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Detailed dynamical systems' models used in the life sciences may includehundreds of state variables and many input parameters, often with physicalmeaning. Therefore, efficient and unique input parameter identification, fromexperimental data, is an essential but challenging task for this class ofmodel. To clarify our understating of the process (which within a clinicalcontext amounts to a personalisation), we utilise the computational methods ofUnscented Kalman filtration (UKF), sensitivity and orthogonality analysis. Wehave applied these three techniques to a test-bench model of a singleventricle, coupled, via Ohmic valves, to a Compliance-Resistor-Compliance (CRC)Windkessel electrical analogue model of the systemic circulation, chosen inview of its relative simplicity, interpretability and prior art. Utilising anefficient, novel and real-time implementation of the UKF (Code available athttps://github.com/H-Sax/CMSB-2023), we show how, counter-intuitively, inputparameters are efficiently recovered from experimental data \emph{even if theyare not sensitive parameters in the currently accepted sense}. This result (i)exposes potential limitations in the standard interpretation of what it meansfor an input parameter to be designated identifiable and (ii) suggests that theconcepts of sensitivity and identifiability may have a weaker relationship thancommonly thought - at least in the presence of an appropriate data set. Werationalise these observations.  Practically, we present results which show the UKF to be an efficient methodfor assigning personalised input parameters from experimental data inreal-time, which enhances the clinical significance of our approach.

New Perspectives on Sensitivity and Identifiability Analysis using the Unscented Kalman Filter