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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yuen, Shelten G. Rudoy, Daniel Howe, Robert D. Wolfe, Patrick J. |
| Copyright Year | 2007 |
| Description | Author affiliation: School of Engineering and Applied Sciences, Department of Statistics, Harvard University, Harvard-MIT Division of Health Sciences & Technology, Oxford Street, Cambridge, MA 02138 USA. sgyuen@seas.harvard.edu (Yuen, Shelten G.) || School of Engineering and Applied Sciences, Department of Statistics, Harvard University, Harvard-MIT Division of Health Sciences & Technology, Oxford Street, Cambridge, MA 02138 USA. patrick@seas.harvard.edu (Wolfe, Patrick J.) || School of Engineering and Applied Sciences, Department of Statistics, Harvard University, Harvard-MIT Division of Health Sciences & Technology, Oxford Street, Cambridge, MA 02138 USA. rudoy@seas.harvard.edu (Rudoy, Daniel) || School of Engineering and Applied Sciences, Department of Statistics, Harvard University, Harvard-MIT Division of Health Sciences & Technology, Oxford Street, Cambridge, MA 02138 USA. howe@seas.harvard.edu (Howe, Robert D.) |
| 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. |
| Starting Page | 104 |
| Ending Page | 108 |
| File Size | 313583 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424411979 |
| DOI | 10.1109/SSP.2007.4301227 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-08-26 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Bayesian methods Force measurement Biological materials Uncertainty Atomic force microscopy Position measurement Material properties Mechanical factors Displacement measurement Polynomials soft tissue mechanics changepoint detection switching regressions Bayesian estimation material indentation |
| Content Type | Text |
| Resource Type | Article |
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