Neuro-Musculoskeletal Mapping for Man-Machine Interfacing

Tamas Kapelner, Massimo Sartori, Francesco Negro, Dario Farina*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

39 Citations (Scopus)
67 Downloads (Pure)


We propose a myoelectric control method based on neural data regression and musculoskeletal modeling. This paradigm uses the timings of motor neuron discharges decoded by high-density surface electromyogram (HD-EMG) decomposition to estimate muscle excitations. The muscle excitations are then mapped into the kinematics of the wrist joint using forward dynamics. The offline tracking performance of the proposed method was superior to that of state-of-the-art myoelectric regression methods based on artificial neural networks in two amputees and in four out of six intact-bodied subjects. In addition to joint kinematics, the proposed data-driven model-based approach also estimated several biomechanical variables in a full feed-forward manner that could potentially be useful in supporting the rehabilitation and training process. These results indicate that using a full forward dynamics musculoskeletal model directly driven by motor neuron activity is a promising approach in rehabilitation and prosthetics to model the series of transformations from muscle excitation to resulting joint function.

Original languageEnglish
Article number5834
JournalScientific reports
Issue number1
Publication statusPublished - 2 Apr 2020


Dive into the research topics of 'Neuro-Musculoskeletal Mapping for Man-Machine Interfacing'. Together they form a unique fingerprint.

Cite this