Dynamic motion planning of 3D human locomotion using gradient-based optimization.

Since humans can walk with an infinite variety of postures and limb movements, there is no unique solution to the modeling problem to predict human gait motions. Accordingly, we test herein the hypothesis that the redundancy of human walking mechanisms makes solving for human joint profiles and force time histories an indeterminate problem best solved by inverse dynamics and optimization methods. A new optimization-based human-modeling framework is thus described for predicting three-dimensional human gait motions on level and inclined planes. The basic unknowns in the framework are the joint motion time histories of a 25-degree-of-freedom human model and its six global degrees of freedom. The joint motion histories are calculated by minimizing an objective function such as deviation of the trunk from upright posture that relates to the human model's performance. A variety of important constraints are imposed on the optimization problem, including (1) satisfaction of dynamic equilibrium equations by requiring the model's zero moment point (ZMP) to lie within the instantaneous geometrical base of support, (2) foot collision avoidance, (3) limits on ground-foot friction, and (4) vanishing yawing moment. Analytical forms of objective and constraint functions are presented and discussed for the proposed human-modeling framework in which the resulting optimization problems are solved using gradient-based mathematical programming techniques. When the framework is applied to the modeling of bipedal locomotion on level and inclined planes, acyclic human walking motions that are smooth and realistic as opposed to less natural robotic motions are obtained. The aspects of the modeling framework requiring further investigation and refinement, as well as potential applications of the framework in biomechanics, are discussed.

How do tissues respond and adapt to stresses around a prosthesis? A primer on finite element stress analysis for orthopaedic surgeons.

Joint implant design clearly affects long-term outcome. While many implant designs have been empirically-based, finite element analysis has the potential to identify beneficial and deleterious features prior to clinical trials. Finite element analysis is a powerful analytic tool allowing computation of the stress and strain distribution throughout an implant construct. Whether it is useful depends upon many assumptions and details of the model. Since ultimate failure is related to biological factors in addition to mechanical, and since the mechanical causes of failure are related to load history, rather than a few loading conditions, chief among them is whether the stresses or strains under limited loading conditions relate to outcome. Newer approaches can minimize this and the many other model limitations. If the surgeon is to critically and properly interpret the results in scientific articles and sales literature, he or she must have a fundamental understanding of finite element analysis. We outline here the major capabilities of finite element analysis, as well as the assumptions and limitations.