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Grasp force estimation from the transient EMG using high-density surface recordings.

Itzel Jared Rodriguez Martinez1, Andrea Mannini, Francesco Clemente

  • 1Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy. The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy. Author to whom any correspondence should be addressed.

Journal of Neural Engineering
|January 4, 2020
PubMed
Summary
This summary is machine-generated.

Researchers decoded grasp force from early muscle signals, achieving high accuracy using transient electromyogram (EMG) data. This breakthrough enables faster, more intuitive prosthetic control by utilizing the initial muscle activity patterns.

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Area of Science:

  • Applied neuroscience and bioengineering
  • Neuroprosthetics
  • Motor control

Background:

  • Decoding neurophysiological signals for prosthesis control is a key challenge.
  • Electromyogram (EMG) signals from forearm muscles typically control prosthesis functions.
  • Early muscle contraction (transient phase) EMG patterns predict grasp types, but grasp force estimation from this phase was unexplored.

Purpose of the Study:

  • To investigate the potential of using transient electromyogram (EMG) signals for estimating grasp force (GF).
  • To determine the optimal time window for GF estimation from EMG onset.
  • To compare GF estimation accuracy using transient versus steady-state EMG data.

Main Methods:

  • Recorded high-density EMG signals (192 channels) from 12 participants during a pick-and-lift task.
  • Estimated final GF using linear regressors with selected EMG channels and features.
  • Analyzed different data window lengths from EMG onset and evaluated performance using absolute error and R-squared metrics.

Main Results:

  • Prediction accuracy for GF improved with longer data windows, plateauing at steady state.
  • GF estimation using 16 EMG channels achieved 2.52% absolute error from transient data alone.
  • GF estimation using the first 500ms post-onset data yielded 1.99% absolute error.

Conclusions:

  • Transient EMG signal phase contains significant information for estimating final grasp force.
  • GF estimation from transient EMG is comparable to steady-state data.
  • This finding supports the development of rapid, online myoelectric controllers that decode grasp strength from early EMG signals.