• Journal of KIISE:Software and Applications
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• v.36 no.12
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• pp.977-985
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• 2009

This paper proposes a real-time algorithm for tracking the fast moving human body. Although Iterative closest point (ICP) algorithm is suitable for real-time tracking due to its efficiency and low computational complexity, ICP often fails to converge when the human body moves fast because the closest point may be mistakenly selected and trapped in a local minimum. To overcome such limitation, we combine a particle filter based on a motion history information with the ICP. The proposed human body motion tracking algorithm reduces the search space for each limb by employing a hierarchical tree structure, and enables tracking of the fast moving human bodies by using the motion prediction based on the motion history. Experimental results show that the proposed human body motion tracking provides accurate tracking performance and fast convergence rate.

• Korean Journal of Psychosomatic Medicine
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• v.20 no.1
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• pp.3-8
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• 2012

Understanding the biopsychosocial model of illness is crucial for any meaningful advance of health. The maintenance and promotion of health is achieved by different combinations of physical, mental, social and spiritual well-being. Health is not an objective of living. It is not only a state, but also a resource for everyday life. Health is a positive concept that emphasizes personal and social resources, as well as physical capacities. Understanding the biopsychosocial model of health and disease is very important in the medical system. George Engel challenged the medical profession to reconsider a strict biomedical approach to medical education and care, and to embrace a "new medical model," the biopsychosocial model. He argued that humans are at once biological, psychological, and social beings who behave in certain ways that can promote or harm their health. Although understanding the biopsychosocial model of illness is important, Korea's medical system have mainly been focusing on the biomedical model of illness. I would like to highlight the importance of biopsychosocial model of illness for Korea's medical system and real clinical field according to the 20th anniversary of Korean Society of Psychosomatic Medicine.

• Journal of the Korea Computer Graphics Society
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• v.29 no.1
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• pp.13-22
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• 2023

In this paper, we present a method for estimating full 3D shapes from given 2D silhouette images of human bodies. Because the silhouette only consists of the partial information on the true shape, it is an ill-posed problem. To address the problem, we use the statistical human shape space obtained from the existing large 3D human shape database. The method consists of three steps. First, we extract the boundary pixels and their appropriate normal vectors from the input silhouette images. Then, we initialize the correspondences of each pixel to the vertex of the statistically-deformable 3D human model. Finally, we numerically optimize the parameters of the statistical model to fit best to the given silhouettes. The viability and the robustness of the method is demonstrated with various experiments.

• Proceedings of the ESK Conference
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• 1992.10a
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• pp.3-8
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• 1992

컴퓨터 스크린상에 어떤 물체의 형상을 나타낼 때 각 형태에 따라 그 표현 기법은 크게 두 분야로 나뉘게 되는데 대부분의 기계부품들처럼 원통이나 평면 등과 같이 기하학적 기본형태들로 이루어진 것과 사람이나 동물처럼 형상자체가 단순한 수학적공식으로 표현이 불가능한 형태(Free Form Geometry)로 분류된다. 어떤 대상물체가 선정되면 그것의 기하학적 형상을 먼저 컴퓨터 스크린상에 정확한 형상데이터로 표현된 다음 가시화를 위한 것이든 시뮬레이션 목적이든 그 형상 데이터가 이용된다. 이처럼 컴퓨터에 의한 모의 실험에서 대상물체를 모델링하는 단계는 반드시 필요하다. 최근 컴퓨터에 의한 각종 모델의 시뮬레이션을 시도할 때 Modeling 단계에서 수학적 공식으로 표현이 가능한 모델(Mathemeatical model)보다 임의 형태를 가진 모델(Physical model)표현에 많은 애로를 겪고 있는 실정이다. 따라서, 본 연구에서는 인간이나 항공기처럼 복잡한 형태를 가진 물체형상을 컴퓨터 스크린상에 표현할 때 비교적 실물에 가까운 형상데이터를 얻는 기법들에 대해 기술하고 그 결과를 소개한다. 특히 인간의 정적 또는 동적인 자세변화에 따른 각 신체 부위의 정확한 계량분석을 시도할 때 본 기법들의 응용이 가능하다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.353-356
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• 2005

감성이란 외부의 여러 가지 감각적인 자극에 대해 직관적이고 반사적으로 발생하는 반응이다. 감성에는 희로애락과 같은 다양한 감정이 포함된다. 감성은 개인과 환경의 영향을 받는 동시에 사회$\cdot$문화적인 영향에 의해 달라지는 종합적이고 복합적인 느낌이기 때문에 명확하게 표현하기 어렵다. 개인적인 차이는 있지만 인간의 감정은 다양한 행동이나 신체적 혹은 생리적인 반응으로 표출되기 때문에 이러한 다양한 반응을 이용해서 인간의 감정을 유추할 수 있다. 행동이나 신체적인 표현은 개인과 환경, 사회$\cdot$문화적인 영향에 따라 다양하게 나타나기 때문에 이를 정량화하기에는 힘든 부분이 있다. 반면 감정 상태에 따른 생리적인 반응은 여러 사람들에게서 공통적인 부분을 찾을 수가 있다. 본 논문에서는 감정에 따라 다양하게 나타나는 인간의 생리적인 반응 정토를 측정하고 이들의 공통점에 따라 생리적 반응을 통해 인간의 감정에 유추할 수 있는 공학적 모델을 제시하고자 한다.

