Opposition Space and Human Prehension

Iberall, Thea; MacKenzie, Christine L.

doi:10.1007/978-1-4613-8974-3_2

Cited by 40 publications

(18 citation statements)

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“…Instead, with the B-grip a reliable grasping must be achieved by a precise differential control in the two hands of the synergies (i.e., sets of muscles recruited by a single neural command signal) responsible for the transport. It has been speculated (Arbib et al 1985;Iberall and MacKenzie 1990) that the control of multifingered grasp is simplified by lumping the real digits in just two virtual digits acting in simpler opposition space. By analogy, it could be suggested that a precise grasp force control is achieved by lumping the three-element biomechanical chain [left arm]-[manipulandum]-[right arm] into a single virtual endpoint effector.…”

Section: Unimanual Versus Bimanual Gripmentioning

confidence: 99%

Grip forces during fast point-to-point and continuous hand movements

Viviani

Lacquaniti

2015

Exp Brain Res

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Three experiments investigated the grip force exerted by the fingers on an object displaced actively in the near-body space. In one condition (unimanual) the object was held by one hand with the tripod grip and was moved briskly back and forth along one of the three coordinate directions (up-down, left-right, near-far). In the second condition (bimanual) the same point-to-point movements were performed while holding the object with the index and middle fingers of both hands. In the third condition (bimanual) the object was held as in the second condition and moved along a circular path lying in one of the three coordinate planes (horizontal, frontal, sagittal). In all conditions participants were asked to exert a baseline level of grip force largely exceeding the safety margin against slippage. Both grip forces and hand displacements were measured with high accuracy. As reported in previous studies, in the two point-to-point conditions we observed an upsurge of the grip force at the onset and at the end the movements. However, the timing of the transient increases of the grip force relative to hand kinematics did not confirm the hypothesis set forth by several previous studies that grip modulation is a pre-planned action based on an internal model of the expected effects of the movement. In the third condition, the systematic modulation of the grip force also for circular movements was again at variance with the internal model hypothesis because it cannot be construed as a pre-planned action aiming at countering large changes in dynamic load. We argue that a parsimonious account of the covariations of load and grip forces can be offered by taking into account the visco-elastic properties of the neuromuscular system.

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Section: Unimanual Versus Bimanual Gripmentioning

confidence: 99%

Grip forces during fast point-to-point and continuous hand movements

Viviani

Lacquaniti

2015

Exp Brain Res

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“…However, a multitude of factors contribute to a grasp which are not considered in these classifications. Iberall and MacKenzie offer an abstract model of grasping [12,17]. They describe grasping as a black box with a goal and an object as inputs and a specific grasp as output.…”

Section: Meaning In a Graspmentioning

confidence: 99%

Grasp sensing for human-computer interaction

Wimmer¹

2010

Proceedings of the Fifth International Conference on Tangible, Embedded, and Embodied Interaction

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The way we grasp an object depends on several factors, e.g. the intended goal or the hand's anatomy. Therefore, a grasp can convey meaningful information about its context. Inferring these factors from a grasp allows us to enhance interaction with grasp-sensitive objects. This paper highlights an grasp as an important source of meaningful context for human-computer interaction and gives an overview of prior work from other disciplines. This paper offers a basis and framework for further research and discussion by proposing a descriptive model of meaning in grasps The GRASP model combines five factors that determine how an object is grasped: goal, relationship between user and object, anatomy, setting, and properties of the object. The model is validated both from an epistemological perspective and by applying it to scenarios from related work.

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“…Bones and joints of the human hand (taken from [23] Types of opposition (taken from [23]). Table 3 Best Cohesive Indices for Precision Grasps on flat circular objects .............17 Table 4 Best Cohesive Indices for Precision Grasps on flat elliptical objects ...........18 Table 5 Description …”

Section: List Of Figuresmentioning

confidence: 99%

“…Cutkosky and Howe [19], in a similar vein, relate grasp attributes such as dexterity and precision to analytic measures such as manipulability and isotropy in their grasp analysis. Iberall and MacKenzie [23] concentrate on finding a grasp solution for the controller given anticipated object properties and predictable interaction outcome in terms of opposition space and virtual fingers. Iberall [24] describes a neural network that maps qualitative task and object properties (surface length in terms of finger span, object width, amount of forces, and level of task precision) onto a desired prehensile posture.…”

Section: Comparisons With Other Grasp Frameworkmentioning

confidence: 99%

A Framework for Recognizing Grasps

Kang¹,

Ikeuchi²

1991

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Opposition Space and Human Prehension

Cited by 40 publications

References 0 publications

Grip forces during fast point-to-point and continuous hand movements

Grip forces during fast point-to-point and continuous hand movements

Grasp sensing for human-computer interaction

A Framework for Recognizing Grasps

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