Background <p>The experimental sources, e.g. transducers, cameras, etc. involved in increasingly sophisticated experimental studies usually have dissimilar temporal resolutions. The data fusion process involves a synchronization step, either subsampling to the slowest resolution or supersampling to the fastest resolution. The former leads to data loss, while the latter can introduce significant interpolation errors in the case of transient behavior.</p> Objective <p>The goal is to develop a methodology to enhance the robustness of time-sensitive material behavior identification, exploiting the full richness of the experimental dataset.</p> Method <p>This paper discusses an identification framework based on a multi-phase interpolation method of the boundary conditions using Multi Degree B-splines and the asynchronous definition of cost functions of established inverse methods, namely Finite Element Model Updating and Integrated Digital Image Correlation. The approach is validated through the identification of viscoelastic constitutive parameters of a polymer 3D-printed specimen on both synthetic and real uniaxial and biaxial experiments.</p> Results <p>The proposed methodology enhances signal-to-noise ratios and time-dependent parameter identification quality without unnecessary data inflation.</p>