Feasibility of EMG-based neural network controller for an upper extremity neuroprosthesis.

The overarching goal of this project is to provide shoulder and elbow function to individuals with C5/C6 spinal cord injury (SCI) using functional electrical stimulation (FES), increasing the functional outcomes currently provided by a hand neuroprosthesis. The specific goal of this study was to design a controller based on an artificial neural network (ANN) that extracts information from the activity of muscles that remain under voluntary control sufficient to predict appropriate stimulation levels for several paralyzed muscles in the upper extremity. The ANN was trained with activation data obtained from simulations using a musculoskeletal model of the arm that was modified to reflect C5 SCI and FES capabilities. Several arm movements were recorded from able-bodied subjects and these kinematics served as the inputs to inverse dynamic simulations that predicted muscle activation patterns corresponding to the movements recorded. A system identification procedure was used to identify an optimal reduced set of voluntary input muscles from the larger set that are typically under voluntary control in C5 SCI. These voluntary activations were used as the inputs to the ANN and muscles that are typically paralyzed in C5 SCI were the outputs to be predicted. The neural network controller was able to predict the needed FES paralyzed muscle activations from "voluntary" activations with less than a 3.6% RMS prediction error.

Musculoskeletal model-guided, customizable selection of shoulder and elbow muscles for a C5 SCI neuroprosthesis.

Individuals with C5/C6 spinal cord injury (SCI) have a number of paralyzed muscles in their upper extremities that can be electrically activated in a coordinated manner to restore function. The selection of a practical subset of paralyzed muscles for stimulation depends on the specific condition of the individual, the functions targeted for restoration, and surgical considerations. This paper presents a musculoskeletal model-based approach for optimizing the muscle set used for functional electrical stimulation (FES) of the shoulder and elbow in this population. Experimentally recorded kinematics from able-bodied subjects served as inputs to a musculoskeletal model of the shoulder and elbow, which was modified to reflect the reduced muscle force capacities of an individual with C5 SCI but also the potential of using FES to activate paralyzed muscles. A large number of inverse dynamic simulations mimicking typical activities of daily living were performed that included 1) muscles with retained voluntary control and 2) many different combinations of stimulated paralyzed muscles. These results indicate that a muscle set consisting of the serratus anterior, infraspinatus and triceps would enable the greatest range of relevant movements. This set will become the initial target in a C5SCI neuroprosthesis to restore shoulder and elbow function.

EMG-based control for a C5/C6 spinal cord injury upper extremity neuroprosthesis.

The goal of this project is to enhance the benefits of functional electrical stimulation (FES) for individuals with cervical mid-level spinal cord injury (C5-C6 SCI) by providing upper arm function that complements the hand function provided by current FES systems. As a result of stimulation to selected shoulder and elbow muscles, individuals are able to increase their range of motion, their reaching ability, and improve their overall shoulder stability. An approach that provides a natural way of controlling arm stimulation is proposed. The controller extracts information from recorded EMG activity of muscles under retained voluntary control and processes these signals to generate the appropriate stimulation levels for the stimulated paralyzed muscles. One subject with complete C5 paralysis has been implemented with this advanced neuroprosthesis which includes four implanted EMG electrodes and 24 channels of stimulation. Eight of these channels were used for hand grasp and six were used for trunk stimulation to provide posture control, trunk stability and weight relief. The shoulder and elbow implanted stimulation channels include the suprascapular, thoracodorsal and radial nerves (via nerve-cuff electrodes) and the pectoralis major, rhomboids and pronator quadratus muscles (via muscle-based electrodes). The thoracic portion of the pectoralis major was transferred to the scapula to restore the actions of the denervated serratus anterior muscle, essential for reaching tasks and shoulder stability. The four EMG channels implanted include the trapezius, biceps, deltoids and extensor carpi radialis longus. Currently, the EMG control strategy is being refined and tested with the subject including evaluation of the functional benefits of the intervention.

Adaptive neural network controller for an upper extremity neuroprosthesis.

The long term goal of this project is to develop an adaptive neural network controller for an upper extremity neuroprosthesis targeted for people with C5/C6 spinal cord injury (SCI). The challenge is to determine how to simultaneously stimulate different paralyzed muscles based on the EMG activity of muscles under retained voluntary control. The controller extracts the movement intention from the recorded EMG signals and generates the appropriate stimulation levels to activate the paralyzed muscles. To test the feasibility of this controller, different arm movements were recorded from able bodied subjects. Using a musculoskeletal model of the arm, inverse simulations provided muscle activation patterns corresponding to these movements. The model was modified to reflect C5/C6 SCI and the optimization criteria were varied to reflect different nervous system motor control strategies. Activation patterns were then used to train a time-delayed neural network to predict paralyzed muscle activations from voluntary muscle activations. Forward simulations were performed to obtain predicted movements and use the kinematic errors to design an adaptive strategy to account for disturbances and changes in the system.