• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2009.01a
• /
• pp.764-767
• /
• 2009

Motion capture systems allow to measure the precise position of markers on the human body in real time. These captured motion data, the marker position data, have to be fitted by a human skeleton model to represent the motion of the human. Typical human skeleton models approximate the joints using a ball joint model. However, because this model cannot represent the human skeleton precisely, errors between the motion data and the movements of the simplified human skeleton model happen. We propose in this paper a method for measuring a translation component of wrist, and elbow joints on upper limb using optical motion capture system. Then we study the errors between the ball joint model and acquired motion data. In addition, we discuss the problem to estimate motion of human joint using optical motion capture system.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.9
• /
• pp.4624-4644
• /
• 2019

With the development of films, games and animation industry, analysis and reuse of human motion capture data become more and more important. Human motion segmentation, which divides a long motion sequence into different types of fragments, is a key part of mocap-based techniques. However, most of the segmentation methods only take into account low-level physical information (motion characteristics) or high-level data information (statistical characteristics) of motion data. They cannot use the data information fully. In this paper, we propose an unsupervised framework using both low-level physical information and high-level data information of human motion data to solve the human segmentation problem. First, we introduce the algorithm of CFSFDP and optimize it to carry out initial segmentation and obtain a good result quickly. Second, we use the ACA method to perform optimized segmentation for improving the result of segmentation. The experiments demonstrate that our framework has an excellent performance.

• Journal of the Korea Safety Management & Science
• /
• v.15 no.2
• /
• pp.55-61
• /
• 2013

For years, many studies have mainly been investigated in a complicated human motion analysis. Recently, many motion analysis equipments have been studied and developed. Therefore, the more complex human movement analyses are possible, we have enabled us to perform more and more complicated human movement analyses. A Three-dimensional(3D) motion analysis on of the several methods is a useful tool for analyzing the human motion analysis. The purpose of this study was to develop the 3D human motion analysis using a kalman filter algorithm and a gyro sensor. The algorithm and sensor were used to human motion analysis with high-speed motion capture. In this study, the developed system will be adapted to facilitate golf swing analysis. Using the developed system, golfers and coaches who do not have advanced biomechanical knowledge can easily be used to their golf swing analysis. Future study is necessary for more practical and efficient area such as other sports industries, 3D game industries, rehabilitation training, etc..

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.383-383
• /
• 2000

In this paper, a motion adaptation control is applied for animation of 3-D human character. The method includes parameterization of joint motion data, motion adaptation based on body ratio of character, dynamic adaptation using genetic algorithm, etc. The feasibility of motion adaptation technique is verified by applying to motion control and adaptation of a 3-D human character.

• Proceedings of the Korea Multimedia Society Conference
• /
• 1998.04a
• /
• pp.262-267
• /
• 1998

A hierarchical motion control system for animation of 3D human character is implemented using the motion database in realtime. The proposed motion control system consists of coordination controller for gait timing and balancing of walking motion, joint servo controller for realistic limb movement, and motion database for goal-directed character animation which makes time-consuming animation relatively easy task. As one example among the various applications of the proposed motion control system. We present a simple virtual reality system in which the motion control system plays a central role in generating realistic motion of virtual human character.

• The Journal of Korea Robotics Society
• /
• v.3 no.4
• /
• pp.287-299
• /
• 2008

People have expected a humanoid robot to move as naturally as a human being does. The natural movements of humanoid robot may provide people with safer physical services and communicate with persons through motions more correctly. This work presented a methodology to generate the natural motions for a humanoid robot, which are converted from human motion capture data. The methodology produces not only kinematically mapped motions but dynamically mapped ones. The kinematical mapping reflects the human-likeness in the converted motions, while the dynamical mapping could ensure the movement stability of whole body motions of a humanoid robot. The methodology consists of three processes: (a) Human modeling, (b) Kinematic mapping and (c) Dynamic mapping. The human modeling based on optimization gives the ZMP (Zero Moment Point) and COM (Center of Mass) time trajectories of an actor. Those trajectories are modified for a humanoid robot through the kinematic mapping. In addition to modifying the ZMP and COM trajectories, the lower body (pelvis and legs) motion of the actor is then scaled kinematically and converted to the motion available to the humanoid robot considering dynamical aspects. The KIST humanoid robot, Mahru, imitated a dancing motion to evaluate the methodology, showing the good agreement in the motion.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.2
• /
• pp.1019-1031
• /
• 2017

In this study, we propose a new method for simulating water responding to human motion. Motion data obtained from motion-capture devices are represented as a jointed skeleton, which interacts with the velocity field in the water simulation. To integrate the motion data into the water simulation space, it is necessary to establish a mapping relationship between two fields with different properties. However, there can be severe numerical instability if the mapping breaks down, with the realism of the human-water interaction being adversely affected. To address this problem, our method extends the joint velocity mapped to each grid point to neighboring nodes. We refine these extended velocities to enable increased robustness in the water solver. Our experimental results demonstrate that water animation can be made to respond to human motions such as walking and jumping.

• Journal of Sensor Science and Technology
• /
• v.26 no.6
• /
• pp.391-396
• /
• 2017

Human body motion detection is important in several industry sectors, such as entertainment, healthcare, rehabilitation, and so on. In this paper, we first discuss commercial human motion detection technologies (optical markers, MEMS acceleration sensors, infrared imaging, etc.) and then explain recent advances in the development of flexible and stretchable strain sensors for human motion detection. In particular, flexible and stretchable strain sensors that are fabricated using carbon nanotubes, silver nanowires, graphene, and other materials are reviewed.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.3 no.1
• /
• pp.84-95
• /
• 2009

The ubiquitous smart home is the home of the future, which exploits context information from both the human and the home environment, providing an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. In this paper, we present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. The system uses four network cameras for real-time human tracking. This paper explains the architecture of the real-time human tracker, and proposes an algorithm for predicting human location and motion. To detect human location, three kinds of images are used: $IMAGE_1$ - empty room image, $IMAGE_2$ - image of furniture and home appliances, $IMAGE_3$ - image of $IMAGE_2$ and the human. The real-time human tracker decides which specific furniture or home appliance the human is associated with, via analysis of three images, and predicts human motion using a support vector machine (SVM). The performance experiment of the human's location, which uses three images, lasted an average of 0.037 seconds. The SVM feature of human motion recognition is decided from the pixel number by the array line of the moving object. We evaluated each motion 1,000 times. The average accuracy of all types of motion was 86.5%.

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