Grip force and force sharing in two different manipulation tasks with bottles

Grip force and force sharing during two activities of daily living were analysed experimentally in 10 right-handed subjects. Four different bottles, filled to two different levels, were manipulated for two tasks: transporting and pouring. Each test subject's hand was instrumented with eight thin wearable force sensors. The grip force and force sharing were significantly different for each bottle model. Increasing the filling level resulted in an increase in grip force, but the ratio of grip force to load force was higher for lighter loads. The task influenced the force sharing but not the mean grip force. The contributions of the thumb and ring finger were higher in the pouring task, whereas the contributions of the palm and the index finger were higher in the transport task. Mean force sharing among fingers was 30% for index, 29% for middle, 22% for ring and 19% for little finger. Practitioner Summary: We analysed grip force and force sharing in two manipulation tasks with bottles: transporting and pouring. The objective was to understand the effects of the bottle features, filling level and task on the contribution of different areas of the hand to the grip force. Force sharing was different for each task and the bottles features affected to both grip force and force sharing.

Analytical and numerical analysis of inverse optimization problems: conditions of uniqueness and computational methods

One of the key problems of motor control is the redundancy problem, in particular how the central nervous system (CNS) chooses an action out of infinitely many possible. A promising way to address this question is to assume that the choice is made based on optimization of a certain cost function. A number of cost functions have been proposed in the literature to explain performance in different motor tasks: from force sharing in grasping to path planning in walking. However, the problem of uniqueness of the cost function(s) was not addressed until recently. In this article, we analyze two methods of finding additive cost functions in inverse optimization problems with linear constraints, so-called linear-additive inverse optimization problems. These methods are based on the Uniqueness Theorem for inverse optimization problems that we proved recently (Terekhov et al., J Math Biol 61(3):423-453, 2010). Using synthetic data, we show that both methods allow for determining the cost function. We analyze the influence of noise on the both methods. Finally, we show how a violation of the conditions of the Uniqueness Theorem may lead to incorrect solutions of the inverse optimization problem.