Two-Stage Optimized Perturbation Design for Efficient Human Arm Impedance Identification With Device Dynamics Compensation

System identification of human sensorimotor systems requires multiple experimental trials to achieve reliable parameter estimates, yet practical constraints limit the total number of trials possible. While pseudorandom sequence (PRS) perturbations are widely used due to their white noise-like properties, and optimal multisines can theoretically provide better performance when prior system knowledge is available, their implementation on mechanical devices presents significant challenges. Device dynamics can degrade the designed spectral properties of both perturbation types, increasing the number of required trials to achieve desired estimation precision. This paper presents a foundational framework for device-dynamicsaware perturbation design that reduces the necessary number of experimental trials. The framework introduces two key components: a prefilter for PRS to minimize digital-to-analog conversion effects, and a modified cost function for multisine optimization that explicitly compensates for mechanical device dynamics. We propose a two-stage approach where the prefiltered PRS first provides initial estimates that inform subsequent optimal multisine design. Through human arm impedance experiments and devicerendered validation, we demonstrate that our framework achieves much smaller covariance resulting in fewer trials to achieve satisfactory identification performance compared to conventional methods. The optimal multisine stage, enhanced by device dynamics compensation, shows particular effectiveness in reducing parameter covariance. The covariance improvement translates to multiple practical benefits: a potential 62.5% reduction in required trial numbers when full-length signals are used, a 75% reduction in single-trial duration while maintaining estimation quality, or various combinations of these improvements depending on experimental constraints. These results establish a practical path toward more efficient human system identification protocols that minimize experimental burden while maintaining estimation accuracy.