Optimizing Fingernail Imaging Calibration for 3D Force Magnitude Prediction.

Abstract

This paper discusses the optimization of a fingernail imaging system for predicting fingerpad force. The effects of lighting coloration, calibration grid, and force prediction model on the registration process and force prediction accuracy of fingernail imaging are investigated. White and green LEDs are found to produce statistically similar effects on registration error and force prediction results across all three directions of force. Two calibration grids are implemented, with no statistically significant difference in either registration or force prediction between the Cartesian and cylindrical grid designs. Of the five force prediction models investigated, a principal component regression model based on the pixel intensity eigenvectors estimates the force with the greatest accuracy. This EigenNail Magnitude Model simultaneously estimates force in all three directions with RMS error with 95 percent confidence interval of 0.55 ± 0.02 N (7.6 percent of the full force range). These results indicate a set of optimal parameter choices for the calibration of a fingernail imaging system.