Why Early Tactile Speech Aids May Have Failed: No Perceptual Integration of Tactile and Auditory Signals.

Tactile speech aids, though extensively studied in the 1980's and 1990's, never became a commercial success. A hypothesis to explain this failure might be that it is difficult to obtain true perceptual integration of a tactile signal with information from auditory speech: exploitation of tactile cues from a tactile aid might require cognitive effort and so prevent speech understanding at the high rates typical of everyday speech. To test this hypothesis, we attempted to create true perceptual integration of tactile with auditory information in what might be considered the simplest situation encountered by a hearing-impaired listener.

The brain uses extrasomatic information to estimate limb displacement.

A fundamental problem faced by the brain is to estimate whether a touched object is rigidly attached to a ground reference or is movable. A simple solution to this problem would be for the brain to test whether pushing on the object with a limb is accompanied by limb displacement. The mere act of pushing excites large populations of mechanoreceptors, generating a sensory response that is only weakly sensitive to limb displacement if the movements are small, and thus can hardly be used to determine the mobility of the object.

Segregation of tactile input features in neurons of the cuneate nucleus.

Our tactile perception of external objects depends on skin-object interactions. The mechanics of contact dictates the existence of fundamental spatiotemporal input features-contact initiation and cessation, slip, and rolling contact-that originate from the fact that solid objects do not interpenetrate. However, it is unknown whether these features are represented within the brain.

Spatio-temporal skin strain distributions evoke low variability spike responses in cuneate neurons.

A common method to explore the somatosensory function of the brain is to relate skin stimuli to neurophysiological recordings. However, interaction with the skin involves complex mechanical effects. Variability in mechanically induced spike responses is likely to be due in part to mechanical variability of the transformation of stimuli into spiking patterns in the primary sensors located in the skin.

Optimization and variability of motor behavior in multifinger tasks: what variables does the brain use?

The neural control of movement has been described using different sets of elemental variables. Two possible sets of elemental variables have been suggested for finger pressing tasks: the forces of individual fingers and the finger commands (also called finger modes or central commands). The authors analyzed which of the 2 sets of the elemental variables is more likely used in the optimization of the finger force sharing and which set is used for the stabilization of performance.

Comparison of interfinger connection matrix computation techniques.

A hypothesis was proposed that the central nervous system controls force production by the fingers through hypothetical neural commands. The neural commands are scaled between values of 0 to 1, indicating no intentional force production or maximal voluntary contraction (MVC) force production, respectively. A matrix of interfinger connections transforms neural commands into finger forces.

Reconstruction of the unknown optimization cost functions from experimental recordings during static multi-finger prehension.

The goal of the research is to reconstruct the unknown cost (objective) function(s) presumably used by the neural controller for sharing the total force among individual fingers in multifinger prehension. The cost function was determined from experimental data by applying the recently developed Analytical Inverse Optimization (ANIO) method (Terekhov et al. 2010).

Minimal adhesion surface area in tangentially loaded digital contacts.

The stick-to-slip transition of a fingertip in contact with a planar surface does not occur instantaneously. As the tangential load increases, portions of the skin adhere while others slip, giving rise to an evolution of the contact state, termed partial slip. We develop a quasi-static model that predicts that if the coefficient of kinetic friction is larger than the coefficient of static friction, then the stuck surface area diminishes as the tangential load increases until reaching a 'minimal adhesion surface area' where it vanishes abruptly.

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.

An analytical approach to the problem of inverse optimization with additive objective functions: an application to human prehension.

We consider the problem of what is being optimized in human actions with respect to various aspects of human movements and different motor tasks. From the mathematical point of view this problem consists of finding an unknown objective function given the values at which it reaches its minimum. This problem is called the inverse optimization problem.

Intersegmental coordination during human locomotion: does planar covariation of elevation angles reflect central constraints?

To study intersegmental coordination in humans performing different locomotor tasks (backward, normal, fast walking, and running), we analyzed the spatiotemporal patterns of both elevation and joint angles bilaterally in the sagittal plane. In particular, we determined the origins of the planar covariation of foot, shank, and thigh elevation angles. This planar constraint is observable in the three-dimensional space defined by these three angles and corresponds to the plane described by the three time-varying elevation angle variables over each step cycle.