Kinematics Analysis

The computer graphic biomechanical models allow kinematics analysis of the musculoskeletal joints. Kinematic data is applied to the graphic models to animate experimentally measured or theoretical joint motions. Several techniques can be used to record joint motions experimentally. The most commonly used devices are 3D video capture systems [10], electromagnetic sensors [2], and electrogoniometers [28].

Animation of experimentally measured motions requires construction of a global coordinate system within a workstation environment that matches the global coordinate system used experimentally. Typically, external markers define the position of each limb segment throughout the motions. These markers should be rigidly fixed to the bones of interest, but often are fixed to the skin, creating experimental artifact due to the relative motion of the skin with respect to the bone. The markers generally are positioned on the body with respect to anatomic landmarks. The marker positions are quantified within a global coordinate system during experimental testing. The graphical model allows recreation of the marker positions with respect to the same anatomic landmarks on the computer workstation. The global coordinate system is recreated within the workstation environment to animate the model according to the recorded motions (Fig. 2). The markers could also be imaged and graphically reconstructed with the body segments during model development. Shape-matching algorithms can define the orientation of the markers on the graphic models. The markers and the attached bones can be reoriented to match the positions recorded during experimental testing.

Once the experimental or theoretical joint motions are reproduced on the workstation, the model can be used to quantify the joint motion with respect to an anatomically defined coordinate system. Typically, Euler rotations are used to describe joint rotations with respect to anatomical reference axes [4,8,17,36]. Anatomic features such as bony landmarks,

FIGURE 1 An image-based model of the shoulder created from the Visible Human dataset. The model is shown with all of the surrounding musculature intact (a), and after removing the musculature to highlight the bones and nerves of the shoulder (b). See also Plate 32.

FIGURE 1 An image-based model of the shoulder created from the Visible Human dataset. The model is shown with all of the surrounding musculature intact (a), and after removing the musculature to highlight the bones and nerves of the shoulder (b). See also Plate 32.

FIGURE 2 A model of the bones of the shoulder and the attached electromagnetic sensors showing passive humerus abduction in the plane of the scapula. The model was constructed from CT data of a cadaver specimen used for kinematic testing. The motions recorded by the electromagnetic sensors were applied to the model to animate the experimental motions. The scapula and clavicle rotations between 0° of abduction and 90° of abduction were minimal. Humerus abduction beyond 90° of elevation produced noticeable scapula and clavicle rotations. See also Plate 33.

FIGURE 2 A model of the bones of the shoulder and the attached electromagnetic sensors showing passive humerus abduction in the plane of the scapula. The model was constructed from CT data of a cadaver specimen used for kinematic testing. The motions recorded by the electromagnetic sensors were applied to the model to animate the experimental motions. The scapula and clavicle rotations between 0° of abduction and 90° of abduction were minimal. Humerus abduction beyond 90° of elevation produced noticeable scapula and clavicle rotations. See also Plate 33.

longitudinal axes of long bones, and centers of spherical bone surfaces are used to define the anatomic coordinate systems. Without a graphical model, landmark digitization is used to create all of the reference axes. Therefore, reference axes based on bone shape may include significant error. In addition, palpation through the skin also creates experimental errors during landmark digitization. For a graphically reconstructed joint, the joint coordinate systems can be developed with access to the entire bone geometry, allowing the most accurate axis definitions possible.

Graphic animation of the joint motions provides the additional benefit of carrying the entire bone surface during the motion. The defined bone surfaces can be used to quantify the proximity between bones during the joint motions using collision detection or distance measurement algorithms. This information is particularly relevant when investigating bone-on-bone impingement or nerve entrapment during joint motion. This quantitative flexibility is not available during experimental quantification of joint motion.

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