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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
Published on: April 21, 2023
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A soft-contact and wrench based approach to study grasp planning and execution
Tarkeshwar Singh1, Satyajit Ambike2
1Department of Kinesiology, The Pennsylvania State University, University Park, PA 16802, United States.
Journal of Biomechanics
|October 18, 2015
Summary
Researchers developed new tools to analyze human grasping in 3D. New indices quantify grasp stability, aiding studies on how we control our hands for stable, precise manipulation.
Area of Science:
- Neuroscience
- Robotics
- Biomechanics
Background:
- Robotic grasping has advanced significantly, yet human prehension remains complex due to intricate hand anatomy and neural control.
- Most human prehension studies use simplified planar models, limiting the scope of inquiry and hindering 3D analysis.
- A lack of analytical tools for quantifying spatial prehension impedes research into human grasp stability.
Purpose of the Study:
- To adapt a soft-contact wrench model for analyzing spatial human prehension.
- To introduce quantitative indices for evaluating grasp stability in three dimensions.
- To provide a framework for investigating sensory-motor control and perception in grasp planning.
Main Methods:
- Theoretical adaptation of a soft-contact wrench model for spatial prehension.
- Step-by-step implementation of the adapted model.
- Introduction of two novel indices: grasp caliber and grasp intensity.
Main Results:
- Grasp caliber quantifies proximity to instability by analyzing the grasp matrix's smallest singular value.
- Grasp intensity measures the force applied by digits to counteract external perturbations.
- The proposed indices offer a quantitative method to assess spatial grasp stability.
Conclusions:
- The developed model and indices provide essential tools for analyzing spatial human prehension.
- These quantitative measures can advance our understanding of sensory-motor coupling and perceptual roles in grasp planning.
- This work bridges the gap between robotic grasping research and the complexities of human hand control.
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