• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2154-2159
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• 2005

This paper presents the kinematic analysis of two robots, an exoskeleton type master robot and a human like slave robot with two arms. Two robots are designed and built to be equivalent as motion following robots. The operator wears the exoskeleton robot to generate motions, then the slave robot is required to follow after the motion of the master robot. However, different kinematic configuration yields position mismatches of the end-effectors. To synchronize motions of two robots, kinematic analysis of mapping is analyzed. The forward and inverse kinematics have been simulated and the corresponding experiments are also conducted to confirm the proposed mapping analysis.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.3
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• pp.55-67
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• 2009

This paper addresses the dynamic consideration of the athletic constraints on skating motion. In order to generate a human-like skating motion, the athletic constraints are distinctively analyzed into dynamic constraints and physical constraints. A close investigation of the athletic constraints evolved valid extent of dominant parameter for a leg muscle. During the human-like skating motion, the state of actuation was shifted from region of maximum force to region of maximum power. Simulation results were intuitively comprehensible, and the effectiveness of analytic algorithm was demonstrated for skating motion.

• The Journal of Korea Robotics Society
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• v.12 no.2
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• pp.239-247
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• 2017

The purpose of this study is to develop a motion generation technique based on a double inverted pendulum model (DIPM) that learns and reproduces humanoid robot (or virtual human) motions while keeping its balance in a pattern similar to a human. DIPM consists of a cart and two inverted pendulums, connected in a serial. Although the structure resembles human upper- and lower-body, the balancing motion in DIPM is different from the motion that human does. To do this, we use the motion capture data to obtain the reference motion to keep the balance in the existence of external force. By an optimization technique minimizing the difference between the motion of DIPM and the reference motion, control parameters of the proposed method were learned in advance. The learned control parameters are re-used for the control signal of DIPM as input of linear quadratic regulator that generates a similar motion pattern as the reference. In order to verify this, we use virtual human experiments were conducted to generate the motion that naturally balanced.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.87-92
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• 2024

The rapid development of artificial intelligence technology in recent years is evident, from the emergence of ChatGPT to innovations like Midjourney, Stable Diffution, and the upcoming SORA text-to-video technology by OPENai. Animation capture technology, driven by the AI technology trend, is undergoing significant advancements, accelerating the progress of the animation industry. Through an analysis of the current application of DEEPMOTION, this paper explores the development direction of AI motion capture technology, analyzes issues such as errors in multi-person object motion capture, and examines the vast prospects. With the continuous advancement of AI technology, the ability to recognize and track complex movements and expressions faster and more accurately, reduce human errors, enhance processing speed and efficiency. This advancement lowers technological barriers and accelerates the fusion of virtual and real worlds.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.06a
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• pp.37-38
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• 2007

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2126-2131
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• 2005

Interactions of a humanoid with a human are important, when the humanoid is requested to provide people with human-friendly services in unknown or uncertain environment. Such interactions may require more complicated and human-like behaviors from the humanoid. In this work the arm motions of a human are discussed as the early stage of human motion imitation by a humanoid. A motion capture system is used to obtain human-friendly arm motions as references. However the captured motions may not be applied directly to the humanoid, since the differences in geometric or dynamics aspects as length, mass, degrees of freedom, and kinematics and dynamics capabilities exist between the humanoid and the human. To overcome this difficulty a method to adapt captured motions to a humanoid is developed. The geometric difference in the arm length is resolved by scaling the arm length of the humanoid with a constant. Using the scaled geometry of the humanoid the imitation of actor's arm motions is achieved by solving an inverse kinematics problem formulated using optimization. The errors between the captured trajectories of actor arms and the approximated trajectories of humanoid arms are minimized. Such dynamics capabilities of the joint motors as limits of joint position, velocity and acceleration are also imposed on the optimization problem. Two motions of one hand waiving and performing a statement in sign language are imitated by a humanoid through dynamics simulation.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.199-202
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• 2002

We described a real-time 3D computer vision system called MIMIC(Motion interface f Motion information Capture system) that can capture and save motion of an actor. This system analyzes input images from vision sensors and searches feature information like a head, hands, and feet. Moreover, this estimates intermediated joints as an elbow and hee using feature information and makes 3D human model having 20 joints. This virtual human model mimics the motion of an actor in real-time. Therefore this system can realize the movement of an actor unaffectedly because of making intermediated joint for complete human body contrary to other marker-free motion capture system.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.10
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• pp.1095-1102
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• 2014

In this paper, We proposed the approach of GPU and motion vector to analysis the Human activity in real-time surveillance system. The most important part, that is detect blob(human) in the foreground. We use to detect Adaptive Gaussian Mixture, Weighted subtraction image for salient motion and motion vector. And then, We use motion vector for human activity analysis. In this paper, the activities of human recognize and classified such as meta-classes like this {Active, Inactive}, {Position Moving, Fixed Moving}, {Walking, Running}. We created approximately 300 conditions for the simulation. As a result, We showed a high success rate about 86~98%. The results also showed that the high resolution experiment by the proposed GPU-based method was over 10 times faster than the cpu-based method.

• Journal of Korea Multimedia Society
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• v.10 no.6
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• pp.716-725
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• 2007

In this paper we describe a motion tracking algorithm for 3D human animation using stereo vision system. This allows us to extract the motion data of the end effectors of human body by following the movement through segmentation process in HIS or RGB color model, and then blob analysis is used to detect robust shape. When two hands or two foots are crossed at any position and become disjointed, an adaptive algorithm is presented to recognize whether it is left or right one. And the real motion is the 3-D coordinate motion. A mono image data is a data of 2D coordinate. This data doesn't acquire distance from a camera. By stereo vision like human vision, we can acquire a data of 3D motion such as left, right motion from bottom and distance of objects from camera. This requests a depth value including x axis and y axis coordinate in mono image for transforming 3D coordinate. This depth value(z axis) is calculated by disparity of stereo vision by using only end-effectors of images. The position of the inner joints is calculated and 3D character can be visualized using inverse kinematics.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.483-486
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• 1999

In this paper, we present a real-time hand gesture recognition system that controls motion of a human avatar based on the pre-defined dynamic hand gesture commands in a virtual environment. Each motion of a human avatar consists of some elementary motions which are produced by solving inverse kinematics to target posture and interpolating joint angles for human-like motions. To overcome processing time of the recognition system for teaming, we use a Fuzzy Min-Max Neural Network (FMMNN) for classification of hand postures