Home  About us  Contact
 

Motion Capture (motion + capture)

Distribution by Scientific Domains
Information Science and Computing83%

Terms modified by Motion Capture

  • motion capture data
    		•
  • motion capture system
    		•

    Selected Abstracts

    Articulated Object Reconstruction and Markerless Motion Capture from Depth Video

    COMPUTER GRAPHICS FORUM, Issue 2 2008
    Yuri Pekelny
    Abstract We present an algorithm for acquiring the 3D surface geometry and motion of a dynamic piecewise-rigid object using a single depth video camera. The algorithm identifies and tracks the rigid components in each frame, while accumulating the geometric information acquired over time, possibly from different viewpoints. The algorithm also reconstructs the dynamic skeleton of the object, thus can be used for markerless motion capture. The acquired model can then be animated to novel poses. We show the results of the algorithm applied to synthetic and real depth video. [source]

    Capturing human motion using body-fixed sensors: outdoor measurement and clinical applications

    COMPUTER ANIMATION AND VIRTUAL WORLDS (PREV: JNL OF VISUALISATION & COMPUTER ANIMATION), Issue 2 2004
    Kamiar Aminian
    Abstract Motion capture is mainly based on standard systems using optic, magnetic or sonic technologies. In this paper, the possibility to detect useful human motion based on new techniques using different types of body-fixed sensors is shown. In particular, a combination of accelerometers and angular rate sensors (gyroscopes) showed a promising design for a hybrid kinematic sensor measuring the 2D kinematics of a body segment. These sensors together with a portable datalogger, and using simple biomechanical models, allow capture of outdoor and long-term movements and overcome some limitations of the standard motion capture systems. Significant parameters of body motion, such as nature of motion (postural transitions, trunk rotation, sitting, standing, lying, walking, jumping) and its spatio-temporal features (velocity, displacement, angular rotation, cadence and duration) have been evaluated and compared to the camera-based system. Based on these parameters, the paper outlines the possibility to monitor physical activity and to perform gait analysis in the daily environment, and reviews several clinical investigations related to fall risk in the elderly, quality of life, orthopaedic outcome and sport performance. Taking advantage of all the potential of these body-fixed sensors should be promising for motion capture and particularly in environments not suitable for standard technology such as in any field activity. Copyright © 2004 John Wiley & Sons, Ltd. [source]

    Multiple animated characters motion fusion

    COMPUTER ANIMATION AND VIRTUAL WORLDS (PREV: JNL OF VISUALISATION & COMPUTER ANIMATION), Issue 5 2002
    Luo Zhongxiang
    Abstract One of the major problems of the motion capture-based computer animation technique is the relatively high cost of equipment and low reuse rate of data. To overcome this problem, many motion-editing methods have been developed. However, most of them can only handle one character whose motions are preset, and hence cannot interact with its environment automatically. In this paper, we construct a new architecture of multiple animated character motion fusion, which not only enables the characters to perceive and respond to the virtual environment, but also allows them to interact with each other. We will also discuss in detail the key issues, such as motion planning, coordination of multiple animated characters and generation of vivid continuous motions. Our experimental results will further testify to the effectiveness of the new methodology. Copyright © 2002 John Wiley & Sons, Ltd. [source]

    Articulated Object Reconstruction and Markerless Motion Capture from Depth Video

    COMPUTER GRAPHICS FORUM, Issue 2 2008
    Yuri Pekelny
    Abstract We present an algorithm for acquiring the 3D surface geometry and motion of a dynamic piecewise-rigid object using a single depth video camera. The algorithm identifies and tracks the rigid components in each frame, while accumulating the geometric information acquired over time, possibly from different viewpoints. The algorithm also reconstructs the dynamic skeleton of the object, thus can be used for markerless motion capture. The acquired model can then be animated to novel poses. We show the results of the algorithm applied to synthetic and real depth video. [source]

    Dynamic postural stability during sit-to-walk transitions in Parkinson disease patients

    MOVEMENT DISORDERS, Issue 9 2008
    Thomas A. Buckley EdD
    Abstract In an effort to further our understanding of postural control in Parkinson's disease, we biomechanically evaluated the sit to walk task and its component tasks, sit to stand (STS) and gait initiation (GI) in 12 healthy older adults and 12 persons with Parkinson's disease (PWP). Performance was evaluated utilizing motion capture and two force plates. The major finding of this study was the inability of the PWP to appropriately merge the sequential component tasks (STS and GI) during STW. The PWP rose to nearly full height and had a longer delay between seat-off and gait initiation (P = 0.003 and P < 0.001, respectively) during STW. Additionally, the PWP moved with slower velocities leading to shorter, slower steps and decreased separation of the center of mass and center of pressure. These observed motor sequencing disturbances may be due to a disease related disability or limitations in proprioception, movement speed, muscular strength, and reduced general mobility. © 2008 Movement Disorder Society [source]