In an effort to better understand the human head-neck target tracking response, we have developed a procedure for designing a robustly optimal experimental configuration for system identification. This configuration is comprised of a parametrized input sequence along with physical parameters for the experiment. We have developed both nominal and experimental models containing uncertainties for the target tracking task based on several preliminary experimental data sets, and identified a feasible population of subject controller parameters. We applied a min-max optimization scheme to minimize a performance cost over the feasible experimental configurations, while simultaneously maximizing it over the population of subject controller parameters. In this way, a minimum level of design performance for any subject within the defined population can be guaranteed. We show that in the worst-case, the performance cost is 0.473 in flexion/extension, and 0.122 in axial rotation.

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