A neural network model for coordination of hand gesture during reach to grasp

Molina-Vilaplana, Javier; Coronado, Juan López

doi:10.1016/j.neunet.2005.07.014

Cited by 37 publications

(12 citation statements)

References 65 publications

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“…The human hand is versatile and dexterous in its interaction with the environment in that it allows for both powerful and stable grasps (Cutkosky 1989; Vilaplana and Coronado 2006; Prattichizzo and Trinkle 2008) and fine dexterous manipulation by the digits (Zatsiorsky and Latash 2009). When a person holds an object steadily with all five digits using a prismatic grasp (the thumb opposing the four fingers), the action has been discussed as organized in a hierarchical way (Arbib et al 1985; Baud-Bovy and Soechting 2001; Shim et al 2005; Gorniak et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Adaptations to fatigue of a single digit violate the principle of superposition in a multi-finger static prehension task

2013

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We investigated the effects of exercise-induced fatigue of a digit on the biomechanics of a static prehension task. The participants were divided into two groups. One group performed the fatiguing exercise using the thumb (group-thumb) and the second group performed the exercise using the index finger (group-index). We analyzed the prehensile action as being based on a two-level hierarchy. Our first hypothesis was that fatigue of the thumb would have stronger effects at the upper level (action shared between the thumb and all four fingers combined – virtual finger) and fatigue of the index finger would have stronger effects at the lower level of the hierarchy (action of the virtual finger shared among actual fingers). We also hypothesized that fatigue would cause a decrease in the normal force applied by the exercised digit and correspondingly lead to a decrease in the normal force applied by the opposing digit(s). Our third hypothesis was that fatigue would leave the tangential forces unaffected. Fatigue led to a significant drop in the normal force of both exercised and non-exercised (opposing) digits. The tangential forces of the exercised digits increased after fatigue. This led to a drop in the safety margin in the group-thumb, but not group-index. As such, the results supported the first two hypotheses but not the third hypothesis. Overall, the results suggested that fatigue triggered a chain reaction that involved both forces and moments of force produced by individual digits leading to a violation of the principle of superposition. The findings are interpreted within the framework of the referent configuration hypothesis.

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Section: Introductionmentioning

confidence: 99%

Adaptations to fatigue of a single digit violate the principle of superposition in a multi-finger static prehension task

2013

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“…However, DIRECT, like many other solutions, was validated in simulation using perfect knowledge of end-effector position and stick-model limbs. Other applications of DIRECT have been reported (Vilaplana and Coronado, 2006 ; Grosse-Wentrup and Contreras-Vidal, 2007 ; Bouganis and Shanahan, 2010 ), but these too were only performed in simulation with perfect positional knowledge and lack of physical constraints beyond joint rotation boundaries. Our implementation is the first instance we are aware of that demonstrates the efficacy of DIRECT using actual computer vision to determine end-effector and target localization.…”

Section: Discussionmentioning

confidence: 99%

A neural network-based exploratory learning and motor planning system for co-robots

2015

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Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or “learning by doing,” an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object.

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“…Schlicht and Schrater ( 2007 ) suggested, based on the principal components analysis, that only one principal component, which can be represented by the aperture size, would explain the difference in the trajectory of the fingers during reach-to-grasp movements where the target is placed in different visual eccentricities. Therefore, we assume that modeling grip aperture between thumb and index finger, and not the kinematics of each finger, is sufficient for revealing the grip control mechanism influenced by online vision (see also Vilaplana and Coronado, 2006 ).…”

Section: Methodsmentioning

confidence: 99%

A Computational Model for Aperture Control in Reach-to-Grasp Movement Based on Predictive Variability

Takemura

Fukui

Inui

2015

Front. Comput. Neurosci.

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In human reach-to-grasp movement, visual occlusion of a target object leads to a larger peak grip aperture compared to conditions where online vision is available. However, no previous computational and neural network models for reach-to-grasp movement explain the mechanism of this effect. We simulated the effect of online vision on the reach-to-grasp movement by proposing a computational control model based on the hypothesis that the grip aperture is controlled to compensate for both motor variability and sensory uncertainty. In this model, the aperture is formed to achieve a target aperture size that is sufficiently large to accommodate the actual target; it also includes a margin to ensure proper grasping despite sensory and motor variability. To this end, the model considers: (i) the variability of the grip aperture, which is predicted by the Kalman filter, and (ii) the uncertainty of the object size, which is affected by visual noise. Using this model, we simulated experiments in which the effect of the duration of visual occlusion was investigated. The simulation replicated the experimental result wherein the peak grip aperture increased when the target object was occluded, especially in the early phase of the movement. Both predicted motor variability and sensory uncertainty play important roles in the online visuomotor process responsible for grip aperture control.

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A neural network model for coordination of hand gesture during reach to grasp

Cited by 37 publications

References 65 publications

Adaptations to fatigue of a single digit violate the principle of superposition in a multi-finger static prehension task

Adaptations to fatigue of a single digit violate the principle of superposition in a multi-finger static prehension task

A neural network-based exploratory learning and motor planning system for co-robots

A Computational Model for Aperture Control in Reach-to-Grasp Movement Based on Predictive Variability

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