• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.13-16
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• 2002

This paper proposes a method to track hand gesture and to recognize the gesture pattern using Kalman filter and continuous dynamic programming (CDP). The positions of hands are predicted by Kalman filter, and corresponding pixels to the hands are extracted by skin color filter. The center of gravity of the hands is the same as the input pattern vector. The input gesture is then recognized by matching with the reference gesture patterns using CDP. From experimental results to recognize circle shape gesture and intention gestures such as “Come on” and “Bye-bye”, we show the proposed method is feasible to the hand gesture-based human -computer interaction.

• Korean Journal of Computational Design and Engineering
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• v.13 no.5
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• pp.323-333
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• 2008

Various virtual gloves have been developed for the past four decades. These gloves have sensors that can measure bending angles at finger joints and the positions as well as orientations of hands. Previous researches were mostly concentrated on utilizing different kinds of sensors. As the technology matured, more interests are given towards building virtual reality applications. Furthermore, due to the recent reduction of costs, these devices have been widely adopted. Our particular interest lies in three-dimensional applications where virtual objects are grasped and manipulated. For these applications, it is crucial to accurately measure finger joints angles for realistic object interactions with the virtual hand. With inaccurate measurements, virtual hands would penetrate inside virtual objects after they are grasped. Or alternatively, virtual objects would be grasped before hands are making any contacts with virtual objects. In this paper, we introduce new design of virtual gloves for improved finger joints measurements.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.85-91
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• 1995

This paper presents a system which recognizes the Korean Sign Language(KSL) and translates into normal Korean speech. A sign language is a method of communication for the deaf-mute who uses gestures, especially both hands and fingers. Since the human hands and fingers are not the same in physical dimension, the same form of a gesture produced by two signers with their hands may not produce the same numerical values when obtained through electronic sensors. In this paper, we propose a dynamic gesture recognition method based on feature analysis for efficient classification of hand motions, and on a fuzzy min-max neural network for on-line pattern recognition.

• Korean Journal of Human Ecology
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• v.12 no.1
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• pp.63-73
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• 2003

The purpose of this study was to quantify the hands of fabrics for the Korean folk clothes using both a KES-FB and an artificial neural network. In order to select the proper input parameters, we calculated the correlation using step-wise regression between mechanical properties and the hand value of fabrics. For the classification, the primary hand values and total hand value, five neural networks with three-layered structure were constructed using the error back propagation algorithm and, in order to reduce errors and to speed up learning, the momentum method was selected. From the analysis of the primary and total hands using a self-constructed artificial intelligence system, the error rates of sleekness, stiffness, silkiness, and roughness compared with the judgement of expert panels were found to be 3.3%, 3.3%, 1.6%, and 4.9%, respectively, while that of the total hand was 9.83%.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.4
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• pp.433-438
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• 2009

In this paper, we propose the robust features extraction method of human by using the skeleton model and hybrid silhouette model. The proposed feature extraction method is divided by hands, shoulder line and elbow region extraction. We use the peer's color information to find the position of hands and propose the circle detection method to extract the shoulder line and elbow. Finally, we show the effectiveness and feasibility of the proposed method through some experiments.

• Pediatric Infection and Vaccine
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• v.5 no.1
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• pp.143-146
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• 1998

Syphilis is a systemic communicable disease caused by the motile spirochete, Treponema pallidum, which is only a natural pathogen for human. The distribution and trends of syphilis are influenced by biologic factors, sexual behaviors, biomedical technology, availability of and access to health care, public health efforts, changes in population dynamics, and sociocultural factors. Although sexual contact is the main route of transmission, Treponema pallidum may also be infected through direct contact with syphilitic lesions, blood transfusion, ingestion of menstrual blood or vaginal secretions, or transplacental transmissions. In extremely unusual circumstances, infection by means of contact with a skin lesion and human bite have been reported. We experienced a case of syphilis which is thought to be infected by the abnormal imposition of hands on a child, which caused unnecessary erosive trauma with fingernails.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.6
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• pp.1127-1134
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• 2017

