• Journal of the Ergonomics Society of Korea
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• v.12 no.1
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• pp.57-73
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• 1993

In this paper, methods of human figure representation in computer graphics are described. Many applications of the human figure representation are found in areas including industry, advertisement, and cartoon production and further research for the methods that show the human figure more realistically is ex- pected. Two analytic methods for human model, kinematics and dynamics, are ex- plained and the characteristics of the man-machine interface systems that include human figure representation are presented. Various techniques of the human figure representation based on kinematics or(and) dynamics are discussed and representation methods of human body segments such as hand, face, spine are introduced in this paper.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.159-162
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• 2007

The inverse kinematics problem in robotics is an essential work for grasping and manipulation tasks by robotic and humanoid hands. In this paper, an intelligent neural learning scheme for solving such inverse kinematics of humanoid fingers is presented. Specifically, a multi-layered neural network is utilized for effective inverse kinematics, where a dynamic neural learning algorithm is employed. Also, a bio-mimetic feature of general human fingers is incorporated to the learning scheme. The usefulness of the proposed approach is verified by simulations.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.6
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• pp.39-47
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• 2010

A research of image-based articulated pose estimation has some problems such as detection of human feature, precise pose estimation, and real-time performance. In particular, various methods are currently presented for recovering many joints of human body. We propose the novel numerical inverse kinematics improved with the UKF(unscented Kalman filter) in order to estimate the human pose in real-time. An existing numerical inverse kinematics is required many iterations for solving the optimal estimation and has some problems such as the singularity of jacobian matrix and a local minima. To solve these problems, we combine the UKF as a tool for optimal state estimation with the numerical inverse kinematics. Combining the solution of the numerical inverse kinematics with the sampling based UKF provides the stability and rapid convergence to optimal estimate. In order to estimate the human pose, we extract the interesting human body using both background subtraction and skin color detection algorithm. We localize its 3D position with the camera geometry. Next, through we use the UKF based numerical inverse kinematics, we generate the intermediate joints that are not detect from the images. Proposed method complements the defect of numerical inverse kinematics such as a computational complexity and an accuracy of estimation.

• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.862-868
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• 2007

The planar motion of the index finger in general human hands is usually implemented by the actuation of three joints. This task requires a technique to determine the joint combination for each fingertip position which is well-known as the inverse kinematics problem in robotics. Especially, it is an essential work for grasping and manipulation tasks by robotic and humanoid fingers. In this paper, an intelligent neural learning scheme for solving such inverse kinematics is presented. Specifically, a multi-layered neural network is utilized for effective inverse kinematics, where a dynamic neural learning algorithm is employed for fast learning. Also, a bio-mimetic feature of general human fingers is incorporated to the learning scheme. The usefulness of the proposed approach is verified by simulations.

• ETRI Journal
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• v.40 no.4
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• pp.511-521
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• 2018

The ability to realize human-motion imitation using robots is closely related to developments in the field of artificial intelligence. However, it is not easy to imitate human motions entirely owing to the physical differences between the human body and robots. In this paper, we propose a work chain-based inverse kinematics to enable a robot to imitate the human motion of upper limbs in real time. Two work chains are built on each arm to ensure that there is motion similarity, such as the end effector trajectory and the joint-angle configuration. In addition, a two-phase filter is used to remove the interference and noise, together with a self-collision avoidance scheme to maintain the stability of the robot during the imitation. Experimental results verify the effectiveness of our solution on the humanoid robot Nao-H25 in terms of accuracy and real-time performance.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.04a
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• pp.147-151
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• 1999

This paper extends and enhances the existing inverse kinematics technique using the concept of motion characteristic capturing. Motion characteristic capturing is not about measuring motion by tracking body points. Instead, it starts from pre-measured motion data, extracts the motion characteristics, and applies them in animating other bodies. The resulting motion resembles the originally measured one in spite of arbitrary dimensional differences between the bodies. Motion characteristics capturing is a new principle in kinematic motion generalization to process measurements and generate realistic animation of human being or other living creatures.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.144-150
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• 2000

In this paper, we introduce a modified six-degrees-of-freedom parallel-link manipulator, which will be applied to the human vibration experiments. We analyze the inverse kinematics and workspace of this manipulator and comprehend the characteristics of kinematics analyzed. Additionally, solutions of forward kinematics are obtained through the iterative Newton-Raphson method known as one of the most used numerical analysis. Finally, dynamic equation of the manipulator is derived in closed form through the Newton-Euler approach, which will be used for the development of control software.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.171-177
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• 2005

This paper describes the method of calculating coordinate using Forward Kinematics and expresses the recursive equation as the numerical formula using a homogeneous coordinate for creating workspace. This paper proposes an algorithm for the workspace of human model using the recursive equation and the joint limit angle of human model, and describes the results of workspace of the human model as computer graphics.

• Korean Journal of Computational Design and Engineering
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• v.12 no.6
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• pp.429-440
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• 2007

The purpose of this research is to develop the Human-centered CAD system in which human factors can be considered during the design stage. For this system there are several issues to research, like the digital human modeling technology, the definition of interactions between human and product, the simulation of human motion when using the product, and the bio-mechanical analysis of human, etc. This paper introduces how to construct the kinematical structure of the digital human model. For our digital human model H-ANIM, the international specification of humanoid animation is referenced. And we added the skeleton geometry and the skin surfaces to our model. And it can manipulate its joints by forward kinematics. Also the IKAN inverse kinematics algorithm is adopted to support the posture prediction of the digital human model in the product environment. All of these ideas are implemented using CAD API so that we can apply these functions to the current commercial CAD systems. In this manner, the human factor issues can be effectively taken into account at the early design phase and the costs of bio-mechanical evaluation will be significantly reduced.