Development and validation of a new method for measuring friction between skin and nonwoven materials.

A new method for measuring the coefficient of friction between nonwoven materials and the curved surface of the volar forearm has been developed and validated. The method was used to measure the coefficient of static friction for three different nonwoven materials on the normal (dry) and over-hydrated volar forearms of five female volunteers (ages 18-44). The method proved simple to run and had good repeatability: the coefficient of variation (standard deviation expressed as a percentage of the mean) for triplets of repeat measurements was usually (80 per cent of the time) less than 10 per cent. Measurements involving the geometrically simpler configuration of pulling a weighted fabric sample horizontally across a quasi-planar area of volar forearm skin proved experimentally more difficult and had poorer repeatability. However, correlations between values of coefficient of static friction derived using the two methods were good (R = 0.81 for normal (dry) skin, and 0.91 for over-hydrated skin). Measurements of the coefficient of static friction for the three nonwovens for normal (dry) and for over-hydrated skin varied in the ranges of about 0.3-0.5 and 0.9-1.3, respectively. In agreement with Amontons' law, coefficients of friction were invariant with normal pressure over the entire experimental range (0.1-8.2 kPa).

Internal models in the cerebellum.

This review will focus on the possibility that the cerebellum contains an internal model or models of the motor apparatus. Inverse internal models can provide the neural command necessary to achieve some desired trajectory. First, we review the necessity of such a model and the evidence, based on the ocular following response, that inverse models are found within the cerebellar circuitry. Forward internal models predict the consequences of actions and can be used to overcome time delays associated with feedback control. Secondly, we review the evidence that the cerebellum generates predictions using such a forward model. Finally, we review a computational model that includes multiple paired forward and inverse models and show how such an arrangement can be advantageous for motor learning and control.

Optimal control predicts human performance on objects with internal degrees of freedom.

On a daily basis, humans interact with a vast range of objects and tools. A class of tasks, which can pose a serious challenge to our motor skills, are those that involve manipulating objects with internal degrees of freedom, such as when folding laundry or using a lasso. Here, we use the framework of optimal feedback control to make predictions of how humans should interact with such objects. We confirm the predictions experimentally in a two-dimensional object manipulation task, in which subjects learned to control six different objects with complex dynamics. We show that the non-intuitive behavior observed when controlling objects with internal degrees of freedom can be accounted for by a simple cost function representing a trade-off between effort and accuracy. In addition to using a simple linear, point-mass optimal control model, we also used an optimal control model, which considers the non-linear dynamics of the human arm. We find that the more realistic optimal control model captures aspects of the data that cannot be accounted for by the linear model or other previous theories of motor control. The results suggest that our everyday interactions with objects can be understood by optimality principles and advocate the use of more realistic optimal control models for the study of human motor neuroscience.