Bayesian Changepoint Detection Through Switching Regressions: Contact Point Determination in Material Indentation Experiments

Abstract

Material indentation is a popular method for determining the mechanical properties of biomaterials. The basic premise of an indentation experiment is to physically displace the sample using an indenter that measures resistive force, in order to formulate a force-displacement curve. However, doing so requires estimating the initial contact event between the indenter and the sample-a statistical changepoint detection problem that has not been rigorously addressed in the biomaterials literature to date. Here we adopt a hierarchical Bayesian approach to contact point determination based on switching regressions, which generalizes an algorithm popular with practitioners and enables both hyperparameter estimation as well as uncertainty quantification. Results using several experimentally obtained silicone indentation data sets indicate that our approach outperforms existing techniques.