Control of static posture is constrained by multiple sensory inputs, motor ability, and task constraints. Development of static postural control across the lifespan can be analyzed effectively using nonlinear analyses of center of pressure (CoP) time series, including approximate and sample entropy. In this paper, the key findings from studies using nonlinear analysis tools are reviewed to describe the development of postural control. Preschool children learn to adopt relatively unstable postures (e.g., standing) in which the regularity of CoP initially increases as a consequence of restricting mechanical degrees of freedom. As children age, CoP regularity decreases as degrees of freedom are released, thus enabling a more functional, adaptable type of postural control. Changes to sensory inputs or task constraints also affect the regularity of CoP sway. For example, removing vision, adding vibration, or imposing dual-task conditions affect performer’s CoP regularity differently. One limitation of approximate and sample entropy analysis is the influence of different input parameters on the output and subsequent interpretation. Ongoing refinement to entropy analysis tools concern determining appropriate values for the length of sequence to be matched and the tolerance level used with CoP data.
Neil Anderson and Chris Button
Donald D. Anderson, Krishna S. Iyer, Neil A. Segal, John A. Lynch and Thomas D. Brown
There exist no large-series human data linking contact stress exposure to an articular joint’s propensity for developing osteoarthritis because contact stress analysis for large numbers of subjects remains impractical. The speed and simplicity of discrete element analysis (DEA) for estimating contact stresses makes its application to this problem highly attractive, but to date DEA has been used to study only a small numbers of cases. This is because substantial issues regarding its use in population-wide studies have not been addressed. Chief among them are developing fast and robust methods for model derivation and the selection of boundary conditions, establishing accuracy of computed contact stresses, and including capabilities for modeling in-series structural elements (e.g., a meniscus). This article describes an implementation of DEA that makes it feasible to perform subject-specific modeling in articular joints in large population-based studies.