• Korean Institute of Interior Design Journal
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• v.22 no.5
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• pp.143-151
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• 2013

As we recognize the space, humans will experience a process to synthesize elements of cognitive various methods, come to understand the environment. It is intended that on humans to recognize the space, it is intended to act directly under study how to recognize. Humans can know that the determining action based on the values and physical condition, based on the space in which they have been recognized, there are differences in the behavior of the human as a result. Social and arrangement of components - physical region that is cultural difficulties constitute experience specific areas therein. Space for human activities and human, can know that it is not a memory of human behavior, to have a closer relationship with human perception. That is, the description will be aware of the space via the perceptual phenomenon of man due to physical elements performed in the space, what acts about what happens. Through an understanding of the potential for this, and emotion space production consisting only of physical visual element future, and use act of the area to be expressed from his recognition, through the expansion of the perceptual elements, diverse experience richer and more it is a case where deemed necessary access space configuration capable of a broad depth study of this portion is happening, in order to constitute a space, a new interpretation for human behavior is progressing.

• Smart Media Journal
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• v.1 no.2
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• pp.12-22
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• 2012

Today we are witnessing an increasingly widespread use of cameras in our lives for video surveillance, robot vision, and mobile phones. This has led to a renewed interest in computer vision in general and an on-going boom in human activity recognition in particular. Although not particularly fancy per se, human gait is inarguably the most common and frequent action. Early on this decade there has been a passing interest in human gait recognition, but it soon declined before we came up with a systematic analysis and understanding of walking motion. This paper presents a set of DBN-based models for the analysis of human gait in sequence of increasing complexity and modeling power. The discussion centers around HMM-based statistical methods capable of modeling the variability and incompleteness of input video signals. Finally a novel idea of extending the discrete state Markov chain with a continuous density function is proposed in order to better characterize the gait direction. The proposed modeling framework allows us to recognize pedestrian up to 91.67% and to elegantly decode out two independent gait components of direction and posture through a sequence of experiments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.791-793
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• 2016

In the development of various types of sensor technology, the general users smartphone, the environment is increased, which can be seen in contact with the movement recognition device, such as a console game machine (Nintendo Wii), an increase in the user needs of the action recognition-based input device there is a tendency to have. Mouse existing behavior recognition, attached to the outside, is mounted in the form of mouse button is deformed, the left mouse was the role of the right button and a wheel, an acceleration sensor (or a gyro sensor) inside to, plays the role of a mouse cursor, is to manufacture a compact, there is a difficulty in operating the button, to apply a motion recognition technology is used to operate recognition technology only pointing cursor is limited. Therefore, in this paper, using a MEMS-based motion-les Koguni tion sensor (Motion Recognition Sensor), to recognize the behavior of the two points of the human body (thumb and forefinger), to generate the motion data, and this to the foundation, compared to the pre-determined matching table (moving and mouse button events cursor), and generates a control signal by determining, were studied the generated control signal input device of the computer wirelessly transmitting.

• The Korean Journal of Food And Nutrition
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• v.6 no.3
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• pp.219-230
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• 1993

This research aims to study the changes In gastrointestinal function attributed to aging In human. The thresholds for recognition and detection of flavors became elevated and salivary gland acinar cells decreased in the old age. But most esophageal function remained relatively Intact. Although gastric emptying time has been slowed with aging, the total intestinal transit time did not differ. Atropic gastritis due to H. pylori in old man decreased secretion of acid and Intrinsic factor and absorbability of calcium and iron. Pancreatic secretion is droned in older persons. Prevalence of gallstones rised with age. Liver size and portal blood flow decreased significantly with age. Mucosal surface area has been reported to be slightly diminished in the aging man. Glucose transporters decreased and Insulin tolerance Increased. Absorption of aromatic amino acid is diminished with age. Dietary protein In that aging human increased fecal nitrogen excretion. Vitamin A tolerance increased. Vitamin D receptor concentration decreased and resistance to 1,25-(OH)2D3 action increased. Permeability of aging small Intestine Increased. Zinc balance dirt not differ Copper absorption appeared not to be significantly affected by age. Neurotensin secretion decreased thus slowed colonic peristaltic movements and Intestinal mucosal growth.

