Understanding the constraints of finger motor control

Wrist Posture Does Not Influence Finger Interdependence

A task involving an instructed finger movement causes involuntary movements in the noninstructed fingers of the hand, also known as finger interdependence. It is associated with both mechanical and neural mechanisms. The current experiment investigated the effect of finger interdependence due to systematic changes of the wrist posture, close to neutral. Eight right-handed healthy human participants performed submaximal cyclic flexion and extension at the metacarpophalangeal joint at 0° neutral, 30° extension, and 30° flexion wrist postures, respectively. The experiment comprised of an instruction to move one of the 4 fingers-index, middle, ring, and little. Movements of the instructed and noninstructed fingers were recorded. Finger interdependence was quantified using enslavement matrix, individuation index, and stationarity index, and it was compared across wrist postures. The authors found that the finger interdependence does not change with changes in wrist posture. Further analysis showed that individuation and stationarity indices were mostly equivalent across wrist postures, and their effects were much smaller than the average differences present among the fingers. The authors conclude that at wrist postures close to neutral, the finger interdependence is not affected by wrist posture.

Synergistic Myoelectrical Activities of Forearm Muscles Improving Robust Recognition of Multi-Fingered Gestures

Currently, surface electromyography (sEMG) features of the forearm multi-tendon muscles are widely used in gesture recognition, however, there are few investigations on the inherent physiological mechanism of muscle synergies. We aimed to study whether the muscle synergies could be used for gesture recognition. Five healthy participants executed five gestures of daily life (pinch, fist, open hand, grip, and extension) and the sEMG activity was acquired from six forearm muscles. A non-negative matrix factorization (NMF) algorithm was employed to decompose the pre-treated six-channel sEMG data to obtain the muscle synergy matrixes, in which the weights of each muscle channel determined the feature set for hand gesture classification. The results showed that the synergistic features of forearm muscles could be successfully clustered in the feature space, which enabled hand gestures to be recognized with high efficiency. By augmenting the number of participants, the mean recognition rate remained at more than 96% and reflected high robustness. We showed that muscle synergies can be well applied to gesture recognition.