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| Content Provider | The American Society of Mechanical Engineers (ASME) Digital Collection |
|---|---|
| Author | Eniko, T. Enikov Polyvás, Péter P. |
| Copyright Year | 2013 |
| Abstract | This article describes a novel method of indirect estimation of intra-ocular pressure using tactile sensors. Two sensor calibration methods have been demonstrated: an artificial neural network (ANN) model and a phenomenological reduced-parameter model based on finite element analysis. The ANN method showed superior performance with an accuracy of +/− 0.7 mmHg, while the reduced order method showed an accuracy of +/− 3.11 mmHg. The latter method however allows calibration of the tactile tonometer from a single pressure measurement if the geometry of the probes is known and satisfying certain solvability conditions. The ANN method was demonstrated using experiment data, while the reduced-order model was tested numerically. Due to its indirect and non-invasive nature, the proposed tactile measurement method can be used in the development of a self-administered home tonometer for management of glaucoma, however the presence of an eye lid might require modification of the calibration procedure outlined here. |
| File Format | |
| ISBN | 9780791856215 |
| DOI | 10.1115/IMECE2013-63380 |
| Volume Number | Volume 3A: Biomedical and Biotechnology Engineering |
| Conference Proceedings | ASME 2013 International Mechanical Engineering Congress and Exposition |
| Language | English |
| Publisher Date | 2013-11-15 |
| Publisher Place | San Diego, California, USA |
| Access Restriction | Subscribed |
| Subject Keyword | Geometry Tactile sensors Artificial neural networks Calibration Finite element analysis Pressure measurement Sensors Pressure Probes |
| Content Type | Text |
| Resource Type | Article |
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