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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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Multidigit force control during unconstrained grasping in response to object perturbations.

Abdeldjallil Naceri1, Alessandro Moscatelli2,3, Robert Haschke4

  • 1Neuroinformatics Group, Cluster of Excellence Cognitive Interaction Technology (CITEC), Bielefeld University, Bielefeld, Germany; abdeldjallil.naceri@uni-bielefeld.de.

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Human hand grasping utilizes motor synergies for stable grips. Studies show synchronous finger stiffening during perturbations, suggesting a default whole-hand control strategy by the nervous system.

Keywords:
controlforcegraspingmanipulationunconstrained

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Area of Science:

  • Neuroscience
  • Biomechanics
  • Robotics

Background:

  • The human hand's complex anatomy allows numerous grasp configurations, posing a motor redundancy problem.
  • Motor synergies offer a strategy to manage this redundancy by coordinating multiple muscles.
  • Understanding unconstrained grasping is crucial for both neuroscience and robotics.

Purpose of the Study:

  • To investigate unconstrained hand grasping using motor synergies.
  • To analyze finger force coordination during external perturbations.
  • To compare human grasping with robotic control strategies.

Main Methods:

  • Utilized an innovative sensorized object for unconstrained grasping tasks.
  • Recorded spatiotemporal grip forces across digits during object perturbation.
  • Compared human grasping data with a robotic hand controlled by global stiffness.

Main Results:

  • Fingers exhibited synchronous stiffening, with similar times to reach peak grip force during perturbations.
  • Robotic grasping with a global stiffness control algorithm produced force patterns similar to human grasping.
  • This suggests a default whole-hand synergistic control strategy in the human nervous system.

Conclusions:

  • The central nervous system employs a default whole-hand synergistic control for stable grasping.
  • This strategy remains consistent despite variations in digit involvement, object contact, and perturbation types.
  • Findings provide insights into neural control mechanisms for dexterous manipulation.