Human tactile sensing and sensorimotor mechanism: from afferent tactile signals to efferent motor control

Abstract

Abstract Despite the recent advances in tactile sensing by low threshold mechanoreceptors, our understanding of human sensorimotor mechanisms, from the afferent tactile input to the efferent motor output controlling forearm muscles and hand manipulations, is still at a basic level. This is largely because of the difficulties in capturing population-level mechano-afferent nerve signals during active touch. The decoding of this sophisticated dynamic relationship as the applicable control algorithm for restoring human-like sensorimotor performance on prosthetics or robotics is a long-term scientific challenge. We use a novel method of integrating the finite element hand and neural dynamic model optimized against microneurography data to predict the group neural response of cutaneous neurons during active touch based on contact biomechanics and membrane transduction dynamics. The neural activation level of the muscle synergy during in-vivo experiments was evaluated using the predicted afferent neural responses. It was firstly found that the dynamic relationship between the afferent tactile signals and neural activation level of forearm muscles could be effectively simplified as transduction functions. The accuracy and applicability of the decoded transduction mechanism were validated by restoring the human-like sensorimotor performance on a tendon-driven biomimetic hand, making a further step toward the application of next-generation prosthetics with neuromorphic tactile feedback. From the transduction functions, it was deduced that human subjects may apply a similar sensorimotor strategy to grasp different objects actively or reactively, and the response time of this closed-loop control can be affected by the size and weight of the object.