Exploring synergistic patterns in bimanual distal limb movements through low dimensional representations.

The human hand is a complex manipulator with many joints that can perform various tasks. Neuroscience research has demonstrated that to perform any posture, the brain does not control the individual joints but relies on coactivation patterns called synergies that simultaneously control a set of joints. A combination of these synergies can then be used to reconstruct a variety of postures. While such a hypothesis has been demonstrated for single-handed tasks, a question that is not well-explored is whether such synergies can simultaneously control the joints of both hands during bimanual tasks. This paper attempted to address this question by exploring synergies obtained by performing Principal Component Analysis (PCA) on the kinematic data recorded from both the dominant and non-dominant hands of the participants as they performed bimanual tasks. The ability of synergies to reconstruct postures from a lower-dimensional subspace was presented, and an analysis of the separability of postures was performed using a classification algorithm. The results showed that the first 3 synergies explained greater than 80% variance in data, indicating that a few bimanual synergies can be utilized to control the fingers of both hands. The first three synergies could reconstruct postures with a Root Mean Square Error (RMSE) of 4° and classify tasks with an accuracy of 90%, demonstrating that the task-related information was retained in the lower dimensional subspace. This could significantly reduce control complexities while designing robotic or prosthetic distal upper limb devices.