• Journal of Internet Computing and Services
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• v.13 no.5
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• pp.9-19
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• 2012

Virtual human used as HCI in digital contents expresses his various emotions across modalities like facial expression and body posture. However, few studies considered combinations of such nonverbal multimodal in emotion perception. Computational engine models have to consider how a combination of nonverbal modal like facial expression and body posture will be perceived by users to implement emotional virtual human, This paper proposes the impacts of nonverbal multimodal in design of emotion expressed virtual human. First, the relative impacts are analysed between different modals by exploring emotion recognition of modalities for virtual human. Then, experiment evaluates the contribution of the facial and postural congruent expressions to recognize basic emotion categories, as well as the valence and activation dimensions. Measurements are carried out to the impact of incongruent expressions of multimodal on the recognition of superposed emotions which are known to be frequent in everyday life. Experimental results show that the congruence of facial and postural expression of virtual human facilitates perception of emotion categories and categorical recognition is influenced by the facial expression modality, furthermore, postural modality are preferred to establish a judgement about level of activation dimension. These results will be used to implementation of animation engine system and behavior syncronization for emotion expressed virtual human.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.9B
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• pp.867-875
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• 2009

The reliability of wireless body area networks is an important research issue since it may jeopardize the vital human life, unless managed properly. In this article, a new modeling and analysis of node misbehaviors in wireless body area networks is presented, in the presence of multi-type failures. First, the nodes are classified into types in accordance with routing capability. Then, the node behavior in the presence of failures such as energy exhaustion and/or malicious attacks has been modeled using a novel Semi-Markov process. The proposed model is very useful in analyzing reliability of WBANs in the presence of multi-type failures.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1794-1795
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• 2015

현재의 자동차는 운전자에게 안정성과 편의성을 제공하기 위해서, 자동차 중심의 인터페이스를 넘어 인간 중심의 인간-차량 인터페이스로 발전함에 따라서 차량의 전체 시스템 설계에 영향을 미치는 새로운 양방향 인터페이스를 필요로 하고 있다. 운전자가 운전함에 있어 사용하고 있는 운전자의 음성, 신체 동작, 생체 신호와 차량의 주행 상태에 대한 종합적이고 체계적으로 관리가 기반이 되는 통합 모델을 통해서 지능화, 복합 체계화된 지능형 자동차를 효율적으로 구현할 수 있는 인간-차량 인터페이스를 제공할 것으로 기대된다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.6
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• pp.75-83
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• 2007

A model based approach of tracking the limbs of a walking human subject is proposed in this paper. The tracking process begins by building a data base composed of conditional probabilities of motions between the limbs of a walking subject. With a suitable amount of video footage from various human subjects included in the database, a probabilistic model characterizing the relationships between motions of limbs is developed. The motion tracking of a test subject begins with identifying and tracking limbs from the surveillance video image using the edge and silhouette detection methods. When occlusion occurs in any of the limbs being tracked, the approach uses the probabilistic motion model in conjunction with the minimum cost based edge and silhouette tracking model to determine the motion of the limb occluded in the image. The method has shown promising results of tracking occluded limbs in the validation tests.

• Journal of Internet Computing and Services
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• v.18 no.6
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• pp.85-92
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• 2017

Recently, in the field of video surveillance, a Deep Learning based learning method has been applied to a method of detecting a moving person in a video and analyzing the behavior of a detected person. The human activity recognition, which is one of the fields this intelligent image analysis technology, detects the object and goes through the process of detecting the body keypoint to recognize the behavior of the detected object. In this paper, we propose a method for Body Keypoint Localization based on Object Detection using RGB-D information. First, the moving object is segmented and detected from the background using color information and depth information generated by the two cameras. The input image generated by rescaling the detected object region using RGB-D information is applied to Convolutional Pose Machines for one person's pose estimation. CPM are used to generate Belief Maps for 14 body parts per person and to detect body keypoints based on Belief Maps. This method provides an accurate region for objects to detect keypoints an can be extended from single Body Keypoint Localization to multiple Body Keypoint Localization through the integration of individual Body Keypoint Localization. In the future, it is possible to generate a model for human pose estimation using the detected keypoints and contribute to the field of human activity recognition.