• Journal of Broadcast Engineering
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• v.19 no.6
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• pp.942-956
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• 2014

This paper proposes a real-time hand pose tracking method using a monocular RGB camera. Hand tracking has high ambiguity since a hand has a number of degrees of freedom. Thus, to reduce the ambiguity the proposed method adopts the step-by-step estimation scheme: a palm pose estimation, a finger yaw motion estimation, and a finger pitch motion estimation, which are performed in consecutive order. Assuming a hand to be a plane, the proposed method utilizes a planar hand model, which facilitates a hand model regeneration. The hand model regeneration modifies the hand model to fit a current user's hand, and improves robustness and accuracy of the tracking results. The proposed method can work in real-time and does not require GPU-based processing. Thus, it can be applied to various platforms including mobile devices such as Google Glass. The effectiveness and performance of the proposed method will be verified through various experiments.

• Proceedings of the ESK Conference
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• 1997.10a
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• pp.393-397
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• 1997

본 연구에서는 손동작(Hand Motion)과 수작업(Manual Task) 분석에 VR환경에서 사용되는 각도 측정 장갑(3-D Glove)을 이용하는 방법을 제안하였다. 본 연구에서 개발된 손동작(Hand Motion)과 수작업(Manual Task)의 분석 시스템은 18-sensor $Cyberglove^{TM}$정 시스템으로부터 측정된 angle data를 기초로 손동작이나 수작업에 대한 totalmuscle moment값과 total muscle excursion값을 구하고, digit와 joint의 moment값을 X,Y.Z방향별고 구하는 기능을 가지고 있다. 시스템의 구성은 : (1) $Cyberglove^{TM}$ System과 분석 시스템의 digital data 처리를 기반으로 하는 손동작의 측정 시스템 ; (2) $Cyberglove^{TM}$ System에서 얻어진 자료를 바탕으로 3차원 공간에서 손동작을 표현할 수 있는 Kinematic Hand Model ; (3) Hand Model과 $Cyberglove^{TM}$ Systme을 기반으로 3차원에서 손동작의 역학적 분석을 할 수 있는 3-D Hand Biomechanical Model ; 등으로 되어있다. 본 시스템은 Telerobotics, Medicine, Virtual Reality 등 다양한 분야에 응용이 가능하며, 수작업에 관련되는 Product Design, Manual Control Device, Computer I/O Device의 설계에도 도움이 될 것으로 기대된다.

• Journal of Korea Multimedia Society
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• v.22 no.4
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• pp.424-431
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• 2019

In this paper, we propose a real-time hand gesture recognition algorithm to eliminate the inconvenience of using hand controllers in VR applications. The user's 3D hand coordinate information is detected by leap motion sensor and then the coordinates are generated into two dimensional image. We classify hand gestures in real-time by learning the imaged 3D hand coordinate information through SSD(Single Shot multibox Detector) model which is one of CNN(Convolutional Neural Networks) models. We propose to use all 3 channels rather than only one channel. A sliding window technique is also proposed to recognize the gesture in real time when the user actually makes a gesture. An experiment was conducted to measure the recognition rate and learning performance of the proposed model. Our proposed model showed 99.88% recognition accuracy and showed higher usability than the existing algorithm.

• Journal of Korea Game Society
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• v.12 no.5
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• pp.79-88
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• 2012

In this paper, we present a real-time hand pose recognition method to provide an intuitive user interface through hand poses or movements without a keyboard and a mouse. For this, the areas of right and left hands are segmented from the depth camera image, and noise removal is performed. Then, the rotation angle and the centroid point of each hand area are calculated. Subsequently, a circle is expanded at regular intervals from a centroid point of the hand to detect joint points and end points of the finger by obtaining the midway points of the hand boundary crossing. Lastly, the matching between the hand information calculated previously and the hand model of previous frame is performed, and the hand model is recognized to update the hand model for the next frame. This method enables users to predict the hidden fingers through the hand model information of the previous frame using temporal coherence in consecutive frames. As a result of the experiment on various hand poses with the hidden fingers using both hands, the accuracy showed over 95% and the performance indicated over 32 fps. The proposed method can be used as a contactless input interface in presentation, advertisement, education, and game applications.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.4
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• pp.43-52
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• 1998

We extract a hand region by using color information, which is an important feature for human vision to distinguish objects. Because pixel values in images are changed according to the luminance and lighting source, it is difficult to extract a hand region exactly without previous knowledge. We generate a hand skin model at learning stage, and extract a hand region from images by using the model. We also use a Kalman filter to consider changes of pixel values in a hand skin model. A Kalman filter restricts a search area for extracting a hand region at next frame also. The validity of the proposed method is proved by implementing the hand-region acquisition module.

