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Related Experiment Video

Updated: Aug 14, 2025

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles
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Physically interacting humans regulate muscle coactivation to improve visuo-haptic perception.

Hendrik Börner1, Gerolamo Carboni2, Xiaoxiao Cheng2

  • 1Electrical and Computer Engineering Department, Technical University of Munich, Munich, Germany.

Journal of Neurophysiology
|January 18, 2023
PubMed
Summary
This summary is machine-generated.

Humans adapt muscle coactivation for smooth movement with partners. Stiffness adjusts based on visual clarity and partner performance, optimizing movement guidance and effort.

Keywords:
computational modelelectromyographyhuman-human interactionmuscle coactivationvisuo-haptic perception

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Area of Science:

  • Neuroscience
  • Human-Computer Interaction
  • Robotics

Background:

  • Coordinating movements with a partner, like dancing or moving large objects, requires precise muscle control to sense and respond to each other's actions.
  • Previous models explained adaptation in dynamic environments by minimizing movement error, but this doesn't fully capture human interaction dynamics.
  • Understanding how the central nervous system regulates muscle activation during partnered tasks is crucial for developing intuitive human-robot collaborations.

Purpose of the Study:

  • To investigate how physically connected pairs adjust muscle coactivation when tracking a moving target.
  • To determine the influence of visual acuity and partner performance on muscle coactivation strategies.
  • To develop a computational model explaining adaptive sensing in partnered movements beyond simple error minimization.

Main Methods:

  • Observation of physically connected pairs performing a target-tracking task with varying visual feedback and partner performance.
  • Measurement of muscle coactivation levels during the partnered movement.
  • Development and testing of a novel computational model to simulate and explain the observed adaptive sensing properties.

Main Results:

  • Muscle coactivation increases (stiffens the arm) when a partner performs worse and decreases (relaxes) with blurry visual feedback.
  • A new computational model demonstrates that individuals optimally combine haptic and visual information, considering sensorimotor noise.
  • The partner with more accurate visual information naturally leads the movement, guided by a balance of effort minimization and information extraction.

Conclusions:

  • Human muscle activation adapts dynamically to optimize information extraction from partners and environmental cues during physical interaction.
  • This adaptive sensing strategy balances guiding motion, minimizing effort, and integrating sensory information (haptic and visual) optimally.
  • The findings provide insights into natural human coordination and inform the design of collaborative robots and assistive technologies.