14 research outputs found

    Human Hand as a Parallel Manipulator

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    A Universal Volumetric Haptic Actuation Platform

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    In this paper, we report a method of implementing a universal volumetric haptic actuation platform which can be adapted to fit a wide variety of visual displays with flat surfaces. This platform aims to enable the simulation of the 3D features of input interfaces. This goal is achieved using four readily available stepper motors in a diagonal cross configuration with which we can quickly change the position of a surface in a manner that can render these volumetric features. In our research, we use a Microsoft Surface Go tablet placed on the haptic enhancement actuation platform to replicate the exploratory features of virtual keyboard keycaps displayed on the touchscreen. We ask seven participants to explore the surface of a virtual keypad comprised of 12 keycaps. As a second task, random key positions are announced one at a time, which the participant is expected to locate. These experiments are used to understand how and with what fidelity the volumetric feedback could improve performance (detection time, track length, and error rate) of detecting the specific keycaps location with haptic feedback and in the absence of visual feedback. Participants complete the tasks with great success (p < 0.05). In addition, their ability to feel convex keycaps is confirmed within the subjective comments.Peer reviewe

    Différents paramètres physiques exercés par le singe durant l'exploration tactile

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    Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

    Skin friction: a novel approach to measuring in vivo human skin

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    The human skin plays an important role in people’s lives. It is in constant\ud interaction with the environment, clothing and consumer products.\ud This thesis discusses one of the parameters in the interaction between\ud the human skin in vivo and other materials: skin friction. The thesis is\ud divided into three parts. The first part is an introduction to skin friction\ud and to current knowledge on skin friction. The second part presents the\ud RevoltST, the tribometer that was specially developed for skin friction\ud research and which meets the objectives described in the thesis. The third\ud part presents the results of the skin friction measurements obtained with\ud the RevoltST

    Designing a comprehensive system for analysis of handwriting biomechanics in relation to neuromotor control of handwriting

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    A comprehensive system for investigation of biomechanical and neuromuscular processes involved with producing handwriting and drawing was developed. The system included a pen-like grip measuring device that enabled the variations of finger grip force associated with writing and drawing to be measured while holding the pen in tripod grip. The pen was integrated with a digitiser tablet for recording x,ycoordinates and pressure of the nib and a motion analysis system for recording the limb and hand kinematics. It was observed that for line drawing in the y-direction of the tablet, finger forces were directly related to pen tip movement and finger forces were modulated in a repeatable and predictable fashion, while this was not the case for line drawing in the x-direction. This was evidence for longstanding assumptions. Wrist rotation was required for production of lines in the x-direction without excessive deviation. For writing tasks, it was observed that no two tasks performed by one subject share an identical writing process, not even when the writing results are (nearly) identical. The neuromuscular control apparatus is highly flexible and works in a coordinated fashion that allows production of nearly equal end-results by means of different mechanical and therefore neuromuscular processes. For spiral drawing, tremor that originates from the fingers, hand and arm was quantified with the transducer pen. Limb joint kinematics were displayed in three dimensions with colour coding of coordinate sample numbers. This method can reveal the origin of some forms of limb tremor. Pen grip force patterns during signature writing were found to be characteristic for subjects, which relate to their individual pen-hand interaction, resulting from fine control of distal joints. Variation between trials of the same subject was observed, revealing adaptations of the computational processes during writing. The potential for signature verification by means of finger force recording was explored.A comprehensive system for investigation of biomechanical and neuromuscular processes involved with producing handwriting and drawing was developed. The system included a pen-like grip measuring device that enabled the variations of finger grip force associated with writing and drawing to be measured while holding the pen in tripod grip. The pen was integrated with a digitiser tablet for recording x,ycoordinates and pressure of the nib and a motion analysis system for recording the limb and hand kinematics. It was observed that for line drawing in the y-direction of the tablet, finger forces were directly related to pen tip movement and finger forces were modulated in a repeatable and predictable fashion, while this was not the case for line drawing in the x-direction. This was evidence for longstanding assumptions. Wrist rotation was required for production of lines in the x-direction without excessive deviation. For writing tasks, it was observed that no two tasks performed by one subject share an identical writing process, not even when the writing results are (nearly) identical. The neuromuscular control apparatus is highly flexible and works in a coordinated fashion that allows production of nearly equal end-results by means of different mechanical and therefore neuromuscular processes. For spiral drawing, tremor that originates from the fingers, hand and arm was quantified with the transducer pen. Limb joint kinematics were displayed in three dimensions with colour coding of coordinate sample numbers. This method can reveal the origin of some forms of limb tremor. Pen grip force patterns during signature writing were found to be characteristic for subjects, which relate to their individual pen-hand interaction, resulting from fine control of distal joints. Variation between trials of the same subject was observed, revealing adaptations of the computational processes during writing. The potential for signature verification by means of finger force recording was explored

    Modeling of frictional forces during bare-finger interactions with solid surfaces

