Muscle Synergies as the Basis for the Control of a Hand Prothesis

In this ongoing study, we are exploring the suitability of a novel technique based on the estimation of muscle synergies to control a hand prosthesis. The objective of the study is to predict thirteen distinct hand grasp movements using EMG data.

The method relies on projecting the EMG data using a basis derived via analysis of the muscle synergies.

Five healthy subjects performed a set of thirteen distinct hand grasp movements while sEMG data from seven muscles have been recorded.

The non-negative matrix factorization algorithm was applied to the sEMG envelopes to derive four muscle synergies, which we found to be sufficient to reconstruct the muscle activation patterns with an average R² - across all sEMG channels -of 0.85 and a minimum R² of 0.70. These four muscle synergies were used as a projection basis and the output of the algorithm was analyzed using a template matching technique to detect the performance of the hand grasp movements.

The average (across grasps) classifier accuracy varied from 87.4 (subject #1) to 94.9% (subject #3).