Empowering Movement: A Human-in-the-Loop Proportional Controller for Assisted Elbow Flexion in Brachial Plexus Injury

Although powered orthotic devices are becoming more available and increasingly recognized by the medical community, several factors still prevent them from being widely adopted. These orthoses typically use a simple controller, often termed a "bang-bang" controller, which actuates a motor if the muscle's electromyography (EMG) signal is above a set threshold. A proportional controller that could actuate the elbow mechanism based on the EMG signal magnitude would enable a better human-machine connection. The real challenge for designing a proportional controller lies in the heterogeneity of the types of injury and surgeries used in this patient population, as well as the extent of recovery post-surgery. Neural networks effectively model nonlinear relationships in control system design. Recently, a novel powered myoelectric elbow orthosis (PMEO) was developed and tested on patients with a brachial plexus injury (BPI) that used a bang-bang controller. The report evaluated the possibility of using a proportional controller using a neural network-powered regression (NNR) for this exoskeleton.

Data from 31 participants with a BPI were collected after receiving IRB approval. A custom apparatus was designed to measure elbow flexion torque and EMG, while participants were instructed to match their torque to a predefined target (10%-40% of MVC). A 2-layered NNR (2 hidden nodes) with a Tanh activation and 5 cross-validation folds was used to find a relationship between the properties of the EMG (moving average, root mean square, variance, standard deviation, and slope) and the elbow torque for individual participants. The collected data were divided into a training set (70%) and a test set (30%). The root mean squared error (RMSE) for the test set was calculated for each NNR model, along with the training time and prediction speed.

A standard computer (Intel Core i5-10500 with 16 GB RAM) was used to train the models. The trained models required 82 seconds (range: 3-332 seconds) to train on 8.2e4 (range: 7.7e4-8.9e4) samples. The test dataset consisted of 3.5e4 (range: 3.3e4-3.8e4) samples. The median test RMSE was 0.39 Nm (range: 0.1-3.09 Nm). The NNR took 7 milliseconds (range: 6-10 milliseconds) to predict the test dataset; 35% and 16% of the models had an R2 > 0.5 and >0.7, respectively.

A low-powered office computer was sufficient to train an NNR with training time short enough to be completed in an orthotist's office within a typical appointment time. These data verify that the process can be field deployable.