In this study, the robot arm control system was implemented that is operated by human hands, which can be used in environments that are inaccessible to humans. This function has to be sent to the robot's arms after extracting coordinates of human hands. Through mapping and bluetooth communication we use a leap motion sensor with infrared light and Image recognition sensor.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.5
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• pp.863-870
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• 2015

A Compensation Algorithm for The Position of the User Hands based on the Moving Mean-Shift ($CAPUH_{MMS}$) in Human Robot Interface (HRI) System running the Kinect sensor is proposed in order to improve the performance of the gesture recognition is proposed in this paper. The average error improvement ratio of the trajectories ($AEIR_{TJ}$) in left-right movements of hands for the $CAPUH_{MMS}$ is compared with other compensation algorithms such as the Compensation Algorithm based on the Compensation Algorithm based on the Kalman Filter ($CA_{KF}$) and the Compensation Algorithm based on Least-Squares Method ($CA_{LSM}$) by the developed realtime performance simulator. As a result, the $AEIR_{TJ}$ in up-down movements of hands of the $CAPUH_{MMS}$ is measured as 19.35%, it is higher value compared with that of the $CA_{KF}$ and the $CA_{LSM}$ as 13.88% and 16.68%, respectively.

• The Journal of Korea Robotics Society
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• v.8 no.1
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• pp.20-28
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• 2013

Large workspace and strong grasping force are required when a robot manipulates big and/or heavy objects. In that situation, bimanual manipulation is more useful than unimanual manipulation. However, the control of both hands to manipulate an object requires a more complex model compared to unimanual manipulation. Learning by human demonstration is a useful technique for a robot to learn a model. In this paper, we propose an imitation learning method of bimanual object manipulation by human demonstrations. For robust imitation of bimanual object manipulation, movement trajectories of two hands are encoded as a movement trajectory of the object and a force trajectory to grasp the object. The movement trajectory of the object is modeled by using the framework of dynamic movement primitives, which represent demonstrated movements with a set of goal-directed dynamic equations. The force trajectory to grasp an object is also modeled as a dynamic equation with an adjustable force term. These equations have an adjustable force term, where locally weighted regression and multiple linear regression methods are employed, to imitate complex non-linear movements of human demonstrations. In order to show the effectiveness our proposed method, a movement skill of pick-and-place in simulation environment is shown.

• Journal of Oriental Medical Thermology
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• v.4 no.1
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• pp.45-53
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• 2005

Objectives; Hapkok(L14) and Taechung(Liv3) are acupuncture points located on both sides of each foot and hand of the human body. These two points are called sakwan points. Matching these acupuncture points have a significant reason in points of not only regulating the circulation of Yin-Yang as a source point of each meridian, but also playing a basic role of twelve meridian by controling circulation of ki and blood in the whole body. There are already related documents and studies on stimulating sakwan points. Since those papers mostly studied on either hapkok or taechung, we came to have a doubt of stimulating the two point at the same time when an inbalance of Yin-Yang and ki-blood appears. Accordingly, we got to investigate how thermogram of body changes after applying an acupuncture on sakwan points. Our study is as follows ; Methods; Our study was performed on 30 normal cases(M:F=17:13) with no past history to observe the effects of the acupuncture. We measured temperature of abdomen and the back of both hands by D.I.T.I(Digital Infrared Thermographic Imaging) before and after acupuncture on sakwan points. Results and Conclusion; The thermographic change on abdomen was $0.51{\pm}0.71^{\circ}C$. Temperature of abdomen after acupuncture was higher than before acupuncture with high validity(p <0.01). And the thermographic changes on the back of both hands were right hand $0.54{\pm}1.17^{\circ}C$, left hand $0.56{\pm}1.28^{\circ}C$. Temperature on the back of both hands after acupuncture was higher than before acupuncture, but the difference between them had little validity(p <0.01) In addition, we found that it doesn't necessarily follow that the thermographic changes on abdomen and back of both hands after acupuncture on sakwan points happen concurrently.