Unsupervised learning of haptic material properties

When touching the surface of an object, its spatial structure translates into a vibration on the skin. The perceptual system evolved to translate this pattern into a representation that allows to distinguish between different materials. Here we show that perceptual haptic representation of materials emerges from efficient encoding of vibratory patterns elicited by the interaction with materials. We trained a deep neural network with unsupervised learning (Autoencoder) to reconstruct vibratory patterns elicited by human haptic exploration of different materials. The learned compressed representation (i.e. latent space) allows for classification of material categories (i.e. plastic, stone, wood, fabric, leather/wool, paper, and metal). More importantly, distances between these categories in the latent space resemble perceptual distances, suggesting a similar coding. We could further show, that the temporal tuning of the emergent latent dimensions is similar to properties of human tactile receptors.

HARVEST: High-Resolution Haptic Vest and Fingertip Sensing Glove That Transfers Tactile Sensation of Fingers to the Back

Human fingertips are densely populated with tactile receptors and are hence incredibly sensitive. However, wearing gloves on the fingers drastically reduces the tactile information available to the fingertips, such as the texture and shape of the object, and makes it difficult to perform dexterous work. As a solution, in this study, we developed a high-resolution haptic vest that transfers the tactile sensation of the fingertips to the back. The haptic vest contains 80 voice-coil type vibrators which are located at each of the two discrimination thresholds on the back and can be driven independently. The tactile sensation of the fingertips is transferred to the back using the developed haptic vest in combination with a sensing glove that can detect the pressure distribution on the finger skin at up to 100 points. Different experiments were conducted to validate the performance of the proposed haptic vest and sensing gloves. The use of the haptic vest and the sensing glove enabled the user to perceive the shape of a planar object more accurately when compared to the case where the user wore only the glove.

Verbal realization of „FAILURE” and „НЕВДАЧА” concepts in contemporary American and Ukrainian fiction pieces: perception-cognitive and regulatory aspects

The article describes the features of the perception-cognitive and regulation macrostructure components of the FAILURE and НЕВДАЧА concepts in the epidigmatic plane on the basis of the text pieces selected from modern American and Ukrainian fiction. The analysis of these components of the concepts under consideration is carried out in the framework of the semantic cognitive approach by performing the cognitive interpretation procedure of the text pieces, the results of which have been used for formulating semantic cognitive features that constitute the perception-cognitive and regulation layers of the concept. The specificity of the regulatory aspect of understanding the essence of failure by the bearers of American and Ukrainian linguistic consciousness is clarified due to the semantic cognitive features that structure the regulatory zone of the concept macrostructure and update the strategies of the value and target block of the individual in the context of failure. It has been found that in the epidigmatic plane, in the process of conceptualizing failure by American and Ukrainian speakers, perceptual receptors dominate cognitive-mental thought projections. The visual analyzer is the most effective for the bearers of the American and Ukrainian linguistic consciousness in the context of sensory reflection of the failure category, the sound, taste, smell and tactile receptors are less effective. Cognitive subcomponents of the FAILURE and НЕВДАЧА concepts are structured due to vital and floromorphic metaphorical images; zoomorphic and aeromorphic images for the FAILURE concept as well as religious, aquamorphic and subject images for the НЕВДАЧА concept are considered nationally specific. The regulation zone of the FAILURE concept is significantly larger than the similar НЕВДАЧА concept zone, so for Ukrainian speakers rules and guidelines that help regulate areas covering the conceptual nature of failure are more important in understanding the failed outcome of action than for American ones.

Computational modeling indicates that surface pressure can be reliably conveyed to tactile receptors even amidst changes in skin mechanics

Distinct patterns in neuronal firing are observed between classes of cutaneous afferents. Such differences may be attributed to end-organ morphology, distinct ion-channel complements, and skin microstructure, among other factors. Even for just the slowly adapting type I afferent, the skin's mechanics for a particular specimen might impact the afferent's firing properties, especially given the thickness and elasticity of skin can change dramatically over just days. Here, we show computationally that the skin can reliably convey indentation magnitude, rate, and spatial geometry to the locations of tactile receptors even amid changes in skin's structure. Using finite element analysis and neural dynamics models, we considered the skin properties of six mice that span a representative cohort. Modeling the propagation of the surface stimulus to the interior of the skin demonstrated that there can be large variance in stresses and strains near the locations of tactile receptors, which can lead to large variance in static firing rate. However, variance is significantly reduced when the stimulus tip is controlled by surface pressure and compressive stress is measured near the end organs. This particular transformation affords the least variability in predicted firing rates compared with others derived from displacement, force, strain energy density, or compressive strain. Amid changing skin mechanics, stimulus control by surface pressure may be more naturalistic and optimal and underlie how animals actively explore the tactile environment.

Estimation of Displacement and Rotation by Magnetic Tactile Sensor Using Stepwise Regression Analysis

The human is covered with soft skin and has tactile receptors inside. The skin deforms along a contact surface. The tactile receptors detect the mechanical deformation. The detection of the mechanical deformation is essential for the tactile sensation. We propose a magnetic type tactile sensor which has a soft surface and eight magnetoresistive elements. The soft surface has a permanent magnet inside and the magnetoresistive elements under the soft surface measure the magnetic flux density of the magnet. The tactile sensor estimates the displacement and the rotation on the surface based on the change of the magnetic flux density. Determination of an estimate equation is difficult because the displacement and the rotation are not geometrically decided based on the magnetic flux density. In this paper, a stepwise regression analysis determines the estimate equation. The outputs of the magnetoresistive elements are used as explanatory variables, and the three-axis displacement and the two-axis rotation are response variables in the regression analysis. We confirm the regression analysis is effective for determining the estimate equations through simulation and experiment. The results show the tactile sensor measures both the displacement and the rotation generated on the surface by using the determined equation.

Review and Perspective: Sensory Feedback in Upper Limb Prosthesis

An emerging challenge in developing intelligent prostheses is to replicate or recreate the sensory functions of natural limbs for amputees. Such functions mainly include tactile sensation and proprioception. This paper reviews the tactile receptors and proprioceptors in human upper limb, the artificial sensors in upper limb prosthesis, and the sensory feedback technology used for reconstruction of lost sensory function in the amputee’s upper limb.