Abstract
AbstractIn this paper, a method for decoupling the neuromuscular function of a set of limbs from the role morphology plays in regulating the performance of an activity is introduced. This method is based on two previous methods: the rescaled range analysis specific to time series data, and the use of scaling laws. A review of the literature suggests that limb geometry can either facilitate or constrain performance as measured experimentally. Whether limb geometry is facilitatory or acts as a constraint depends on the size differential between arm morphology and the underlying muscle. "Changes in size and shape" are theoretically extrapolations of morphological geometry to other members of a population or species, to other species, or to technological manipulations of an individual via prosthetic devices. Three datasets are analyzed using the range-based method and a Monte-Carlo simulation, and are used to test the various ways of executing this analysis. It was found that when performance is kept stable but limb size and shape is scaled by a factor of .25, the greatest gain in performance results. It was also found that introducing force-based perturbations results in 'shifts' in the body geometry/performance relationship. While results such as this could be interpreted as a statistical artifact, the non-linear rise within a measurement class and linear decrease between measurement classes suggests an effect of scale in the optimality of this relationship. Overall, range-based techniques allow for the simulation and modeling of myriad changes in phenotype that result from biological and technological manipulation.
Highlights
Neuromechanical systems are adaptive entities that involve interplay between adaptation, the automation of behavior by the nervous system, and the constraints of limb geometry (Enoka, 2002)
D is a biomechanical model of relationships between the scaling factor and the effect it would have on the arm
There are at least three ways to get at this: rescale data based on limb geometry and performance measures by a series of percentages, finding an optimal point for the confluence of morphological size and performance indicators, and simulating existing morphological performance relationships to look for changes under a number of environmental conditions
Summary
Neuromechanical systems are adaptive entities that involve interplay between adaptation, the automation of behavior by the nervous system, and the constraints of limb geometry (Enoka, 2002). In both figures, mapped physiological output and the size (blue) and shape (red) scalings were reduced by 25 (.25), 50 (.5), and 75 (.75) percent. Reducing the performance measurement while keeping the morphological scaling constant results in a large increase in the range-based measurement, and indicates significant hyper-allometry. As in the case of mapped physiological output, reducing the performance measurement by 50 percent (0.50) increases the range-based measurement by a similar magnitude for both muscles. Similar to what is observed with the mapped physiological output performance measurement, the size scaling shows a larger effect than the shape scaling In this instance, the size transformation of the limb geometry data is inherently hyper-allometric, while the shape transformation is inherently hypo-allometric. D is a biomechanical model of relationships between the scaling factor and the effect it would have on the arm
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