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    Touching an object with our fingers yields frictional forces that allow us to perceive and explore its texture, shape, and other features, facilitating grasping and manipulation. While the relevance of dynamic frictional forces to sensory and motor function in the hand is well established, the way that they reflect the shape, features, and composition of touched objects is poorly understood. Haptic displays -electronic interfaces for stimulating the sense of touch- often aim to elicit the perceptual experience of touching real surfaces by delivering forces to the fingers that mimic those felt when touching real surfaces. However, the design and applications of such displays have been limited by the lack of knowledge about what forces are felt during real touch interactions. This represents a major gap in current knowledge about tactile function and haptic engineering. This dissertation addresses some aspects that would assist in their understanding. The goal of this research was to measure, characterize, and model frictional forces produced by a bare finger sliding over surfaces of multiple shapes. The major contributions of this work are (1) the design and development of a sensing system for capturing fingertip motion and forces during tactile exploration of real surfaces; (2) measurement and characterization of contact forces and the deformation of finger tissues during sliding over relief surfaces; (3) the development of a low order model of frictional force production based on surface specifications; (4) the analysis and modeling of contact geometry, interfacial mechanics, and their effects in frictional force production during tactile exploration of relief surfaces. This research aims to guide the design of algorithms for the haptic rendering of surface textures and shape. Such algorithms can be used to enhance human-machine interfaces, such as touch-screen displays, by (1) enabling users to feel surface characteristics also presented visually; (2) facilitating interaction with these devices; and (3) reducing the need for visual input to interact with them.Ph.D., Electrical Engineering -- Drexel University, 201

