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

Itzel Jared Rodriguez Martinez, Andrea Mannini, Francesco Clemente, Angelo Maria Sabatini, Christian Cipriani

Research output: Contribution to journalArticlepeer-review


Objective. Understanding the neurophysiological signals underlying voluntary motor control and decoding them for prosthesis control are among the major challenges in applied neuroscience and bioengineering. Usually, information from the electrical activity of residual forearm muscles (i.e. the electromyogram, EMG) is used to control different functions of a prosthesis. Noteworthy, forearm EMG patterns at the onset of a contraction (transient phase) have shown to contain predictive information about upcoming grasps. However, decoding this information for the estimation of grasp force (GF) was so far overlooked. Approach. High density-EMG signals (192 channels) were recorded from twelve participants performing a pick-and-lift task. The final GF was estimated offline using linear regressors, with four subsets of channels and ten features obtained using three channels-features selection methods. Two different evaluation metrics (absolute error and R 2), complemented with statistical analysis, were used to select the optimal configuration of the parameters. Different windows of data starting at the GF onset were compared to determine the time at which the GF can be ascertained from the EMG signals. Main results. The prediction accuracy improved by increasing the window length from the moment of the onset and kept improving until the steady state at which a plateau of performances was reached. With our methodology, estimations of the GF through 16 EMG channels reached an absolute error of 2.52% the maximum voluntary force using only transient information and 1.99% with the first 500 ms of data following the onset. Significance. The final GF estimation from transient EMG was comparable to the one obtained using steady state data, confirming our hypothesis that the transient phase contains information about the final GF. This result paves the way to fast online myoelectric controllers capable of decoding grasp strength from the very early portion of the EMG signal.

Original languageEnglish
Article number016052
JournalJournal of Neural Engineering
Issue number1
Publication statusPublished - May 1 2020


  • elastic nets
  • grasp force
  • hand prosthetics
  • HD-EMG
  • lasso
  • regularized linear regression
  • transient EMG

ASJC Scopus subject areas

  • Biomedical Engineering
  • Cellular and Molecular Neuroscience


Dive into the research topics of 'Grasp force estimation from the transient EMG using high-density surface recordings'. Together they form a unique fingerprint.

Cite this