SEGMENT INERTIAL PROPERTIES OF PRIMATES - NEW TECHNIQUES FOR LABORATORY AND FIELD STUDIES OF LOCOMOTION

Studies of the dynamics of locomotor performances depend on knowledge of the distribution of body mass within and between limb segments. How ever, these data are difficult to derive. Segment mass properties have generally been estimated by modelling limbs as truncated cones, but t his approach fails to take into account that some segments are of elli ptical, not circular, cross section; and further, the profiles of real segments are generally curved. Thus, they are more appropriately mode lled as solids of revolution, described by the rotation in space of co nvex or concave curves, and the possibility of an elliptical cross sec tion needs to be taken into account. In this project we have set out t o develop a general geometric model which can take these factors into account, and permit segment inertial properties to be derived from cad avers by segmentation, and from living individuals using linear extern al measurements. We present a model which may be described by up to fo ur parameters, depending on the profile and serial cross section (circ ular or ellipsoidal) of the individual segments. The parameters are ob tained from cadavers using a simplified complex-pendulum technique, an d from intact specimens by calculation from measurements of segment di ameters and lengths. From the parameters, the center of mass, moments of inertia, and radii of gyration may be derived, using simultaneous e quations. Inertial properties of the body segments of four Pan. troglo dytes and a single Pongo were determined, and contrasted to comparable findings for humans. Using our approach, the mass distribution charac teristics of any individual or species may be represented by a rigid-l ink segment model or ''android.'' If this is made to move according to motion functions derived from a real performance of the individual re presented, we show that recordings of resulting ground reaction forces may be quite closely simulated by predictive dynamic modelling. (C) 1 996 Wiley-Liss, Inc.