Predicting functional force production capabilities of upper extremity functional electrical stimulation neuroprostheses: a proof of concept study

摘要

Objective. This study’s goal was to demonstrate person-specific predictions of the force productioncapabilities 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’sworkspace, if FES activated muscles can produce enough force to hold the arm against gravity andother passive forces, the amount of force the arm can potentially exert on external objects, and inwhich directions FES can move the arm. Approach. We computed force production predictionsfor a person with high tetraplegia and an FES neuroprosthesis used to activate muscles in hershoulder and arm. We developed Gaussian process regression models of the force produced at theend of the forearm when stimulating individual muscles at different wrist positions in the person’sworkspace. For any given wrist position, we predicted all possible forces a person can produceby any combination of individual muscles. Based on the force predictions, we determined if FEScould produce force sufficient to overcome passive forces to hold a wrist position, the maximumforce FES could produce in all directions, and the set of directions in which FES could move thearm. To estimate the error in our predictions, we then compared our force predictions based onsingle-muscle models to the actual forces produced when stimulating combinations of the person’smuscles. Main results. Our models classified the person’s ability to hold static arm positions correctlyfor 83% (Session #1) and 69% (Session #2) for 39 wrist positions over two sessions. We predictedthis person’s ability to produce force at the end of her arm with an RMS error of 5.5 N and thepercent of directions for which FES could achieve motion with RMS error of 10%. The accuracyof these predictions is similar to that found in the literature for FES systems with fewer degrees offreedom and fewer muscles. Significance. These person and device-specific predictions of functionalcapabilities of the arm allow neuroprosthesis developers to set achievable functional objectives forthe systems they develop. These predictions can potentially serve as a screening tool for cliniciansto use in planning neuroprosthetic interventions, greatly reducing the risk and uncertainty in suchinterventions.