A comprehensive analysis of task-specific hand kinematic, muscle and force synergies

Abstract

Synergies were demonstrated to exist in the kinematic, force and muscular domains, and their task-specificity and subject-specificity was also highlighted in literature. Despite that, no works have extracted synergies on specific grasp classes to analyze task-specific synergistic patterns. Moreover, only few studies focused on the combined analysis of kinematic, force and muscle synergies.

The aim of this work was to (i) identify the grasp classes on which to extract task-specific synergies; (ii) extract subject-specific and task-specific synergies in the three domains and (iii) calculate the similarity of the extracted synergies among subjects and define average generalized synergies.

8 subjects were recruited to perform 21 reach-to-grasp tasks and the kinematics, contact forces and muscular activation of the hand were acquired. A LDA classifier allowed distinguishing power and precision grasp classes with an average accuracy of 89% considering kinematic data alone and combined kinematic, muscle and force data. Subject and task-specific synergies were therefore extracted on these two classes. Kinematic and force synergies were distinctive for the two classes, and highly similar among subjects, thus suggesting the possibility of adopting generalized synergies to describe grasp strategies. Conversely, muscle synergies did not differ particularly for the two classes. The combined analysis of force and kinematic data suggested that the hand posture may be somehow modulated by the optimal distribution of contact forces to perform stable grasps. Simulations with a virtual hand confirmed that stability significantly increased when grasps were generated by activating combined kinematic and force synergies rather than kinematic synergies only.