    Encodage des forces tactiles dans le cortex somatosensoriel primaire

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    Les deux fonctions principales de la main sont la manipulation d’objet et l’exploration tactile. La détection du glissement, rapportée par les mécanorécepteurs de la peau glabre, est essentielle pour l’exécution de ces deux fonctions. Durant la manipulation d’objet, la détection rapide du micro-glissement (incipient slip) amène la main à augmenter la force de pince pour éviter que l’objet ne tombe. À l’opposé, le glissement est un aspect essentiel à l’exploration tactile puisqu’il favorise une plus grande acuité tactile. Pour ces deux actions, les forces normale et tangentielle exercées sur la peau permettent de décrire le glissement mais également ce qui arrive juste avant qu’il y ait glissement. Toutefois, on ignore comment ces forces contrôlées par le sujet pourraient être encodées au niveau cortical. C’est pourquoi nous avons enregistré l’activité unitaire des neurones du cortex somatosensoriel primaire (S1) durant l’exécution de deux tâches haptiques chez les primates. Dans la première tâche, deux singes devaient saisir une pastille de métal fixe et y exercer des forces de cisaillement sans glissement dans une de quatre directions orthogonales. Des 144 neurones enregistrés, 111 (77%) étaient modulés à la direction de la force de cisaillement. L’ensemble de ces vecteurs préférés s’étendait dans toutes les directions avec un arc variant de 50° à 170°. Plus de 21 de ces neurones (19%) étaient également modulés à l’intensité de la force de cisaillement. Bien que 66 neurones (59%) montraient clairement une réponse à adaptation lente et 45 autres (41%) une réponse à adaptation rapide, cette classification ne semblait pas expliquer la modulation à l’intensité et à la direction de la force de cisaillement. Ces résultats montrent que les neurones de S1 encodent simultanément la direction et l’intensité des forces même en l’absence de glissement. Dans la seconde tâche, deux singes ont parcouru différentes surfaces avec le bout des doigts à la recherche d’une cible tactile, sans feedback visuel. Durant l’exploration, les singes, comme les humains, contrôlaient les forces et la vitesse de leurs doigts dans une plage de valeurs réduite. Les surfaces à haut coefficient de friction offraient une plus grande résistance tangentielle à la peau et amenaient les singes à alléger la force de contact, normale à la peau. Par conséquent, la somme scalaire des composantes normale et tangentielle demeurait constante entre les surfaces. Ces observations démontrent que les singes contrôlent les forces normale et tangentielle qu’ils appliquent durant l’exploration tactile. Celles-ci sont également ajustées selon les propriétés de surfaces telles que la texture et la friction. Des 230 neurones enregistrés durant la tâche d’exploration tactile, 96 (42%) ont montré une fréquence de décharge instantanée reliée aux forces exercées par les doigts sur la surface. De ces neurones, 52 (54%) étaient modulés avec la force normale ou la force tangentielle bien que l’autre composante orthogonale avait peu ou pas d’influence sur la fréquence de décharge. Une autre sous-population de 44 (46%) neurones répondait au ratio entre la force normale et la force tangentielle indépendamment de l’intensité. Plus précisément, 29 (30%) neurones augmentaient et 15 (16%) autres diminuaient leur fréquence de décharge en relation avec ce ratio. Par ailleurs, environ la moitié de tous les neurones (112) étaient significativement modulés à la direction de la force tangentielle. De ces neurones, 59 (53%) répondaient à la fois à la direction et à l’intensité des forces. L’exploration de trois ou quatre différentes surfaces a permis d’évaluer l’impact du coefficient de friction sur la modulation de 102 neurones de S1. En fait, 17 (17%) neurones ont montré une augmentation de leur fréquence de décharge avec l’augmentation du coefficient de friction alors que 8 (8%) autres ont montré le comportement inverse. Par contre, 37 (36%) neurones présentaient une décharge maximale sur une surface en particulier, sans relation linéaire avec le coefficient de friction des surfaces. La classification d’adaptation rapide ou lente des neurones de S1 n’a pu être mise en relation avec la modulation aux forces et à la friction. Ces résultats montrent que la fréquence de décharge des neurones de S1 encode l’intensité des forces normale et tangentielle, le ratio entre les deux composantes et la direction du mouvement. Ces résultats montrent que le comportement d’une importante sous-population des neurones de S1 est déterminé par les forces normale et tangentielle sur la peau. La modulation aux forces présentée ici fait le pont entre les travaux évaluant les propriétés de surfaces telles que la rugosité et les études touchant à la manipulation d’objets. Ce système de référence s’applique en présence ou en absence de glissement entre la peau et la surface. Nos résultats quant à la modulation des neurones à adaptation rapide ou lente nous amènent à suggérer que cette classification découle de la manière que la peau est stimulée. Nous discuterons aussi de la possibilité que l’activité des neurones de S1 puisse inclure une composante motrice durant ces tâches sensorimotrices. Finalement, un nouveau cadre de référence tridimensionnel sera proposé pour décrire et rassembler, dans un même continuum, les différentes modulations aux forces normale et tangentielle observées dans S1 durant l’exploration tactile.The two most important functions of the hand are object manipulation and tactile exploration. The detection of slip provided by specialized mechanoreceptors in the glabrous skin is essential for the execution of both these functions. During object manipulation, the early detection of incipient slip leads to a grip force increase in order to prevent dropping an object. Slip is also an important aspect of tactile exploration because it greatly increases the acuity of touch perception. In both actions, normal and tangential forces on the skin can describe slip itself but also what occurs just before slip. However, little is known about how these self-generated forces are encoded at the cortical level. To better understand this encoding, we recorded from single neurons in primary somatosensory cortex (S1) as monkeys executed two haptic tasks. In the first task, two monkeys grasped a stationary metal tab with a key grip and exerted shear forces, without slip, in one of four orthogonal directions. Of 144 recorded neurons, 111 (77%) had activity modulated with shear force directions. These preferred shear force vectors were distributed in every direction with tuning arcs varying from 50° to 170°. Also, more than 21 (19%) of these neurons had a firing rate correlated with shear force magnitude. Even if 66 (59%) modulated neurons showed clear slowly adapting response and 45 (41%) other neurons a rapidly adapting response, this classification failed to explain the modulation to force direction and magnitude. These results show that S1 neurons encode force direction and magnitude simultaneously even in the absence of slip. In the second task, two monkeys scanned different surfaces with the fingertips in search of a tactile target without visual feedback. During the exploration, the monkeys, like humans, carefully controlled the finger forces and speeds. High friction surfaces offered greater tangential shear force resistance to the skin that was associated with decrease of the normal contact forces. Furthermore, the scalar sum of the normal and tangential forces remained constant. These observations demonstrate that monkeys control the applied normal and tangential finger forces within a narrow range which is adjusted according to surface properties such as texture and friction. Of the 230 recorded neurons during tactile exploration, 96 (42%) showed instantaneous frequency changes in relation to finger forces. Of these, 52 (54%) were correlated with either the normal or tangential force magnitude with little or no influence from the other orthogonal force component. Another subset of 44 neurons (46%) responded to the ratio between normal and tangential forces regardless of magnitude. Namely, 29 neurons (30%) increased and 15 (16%) others decreased their discharge frequency related to this ratio, which corresponds to the coefficient of friction. Tangential force direction significantly modulated about half the recorded neurons (112). Of these, 59 (53%) responded to both direction and force magnitude. Of the 102 neurons recorded during exploration of three or more surfaces, 17 (17%) showed increased firing rate with increased surface friction and 8 (8%) presented the opposite behavior. However, 37 (36%) neurons seemed to discharge optimally for one of the surfaces without any linear relation to the surfaces’ coefficient of friction. The classification of rapidly and slowly adaptation for neuronal responses in S1 could not be associated with the modulation to forces or direction. These results show that the firing rates of S1 neurons reflect the tangential and normal force magnitude, the ratio of the two forces and the direction of finger movement. These results show that the activity of a significant subpopulation of S1 neurons is represented by normal and tangential forces on the skin. This force modulation uses a frame of reference that can be applied with or without slip. This aspect provides a link between investigations of the cortical representation of surface properties and studies on object manipulation. Our results regarding the distinction between rapidly and slowly adapting neurons leads us to suggest that this difference is a consequence of the manner in which the skin was stimulated. A potential motor component in the modulation of S1 neurons during these sensorimotor tasks is also discussed. Finally, a novel three-dimensional reference frame is proposed to describe, as a single continuum, the different modulations to forces observed in S1 during tactile exploration
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