. We apply the equations to transform the vector including the displacement vector and the relational vector, and study the continuous vector data through the RNN model. As a result of applying the learned model to various data sets and confirming the accuracy of the behavior recognition, the performance similar to that of the existing algorithm using the 3D information can be verified only by the 2D information.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.16 no.10
• /
• pp.7146-7156
• /
• 2015

The purpose of this study was to investigate the effect of health behavior, resilience an recognition of well-dying on the depression of elderly with chronic disease. It was the survey period was from February 13 to March 20, 2015 with 185 people. For data analysis, SPSS 20.0 program was used. As a result, the mean level of health behavior was 2.60, the mean level of resilience was 3.14, recognition of well-dying was 3.41, the mean level of depression was 3.29. Depression was showed a negative corelation with health behavior and recognition of well-dying. Factors that affect depression was health behavior, subjective health status, recognition of well-dying, live with and sex. Also, strange cause of these was 44.8% of depression. Results suggest that, to mediate melancholy elderly, it is necessary to develop a program in consideration of various factors.

• Korean Journal of Rural Living Science
• /
• v.6 no.1
• /
• pp.65-72
• /
• 1995

This article explores : 1) There were significant differences in the behavioral aggressiveness and the recognition about economical and psychological benefits related to complaining behavior. 2) The significant differences were found in the trust, distrust and behavioral aggressiveness related to types of consumer attitudes. 3) The satisfaction with the purchasing behavior was significantly explained by the distrusted relationship, the recognition of economic and psychological benefits, private complaining behavior types and aggressive types of consumer attitudes.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.3
• /
• pp.1450-1463
• /
• 2019

The problem towards crowd behavior recognition in a serious clustered scene is extremely challenged on account of variable scales with non-uniformity. This paper aims to propose a crowed behavior classification framework based on a transferring hybrid network blending 3D res-net with inception-v3. First, the 3D res-inception network is presented so as to learn the augmented visual feature of UCF 101. Then the target dataset is applied to fine-tune the network parameters in an attempt to classify the behavior of densely crowded scenes. Finally, a transferred entropy function is used to calculate the probability of multiple labels in accordance with these features. Experimental results show that the proposed method could greatly improve the accuracy of crowd behavior recognition and enhance the accuracy of multiple label classification.

• Nutrition Research and Practice
• /
• v.1 no.1
• /
• pp.36-41
• /
• 2007

The purpose of this study was to determine the recognition of body image and food behavior factors according to the BMI. The subjects of this study were 242 7th grade students resided in San Francisco area. The degree of recognition for self-estimated physique of subjects by gender and by race showed no significant differences by gender but significant differences by race, showing that 20.0% was considered as underweight in Asian and 7.5% was considered as underweight in White students. This showed the same tendency as actual physique status (BMI). Also, the ratio of being recognized as more than overweight was 17.3% in Asian, 23.3% in Hispanic, and 13.4% in White students. In case of female students, the ratio of dieting experience was 63.3%, and 49.3% of White students and 63.3% of Hispanic students experienced dieting. In case of students answered not healthy, their body weight were significantly higher than those answered as healthy, and the BMI was also over 19, showing significant differences. Thus cases that answered as not healthy had greater body weight and BMI. Also it showed that frequent dieting experience is related to higher height and weight. The analysis of food behavior factors perceived by body shape showed that the group perceived itself as overweight consumed more 'fast food' but had low scores in 'vegetables' intake, with frequent intake of 'soda' and tendency to 'overeat'. Also, the tendency for 'balanced life' was significantly lower and for skipping breakfast was significantly higher, suggesting problematic food behavior.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.35 no.9C
• /
• pp.756-760
• /
• 2010

This paper is proposed to an novel facial behavior recognition system for driver's fatigue detection. Facial behavior is shown in various facial feature such as head expression, head pose, gaze, wrinkles. But it is very difficult to clearly discriminate a certain behavior by the obtained facial feature. Because, the behavior of a person is complicated and the face representing behavior is vague in providing enough information. The proposed system for facial behavior recognition first performs detection facial feature such as eye tracking, facial feature tracking, furrow detection, head orientation estimation, head motion detection and indicates the obtained feature by AU of FACS. On the basis of the obtained AU, it infers probability each state occur through Bayesian network.