Optimal Sampling of Recruitment Curves for Functional Electrical Stimulation Control

A major challenge in controlling multiple-input multiple output functional electrical stimulation systems is the large amount of time required to identify a workable system model due to the high dimensionality of the space of inputs. To address this challenge we are exploring optimal methods to sample the input space. In this paper we present two methods for optimally sampling isometric muscle force recruitment curves. One method maximizes the information about the recruitment curve parameters, and the second method minimizes the average variance of the predicted output force. We compared these methods to two previously-used methods in simulation. The simulation model was identified from recruitment data collected during experiments with a human subject with a high spinal cord injury. The optimal sampling methods on average produced estimates of the output force with less error than the two previously-used methods. The optimal sampling methods require fewer system identification experiments to identify models with similar output prediction accuracy.