Predicting rare events of a non-linear process can prove to be a difficult challenge. In this paper, the linearization of a non-linear process, namely impact loading, was completed by performing a gaussianization of a sample non-linear, non-gaussian time series to predict the probability of human injury in a specified sea state. The gaussianized time series was then input into the Design Loads Generator (DLG) to estimate a lower bound to the non-gaussian extrema as well as providing an ensemble of extreme time series of the gaussianized process. The DLG takes an input spectrum, transfer function, and associated exposure time to optimize phase sets of a modified gaussian distribution such that the extreme realizations with return periods equal to the exposure time of the desired process, as well as the input that leads to those realizations, can be generated. In the present application, the input to the corresponding gaussian extreme time series was used as input in the non-linear, non-gaussian model to estimate the extremes of the non-linear process, conditioned on the gaussian process being extreme. The process of gaussianizing the non-linear time series, entering into the DLG, and using the resulting input time series as input into the non-linear model was iterated to further develop the conditional extreme pdf. The relationship between these conditional, developed extrema and the observed extrema of the process was applied to determine the probability of human injury. Here, injury is assumed to be related to two possibly correlated random variables: the magnitude and duration of an extreme acceleration event.