Simulating humans: computer graphics animation and control
Simulating humans: computer graphics animation and control
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Real-time inverse kinematics techniques for anthropomorphic limbs
Graphical Models and Image Processing
A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Introduction to Robotics: Mechanics and Control
Introduction to Robotics: Mechanics and Control
Head Tracking by Active Particle Filtering
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Sequential Monte Carlo Tracking of Body Parameters in a Sub-Space
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
View invariant gesture recognition using the CSEM SwissRanger SR-2 camera
International Journal of Intelligent Systems Technologies and Applications
An experimental characterization of a 1-DOF anthropomorphic arm for humanoid robots
ICS'09 Proceedings of the 13th WSEAS international conference on Systems
ARMin III --arm therapy exoskeleton with an ergonomic shoulder actuation
Applied Bionics and Biomechanics
Combination of annealing particle filter and belief propagation for 3D upper body tracking
Applied Bionics and Biomechanics - Personal Care Robotics
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In model-based computer vision it is necessary to have a geometric model of the object the pose of which is being estimated. In this paper a very compact model of the human shoulder complex and arm is presented. First an investigation of the anatomy of the arm and the shoulder is conducted to identify the primary joints and degrees of freedom. To model the primary joints we apply image features and clinical data. Our model only requires two parameters to describe the configuration of the arm. It is denoted the local screw axis model since a new representation is produced for each image. In the light of this model we have a closer look at the parameters in the shoulder complex. We show how to eliminate the effects of these parameters by estimating their values in each image. This is done based on experimental data found in the literature together with an investigation of the movement of the bones in the shoulder -- the "shoulder rhythm" -- as a function of the position of the hand in the image. Finally we justify our approach by comparing the model with real data. It is shown that the model behaviour is consistent with real arm movements, and the model is thus validated.