Behnam Khojasteh obtained his B.Sc. and M.Sc. degree in Mechanical Engineering and Mechatronics from TU Hamburg. While studying abroad at UC Santa Barbara as a MS student, Khojasteh completed coursework and conducted research in Prof. Visell’s RE Touch lab. There, his duties as a graduate research assistant comprised cutaneous sliding friction as well as hand wave propagation projects of haptic interaction.
Nature Scientific Reports, 8(13710), September 2018 (article)
Sliding friction between the skin and a touched surface is highly complex, but lies at the heart of our ability to discriminate surface texture through touch. Prior research has elucidated neural mechanisms of tactile texture perception, but our understanding of the nonlinear dynamics of frictional sliding between the finger and textured surfaces, with which the neural signals that encode texture originate, is incomplete. To address this, we compared measurements from human fingertips sliding against textured counter surfaces with predictions of numerical simulations of a model finger that resembled a real finger, with similar geometry, tissue heterogeneity, hyperelasticity, and interfacial adhesion. Modeled and measured forces exhibited similar complex, nonlinear sliding friction dynamics, force fluctuations, and prominent regularities related to the surface geometry. We comparatively analysed measured and simulated forces patterns in matched conditions using linear and nonlinear methods, including recurrence analysis. The model had greatest predictive power for faster sliding and for surface textures with length scales greater than about one millimeter. This could be attributed to the the tendency of sliding at slower speeds, or on finer surfaces, to complexly engage fine features of skin or surface, such as fingerprints or surface asperities. The results elucidate the dynamical forces felt during tactile exploration and highlight the challenges involved in the biological perception of surface texture via touch.
Extended abstract presented in form of an oral presentation at the 3rd International Conference on BioTribology (ICoBT), London, England, September 2016 (misc)
The biomechanics of the human finger pad has been investigated in relation to motor behaviour and sensory function in the upper limb. While the frictional properties of the finger pad are important for grip and grasp function, recent attention has also been given to the roles played by friction when perceiving a surface via sliding contact. Indeed, the mechanics of sliding contact greatly affect stimuli felt by the finger scanning a surface. Past research has shed light on neural mechanisms of haptic texture perception, but the relation with time-resolved frictional contact interactions is unknown. Current biotribological models cannot predict time-resolved frictional forces felt by a finger as it slides on a rough surface. This constitutes a missing link in understanding the mechanical basis of texture perception.
To ameliorate this, we developed a two-dimensional finite element numerical simulation of a human finger pad in sliding contact with a textured surface. Our model captures bulk mechanical properties, including hyperelasticity, dissipation, and tissue heterogeneity, and contact dynamics. To validate it, we utilized a database of measurements that we previously captured with a variety of human fingers and surfaces. By designing the simulations to match the measurements, we evaluated the ability of the FEM model to predict time-resolved sliding frictional forces. We varied surface texture wavelength, sliding speed, and normal forces in the experiments.
An analysis of the results indicated that both time- and frequency-domain features of forces produced during finger-surface sliding interactions were reproduced, including many of the phenomena that we observed in analyses of real measurements, including quasiperiodicity, harmonic distortion and spectral decay in the frequency domain, and their dependence on kinetics and surface properties. The results shed light on frictional signatures of surface texture during active touch, and may inform understanding of the role played by friction in texture discrimination.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems