Functional electrical stimulation (FES) is a promising solution for restoring functional motion to individuals with paralysis, but the potential for achieving any desired full-arm reaching motion has not been realized. We present a combined feedforward-feedback controller capable of automatically calculating and applying the necessary muscle stimulations to hold the wrist of an individual with high tetraplegia in a desired static position. We used the controller to hold a complete arm configuration to maintain a series of static wrist positions. The average distance to the target wrist position, or accuracy, was 2.9 cm. The precision is defined as the radius of the 95% confidence ellipsoid for the final positions of a set of trials with the same muscle stimulations and starting position. The average precision was 3.7 cm. The control architecture used in this study to hold static positions has the potential to control arbitrary reaching motions.

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Objective. This study’s goal was to demonstrate person-specific predictions of the force production capabilities of a paralyzed arm when actuated with a functional electrical stimulation (FES) neuroprosthesis. These predictions allow us to determine, for each hand position in a person’s workspace, if FES activated muscles can produce enough force to hold the arm against gravity and other passive forces, the amount of force the arm can potentially exert on external objects, and in which directions FES can move the arm. Approach. We computed force production predictions for a person with high tetraplegia and an FES neuroprosthesis used to activate muscles in her shoulder and arm. We developed Gaussian process regression models of the force produced at the end of the forearm when stimulating individual muscles at different wrist positions in the person’s workspace. For any given wrist position, we predicted all possible forces a person can produce by any combination of individual muscles. Based on the force predictions, we determined if FES could produce force sufficient to overcome passive forces to hold a wrist position, the maximum force FES could produce in all directions, and the set of directions in which FES could move the arm. To estimate the error in our predictions, we then compared our force predictions based on single-muscle models to the actual forces produced when stimulating combinations of the person’s muscles. Main results. Our models classified the person’s ability to hold static arm positions correctly for 83% (Session #1) and 69% (Session #2) for 39 wrist positions over two sessions. We predicted this person’s ability to produce force at the end of her arm with an RMS error of 5.5 N and the percent of directions for which FES could achieve motion with RMS error of 10%. The accuracy of these predictions is similar to that found in the literature for FES systems with fewer degrees of freedom and fewer muscles. Significance. These person and device-specific predictions of functional capabilities of the arm allow neuroprosthesis developers to set achievable functional objectives for the systems they develop. These predictions can potentially serve as a screening tool for clinicians to use in planning neuroprosthetic interventions, greatly reducing the risk and uncertainty in such interventions.

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