The 81st Annual Meeting of the American Association of Physical Anthropologists (2012)


A model-based approach to compute 3D bipedal locomotion based on anthropological data – application to non-human primates and early hominids

GUILLAUME NICOLAS1, FRANCK MULTON1 and GILLES BERILLON2.

1M2S Laboratory (Physiology and Biomechanics), University Rennes 2, 2UPR 2147 CNRS, Dynamique de l'Evolution Humaine: individus, populations, espèces

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Identification of locomotion laws requires understanding the anatomical structures and the mechanisms involved during this specific movement. To this end, experimental studies in biomechanics have highlighted numerous principles such as minimizing energy, Jerk or joint torques. In palaeoanthropology, experiments are obviously not possible when studying fossil specimens. In this study, we propose a model-based approach of the pelvic girdle and hindlimb to compute 3D bipedal locomotion while taking into account anatomical data. This method was designed to test palaeoanthropological hypotheses due to some uncertainties in the reconstruction of skeletons (e.g., long bones dimensions, femoral bicondylar angle) and evaluate their impact on the predicted locomotion. Contrary to musculosketetal models which require many accurate knowledge (which is missing for the fossil), our approach combines an inverse kinematics method and an optimization loop which aims at modifying a reference ankle trajectory until a set of criteria is minimized. The goal is to adapt angular trajectories in order to satisfy a set of constraints (e.g., footprints) while minimizing energy criteria. This method is tested on 10 modern humans. Results obtained with this method are close to experimental data and mean error on internal work is less than 6%. We also apply our method to a skeleton of Pan paniscus (Bonobo), and show that the resulting motion is close to those commonly described in comparative biomechanics. Finally, we test our method to a specimen of Australopithecus afarensis (A.L. 288-1) and show that step length influences internal work and may lead to an optimal value.

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