Online Grasp Force Estimation from the Transient EMG

Itzel Jared Rodriguez Martinez, Andrea Mannini, Francesco Clemente, Christian Cipriani

Research output: Contribution to journalArticlepeer-review


Myoelectric upper limb prostheses are controlled using information from the electrical activity of residual muscles (i.e. the electromyogram, EMG). EMG patterns at the onset of a contraction (transient phase) have shown predictive information about upcoming grasps. However, decoding this information for the estimation of the grasp force was so far overlooked. In a previous offline study, we proved that the transient phase of the EMG indeed contains information about the grasp force and determined the best algorithm to extract this information. Here we translated those findings into an online platform to be tested with both non-amputees and amputees. The platform was tested during a pick and lift task (tri-digital grasp) with light objects (200 g-1 kg), for which fine control of the grasp force is more important. Results show that, during this task, it is possible to estimate the target grasp force with an absolute error of 2.06 (1.32) % and 2.04 (0.49) % the maximum voluntary force for non-amputee and amputees, respectively, using information from the transient phase of the EMG. This approach would allow for a biomimetic regulation of the grasp force of a prosthetic hand. Indeed, the users could contract their muscles only once before the grasp begins with no need to modulate the grasp force for the whole duration of the grasp, as required with continuous classifiers. These results pave the way to fast, intuitive and robust myoelectric controllers of limb prostheses.

Original languageEnglish
Article number9187693
Pages (from-to)2333-2341
Number of pages9
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number10
Publication statusPublished - Oct 2020


  • Grasp force control
  • hand prosthetics
  • myoelectric control
  • regularized linear regression
  • transient EMG

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering


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