• ETRI Journal
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• v.33 no.3
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• pp.415-422
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• 2011

In this paper, we present a novel vision-based method of recognizing finger actions for use in electronic appliance interfaces. Human skin is first detected by color and consecutive motion information. Then, fingertips are detected by a novel scale-invariant angle detection based on a variable k-cosine. Fingertip tracking is implemented by detected region-based tracking. By analyzing the contour of the tracked fingertip, fingertip parameters, such as position, thickness, and direction, are calculated. Finger actions, such as moving, clicking, and pointing, are recognized by analyzing these fingertip parameters. Experimental results show that the proposed angle detection can correctly detect fingertips, and that the recognized actions can be used for the interface with electronic appliances.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.542-544
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• 2019

Changes in a person's health affect one's lifestyle and work activities. According to the World Health Organization (WHO), abnormal activity is growing faster in people aged 60 or more than any other age group in almost every country. This trend steadily continues and expected to increase further in the near future. Abnormal activity put these people at high risk of expected incidents since most of these people live alone. Human abnormal activity analysis is a challenging, useful and interesting problem among the researchers and its particularly crucial task in life and health care areas. In this paper, we discuss the problem of abnormal activities of old people lives alone at home. We propose Convolutional Neural Network (CNN) based model to detect the abnormal behaviors of elderlies by utilizing six simulated action data from daily life actions.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.87-93
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• 2019

In this paper, we propose a real-time drone-based violent protest detection system. Our proposed system uses drones to detect scenes of violent protest in real-time. The important problem is that the victims and violent actions have to be manually searched in videos when the evidence has been collected. Firstly, we focused to solve the limitations of existing collecting evidence devices by using drone to collect evidence live and upload in AWS(Amazon Web Service)[1]. Secondly, we built a Deep Learning based violence detection model from the videos using Yolov3 Feature Pyramid Network for human activity recognition, in order to detect three types of violent action. The built model classifies people with possession of gun, swinging pipe, and violent activity with the accuracy of 92, 91 and 80.5% respectively. This system is expected to significantly save time and human resource of the existing collecting evidence.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.360-363
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• 2018

Human activity is influenced by various factors, from individual physical features such as vertebral flexion and pelvic distortion to feelings such as joy, anger, and sadness. However, the nature of these behaviors changes over time, and behavioral characteristics do not change much in the short term. The activity data of a person has a time series characteristic that changes with time and a certain regularity for each action. In this study, we applied LSTM, a kind of cyclic neural network to deal with time - series characteristics, to the technique of recognizing activity type and improved recognition rate of activity type by measuring time and parameter optimization of components of LSTM model.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.95-108
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• 2010

In this paper, we present a hybrid neural network model for dynamic hand gesture recognition. The model consists of two modules, feature extraction module and pattern classification module. We first propose a modified CNN(convolutional Neural Network) a pattern recognition model for the feature extraction module. Then we introduce a weighted fuzzy min-max(WFMM) neural network for the pattern classification module. The data representation proposed in this research is a spatiotemporal template which is based on the motion information of the target object. To minimize the influence caused by the spatial and temporal variation of the feature points, we extend the receptive field of the CNN model to a three-dimensional structure. We discuss the learning capability of the WFMM neural networks in which the weight concept is added to represent the frequency factor in training pattern set. The model can overcome the performance degradation which may be caused by the hyperbox contraction process of conventional FMM neural networks. From the experimental results of human action recognition and dynamic hand gesture recognition for remote-control electric home appliances, the validity of the proposed models is discussed.

• Journal of Nutrition and Health
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• v.43 no.6
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• pp.597-606
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• 2010

This study investigates the change of level of psychosocial factors of decisional balance, processes of change, and self-efficacy after nutrition education on vegetable and fruit intake of children in pre-action stages. Forty-seven 5th grade elementary school children in Yeongi-Gun, Chungnam-Do were categorized into pre-action stages from a total of 71 children by four-stage questions, and then finally into two stages of precontemplation and contemplation & preparation. After 6 weeks nutrition education, recognition of both of pros and cons of vegetable intake were improved in the two groups of precontemplation and contemplation & preparation. In fruits intake, however, recognition of pros was not changed after nutrition education in either stage, but recognition of cons was improved only in the precontemplation stage. However, no difference in any psychosocial variables was found between precontemplation stage and contemplation & preparation stage. After the nutrition education, the score of consciousness raising of vegetable intake belonging to cognitive process of change increased in the subjects of the precontemplation stage, wheres the scores of dramatic relief, self-reevaluation, environment reevaluation and social liberation increased in the group of contemplation & preparation. In fruit intake, however, no change was found except in the environment reevaluation in the precontemplation group. The behavioral process of change of vegetable and fruit intake did not show any change in either group after nutrition education. Self-efficacy scores of vegetable intake of keeping the foods at hand, eating the recommended number of servings and making time to eat increased in the precontemplation group, while in the contemplation & preparation group, self efficacy scores of shopping for a variety of vegetables, instead of keeping the foods at hands, as well as the scores of the rest two items of eating the recommended number of servings and making time to eat increased after nutrition education. However, in case of fruits intake, no difference was shown in the precontemplation stage except an item of keeping the foods at hand. The results indicated that the stage of change was progressed in the pre-action stage after nutrition education and the scores of many psychosocial factors increased in both pre-action stages. It is suggested that the recognition of cons as well as the cognitive process of change and self-efficacy could be best used as indicators of the assessment of nutrition education program for vegetable intake.