ABSTRACT
In myo-control, for computational and setup constraints, the measurement of a high number of muscles is not always possible: the choice of the muscle set to use in a myo-control strategy depends on the desired application scope and a search for a reduced muscle set, tailored to the application, has never been performed. The identification of such set would involve finding the minimum set of muscles whose difference in terms of intention detection performance is not statistically significant when compared to the original set. Also, given the intrinsic sensitivity of muscle synergies to variations of EMG signals matrix, the reduced set should not alter synergies that come from the initial input, since they provide physiological information on motor coordination. The advantages of such reduced set, in a rehabilitation context, would be the reduction of the inputs processing time, the reduction of the setup bulk and a higher sensitivity to synergy changes after training, which can eventually lead to modifications of the ongoing therapy. In this work, the existence of a minimum muscle set, called optimal set, for an upper-limb myoelectric application, that preserves performance of motor activity prediction and the physiological meaning of synergies, has been investigated. Analyzing isometric contractions during planar reaching tasks, two types of optimal muscle sets were examined: a subject-specific one and a global one. The former relies on the subject-specific movement strategy, the latter is composed by the most recurrent muscles among subjects specific optimal sets and shared by all the subjects. Results confirmed that the muscle set can be reduced to achieve comparable hand force estimation performances. Moreover, two types of muscle synergies namely "Pose-Shared" (extracted from a single multi-arm-poses dataset) and "Pose-Related" (clustering pose-specific synergies), extracted from the global optimal muscle set, have shown a significant similarity with full-set related ones meaning a high consistency of the motor primitives. Pearson correlation coefficients assessed the similarity of each synergy. The discovering of dominant muscles by means of the optimization of both muscle set size and force estimation error may reveal a clue on the link between synergistic patterns and the force task.
ABSTRACT
The current knowledge about muscle synergies does not clearly explain how both rehabilitation and brain plasticity act on the way they evolve after a cortical stroke. In this preliminary study, the authors analyzed the correlation between healthy and affected muscle synergies and the way the latter change after rehabilitation, following the clinical scales scores changes. The aim was finding whether the patients were supposed to get the unimpaired synergies back or develop new synergies due to neural changes. Eleven chronic stroke survivors performed 20 rehabilitation sessions with robotic hand, obtaining different scores in the assessment sessions. Results revealed no significant correlations between changes on clinical assessment scales and the difference in similarities with healthy synergies, between post and pre-assessment ones, suggesting that the stroke recovering process involves the growth of new synergies, different from the ones of healthy subjects. Those new synergies could better facilitate motor hand and elbow functions.
