Modelling the 3D pose of a human arm and the shoulder complex utilising only two parameters

Abstract

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.