Gaussian Process Kernel Selection for Performance Prediction Based on Physiological Data
The adoption of Human-Robot (HR) teaming continues to increase within many high-risk fields (e.g., space exploration, medical treatment). This work solves an important problem in HR teaming by learning to interpret Mental Workload (MW) from human passive biosignals in the context of human performance. Using a previously designed experiment, we analyze GP kernels for human performance estimation. GP models help limit designer bias and provide prediction confidence intervals. This analysis offers the following contributions to this field: a heuristic to help understand which biosignals are informative, an evaluation of data, and a comparison of GP kernels. The experiment showed that smooth kernels yield lower performance prediction Root Mean Squared Error (RMSE) and Standard Error of the Mean (SEM) for each performance metric considered. As a result of this work, performance prediction model designers will have a guide for improving HR systems, passive MW monitoring, and performance estimation of HR teaming.