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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Han, S.J. Semmlow, J.L. Granger-Donnetti, B. Alvarez, T.L. |
| Copyright Year | 2009 |
| Description | Author affiliation: Rutgers University, Piscataway, NJ, USA (Semmlow, J.L.) || Essilor International, St.Maur, France (Granger-Donnetti, B.) || New Jersey Institute of Technology, Depart. of Biomedical Engineering Newark, USA (Han, S.J.; Alvarez, T.L.) |
| Abstract | Presbyopia is a universal vision problem caused by the aging process. Progressive addition lenses (PALs) are a preferred solution; however, some patients can not adapt to the lenses. The acceptability is not well understood. Sixteen presbyopic subjects (9 subjects adapted to progressive lenses (PL) and 7 subjects who could not adapt to progressive lenses (PD) ) participated in a motor learning study. The motor learning experiment recorded baseline and modification responses where the dynamics of the 4° step responses were compared. Results show that both baseline and modified convergence dynamics were significantly greater in PLs compared to PDs. An adaptive neural network (ANN) classification technique using the baseline and modification velocity parameters with a linear classifier resulted in 94% correct classification. Therefore, baseline 4° convergence dynamics and its modification rate may be used as indicators to predict progressive addition lens acceptability among presbyopes. |
| Starting Page | 1 |
| Ending Page | 2 |
| File Size | 165058 |
| Page Count | 2 |
| File Format | |
| ISBN | 9781424443628 |
| DOI | 10.1109/NEBC.2009.4967785 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-04-03 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Convergence Lenses Biomedical engineering Aging Adaptive systems Neural networks Artificial neural networks Vision defects Surgery Humans |
| Content Type | Text |
| Resource Type | Article |
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