Extracting the Inertia Properties of the Human Upper Body Using Computer Vision

Dimitrios Menychtas (1) , Alina Glushkova (1) , Sotiris Manitsaris (1)

1 CAOR - Centre de Robotique

Currently, biomechanics analyses of the upper human body are mostly kinematic i.e., they are concerned with the positions, velocities , and accelerations of the joints on the human body with little consideration on the forces required to produces them. Tough kinetic analysis can give insight to the torques required by the muscles to generate motion and therefore provide more information regarding human movements, it is generally used in a relatively small scope (e.g. one joint or the contact forces the hand applies). The problem is that in order to calculate the joint torques on an articulated body, such as the human arm, the correct shape and weight must be measured. For robot manip-ulators, this is done by the manufacturer during the designing phase, however, on the human arm, direct measurement of the volume and the weight is very difficult and extremely impractical. Methods for indirect estimation of those parameters have been proposed, such as the use of medical imaging or standardized scaling factors (SF). However, there is always a trade off between accuracy and practicality. This paper uses computer vision (CV) to extract the shape of each body segment and find the inertia parameters. The joint torques are calculated using those parameters and they are compared to joint torques that were calculated using SF to establish the inertia properties. The purpose here is to examine a practical method for real-time joint torques calculation that can be personalized and accurate.

Domaines: Biomécanique [physics.med-ph]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Dimitrios Menychtas, Alina Glushkova, Sotiris Manitsaris. Extracting the Inertia Properties of the Human Upper Body Using Computer Vision. Computer Vision Systems, pp.596-603, 2019, ⟨10.1007/978-3-030-34995-0_54⟩. ⟨hal-02538310⟩