Section: New Results
Keywords : Virtual Human, Mechanics and Biomechanics, Motion and skeleton description, Dynamics.
Mechanical description of virtual human motion and skeleton.
Our aim is to be able to generate plausible motions for virtual humans. To do so, we develop a generic mechanical representation of the skeletons and propose an algorithm that matches motion on a target skeleton. By using morphological tables and regression equations, the dynamic parameters of the body links are automatically calculated with respect to the gender of the virtual human. To do so we use anthropological tables and regression equations, and finally we estimate support phases giving us the possibility of dynamics calculations.
Mechanical description of virtual human: motion and skeleton
The purpose is to represent motion for virtual humans. As the motion obtained by methods using kinematics or kinetics are often a bit jerky, we try to use dynamics to adapt acquired motions (on real subjects) on modeled humans. Generic motion is represented by angular parameters expressed on a mechanical skeleton. The first step is to load a real captured motion or a file exported from our kinematic interpolator (see below). As motion acquisition protocols are not standard, we can, by analyzing the motion, create additional virtual markers (by replacement, renaming or average) for this motion. The description of the mechanical skeleton is based on the Denavit-Hartenberg modified notation and is extracted from the motion and the landmark configuration. This Denavit-Hartenberg notation seems to be well suited to the description of skeletons. Four parameters are used to describe each degree of freedom (DOF) in the kinematic chain. Figure 8 shows a set of real landmarks and the representation of the a human kinematical chain for which the parameters are automatically extracted.
Forward kinematics on this chain leads to the three-dimensional positions of the articulations. At this point, some treatments may be applied to the motion as smoothness by splines or global motion reorientation. According to the markers labels, the automatic identification of the limbs is then performed. This association may also be specified by the user. This identification allows us to use morphological tables for computing dynamic parameters of the limbs. The obtained skeleton is constituted of rigid bodies representing the limbs. The mass or inertia due to the soft tissues (muscles)is taken into account, but the deformation of these muscles is not embedded in our model.
Now we use a simplified inverse kinematics algorithm for identifying of the skeleton parameters. This algorithm treats iteratively the articulation systems by adjusting the DOFs of each joint in order to obtain a position as close as possible to the original position.
In order to solve dynamics equations, we need to know external forces applied on the kinematic chain. In the case of walk motion, these forces are only the ground reaction forces. We compute the norms of these forces from the motion, in particular by using the acceleration of the center of gravity. We need to access to the support phases and non-support phases for each effector in order to determine if there is a ground reaction (support phase) or not. To this end, we have compared different approaches with a large choice of parameters and discussed the best values to automatically determine the support phase. The reference for this identification is the visual identification of the support phase. It seems that the best choice is to use speed inversion with threshold to determine these phases. The markers (real) representing the extremity (foot) of the chain is also very important. The best results are obtained with a complete set of landmarks on the feet (talus and foot). These evaluations allow to perform the dynamics calculations leading to forces and moments between links. Figure 9 shows the user interface that controls the presented algorithms.
As previously mentioned, the original data of motion are either files issued from motion capture or files exported from our kinematic interpolator. This interpolator was developed within the framework of an ATIP CNRS grant dedicated to the morphological and stance adaptation of model of locomotion for virtual humans. We have so developed a computer tool for testing hypotheses and generating a plausible walk according to anatomical knowledge. To do so, we introduced an interpolation method based on morphological data, and both stance and footprint hypotheses  . This interpolation is combined with an inverse kinematics solver in order to produce motion ensuring the respect of joint limits, the minimisation of the rotational kinetic energy and the respect of the posture of reference  . The main applicative field of this study was the anthropology, contributing to draw a plausible walk for early hominids using their anatomical and osteological data. We worked especially on the Australopithecus Afarensis Lucy (A.L. 288-1) skeleton.