• Journal of the Korea Computer Graphics Society
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• v.3 no.1
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• pp.1-7
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• 1997

Technology for the realistic model and the motion control of human is applied to many areas of computer graphics, virtual reality and computer simulations. Human body is a multi-articular body. Generally, to create a human model and motions. articulated body models are generated and their motions are controlled based upon kinematics. The hand of the human consists of many small articulations and each articulations have a various degree of freedom. This paper presents a model of human hand which is based on the two kinds of constraints to control the motions of the hand realistically. To build a hand model, we experimented the anatomy of the human hand, and the diverse motions of the hand are tested.

• Journal of the Ergonomics Society of Korea
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• v.28 no.3
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• pp.1-6
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• 2009

Recently, importance of the rehabilitation of hand pathologies as well as the development of high-technology hand robot has been increased. The biomechanical model of hand is indispensable due to the difficulty of direct measurement of muscle forces and joint forces in hands. In this study, a three-dimensional biomechanical model of four fingers including three joints and ten muscles in each finger was developed and a mathematical relationship between neural commands and finger forces which represents the enslaving effect and the force deficit effect was proposed. When pressing a plate under the flexed posture, the muscle forces and the joint forces were predicted by the optimization technique. The results showed that the major activated muscles were flexion muscles (flexor digitorum profundus, radial interosseous, and ulnar interosseous). In addition, it was found that the antagonistic muscles were also activated rather than the previous models, which is more realistic phenomenon. The present model has considered the interaction among fingers, thus can be more powerful while developing a robot hand that can totally control the multiple fingers like human.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.4
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• pp.34-40
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• 2008

This paper presents a vision based walking robot control system using hand shape recognition. To recognize hand shapes, the accurate hand boundary needs to be tracked in image obtained from moving camera. For this, we use an active contour model-based tracking approach with mean shift which reduces dependency of the active contour model to location of initial curve. The proposed system is composed of four modules: a hand detector, a hand tracker, a hand shape recognizer and a robot controller. The hand detector detects a skin color region, which has a specific shape, as hand in an image. Then, the hand tracking is performed using an active contour model with mean shift. Thereafter the hand shape recognition is performed using Hue moments. To assess the validity of the proposed system we tested the proposed system to a walking robot, RCB-1. The experimental results show the effectiveness of the proposed system.

• Journal of KIISE:Software and Applications
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• v.35 no.12
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• pp.780-788
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• 2008

Modeling hand poses and tracking its movement are one of the challenging problems in computer vision. There are two typical approaches for the reconstruction of hand poses in 3D, depending on the number of cameras from which images are captured. One is to capture images from multiple cameras or a stereo camera. The other is to capture images from a single camera. The former approach is relatively limited, because of the environmental constraints for setting up multiple cameras. In this paper we propose a method of reconstructing 3D hand poses from a 2D input image sequence captured from a single camera by means of Belief Propagation in a graphical model and recognizing a finger clicking motion using a hidden Markov model. We define a graphical model with hidden nodes representing joints of a hand, and observable nodes with the features extracted from a 2D input image sequence. To track hand poses in 3D, we use a Belief Propagation algorithm, which provides a robust and unified framework for inference in a graphical model. From the estimated 3D hand pose we extract the information for each finger's motion, which is then fed into a hidden Markov model. To recognize natural finger actions, we consider the movements of all the fingers to recognize a single finger's action. We applied the proposed method to a virtual keypad system and the result showed a high recognition rate of 94.66% with 300 test data.

• Journal of Korean society of Dental Hygiene
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• v.13 no.2
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• pp.193-200
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• 2013

Objectives : The purpose of this study was to examine influential factors related to hand washing practice in dental hygienists by health belief model, one of the major predictors of health behavior including perceived susceptibility, perceived seriousness, perceived benefits, perceived barriers and cues to action. Methods : The subjects were dental hygienists in dental hospitals, dental clinics, general hospitals and university hospitals in Seoul. A survey was conducted from May 1 to September 30, 2011. Results : Analysis of health belief of dental hygienists in hand washing, they revealed the highest marks of 4.39 to perceived benefits, followed by perceived susceptibility(4.29), perceived seriousness(3.94), cues to action(3.30) and perceived barriers(1.81). The mean was 4.13 in hand washing practice. The senior and well educated dental hygienists in general hospitals had a tendency to wash hands frequently. It is statistically significant(p<0.05). In regard to the correlation among the subfactors of health beliefs, susceptibility had a statistically significant positive correlation to seriousness, benefits and cues to action, and seriousness was positively correlated to benefits and cues to action. Conclusions : It is necessary to develop and implement hand washing education program for dental hygienists focusing on perceived benefits and barriers which are two of the health beliefs affecting the hand washing practice.