Neurorobotic fusion of prosthetic touch, kinesthesia, and movement in bionic upper limbs promotes intrinsic brain behaviors.

Bionic prostheses have restorative potential. However, the complex interplay between intuitive motor control, proprioception, and touch that represents the hallmark of human upper limb function has not been revealed. Here, we show that the neurorobotic fusion of touch, grip kinesthesia, and intuitive motor control promotes levels of behavioral performance that are stratified toward able-bodied function and away from standard-of-care prosthetic users. This was achieved through targeted motor and sensory reinnervation, a closed-loop neural-machine interface, coupled to a noninvasive robotic architecture. Adding touch to motor control improves the ability to reach intended target grasp forces, find target durometers among distractors, and promote prosthetic ownership. Touch, kinesthesia, and motor control restore balanced decision strategies when identifying target durometers and intrinsic visuomotor behaviors that reduce the need to watch the prosthetic hand during object interactions, which frees the eyes to look ahead to the next planned action. The combination of these three modalities also enhances error correction performance. We applied our unified theoretical, functional, and clinical analyses, enabling us to define the relative contributions of the sensory and motor modalities operating simultaneously in this neural-machine interface. This multiperspective framework provides the necessary evidence to show that bionic prostheses attain more human-like function with effective sensory-motor restoration.

The effects of control signal noise on simultaneous submovements.

Understanding the stereotypical characteristics of human movement can better inform rehabilitation practices by providing a template of healthy and expected human motor control. Multiplicative noise is inherent in goal-directed movement, such as reaching to grasp an object. Multiplicative noise plays an important role in computational motor control models to help support phenomena such as stereotypical kinematic profiles in time-constrained and unconstrained tasks. Most tasks are not carried out along an isolated degree-of-freedom (DOF), and modelling the contribution of noise can be difficult. Here we add a noise term proportional to the degree of simultaneity for multi-DOF tasks to approximate the contribution of system noise. With this approach, we are able to explain previously observed motor phenomena including the presence of submovements in multi-DOF tasks, and the transition from simultaneous to sequential control of joints without the presence of feedback. Inclusion of a simultaneous multiplicative noise term presents a simple theory that expands on previous research in order to describe characteristics of multiple-DOF movements. This model can be used as a guide to compare healthy human motor control to the movements of patients receiving rehabilitation in an effort to improve their motor planning.