Adaptive Experimental Design Applied to an Ergonomics Testing Procedure

Nonlinear constrained optimization algorithms are widely utilized in artifact design. Certain algorithms also lend themselves well to design of experiments (DOE). Adaptive design refers to experimental design where determining where to sample next is influenced by information from previous experiments. We present a constrained optimization algorithm known as superEGO (a variant of the EGO algorithm of Schonlau, Welch and Jones) that is able to create adaptive designs effectively. Its ability to allow easily for a variety of sampling criteria and to incorporate constraint information accurately makes it well suited to the needs of adaptive design. The approach is demonstrated on a human reach experiment where the selection of sampling points adapts successfully to the stature and perception of the individual test subject. Results from the initial study indicate that superEGO is able to create experimental designs that yield more accurate models using fewer points than the original testing procedure. NOMENCLATURE DOE Design of Experiments ISC Infill Sampling Criterion MLE Maximum Likelihood Estimation didate, Department of Mechanical Engineering -Candidate, Department of Biomedical Engineering Professor, Department of Civil and Environmental Engineering , Department of Mechanical Engineering Research Scientist, UofM Transportation Research Institute 1 SCF Spatial Correlation Function ŷ(x) Kriging estimate of y at x ŝ2(x) Kriging variance of y at x R(w,x) Spatial Correlation between w and x R sample to sample correlation matrix rx Sample to prediction point correlation vector σ2 Process variance β̂ Global trend of the kriging model θ Kriging model parameter to be fit p Kriging model parameter to be fit nugget Kriging model parameter to be fit (controls smoothing) d Number of input variable dimensions n Number of sample points gi Constraint function