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This paper investigates the effect of two different parameters on the registration and force prediction accuracy of using Active Appearance Models (AAM) to align fingernail images. First, the color channel used to form the AAM is varied between (1) an averaged grayscale image, (2) the red channel, (3) the green channel and (4) the blue channel. Second, the number of landmark points used to create the AAM is varied between 6 and 75.The color channel is found to have an effect on the registration accuracy and the force prediction error. The green, blue and grayscale images are approximately equivalent, while the red images have a larger error across all metrics used. The number of landmark points may be reduced to 25 with no significant effect on either the registration accuracy or the force prediction error, though further reduction has shown some effect. With this information, a simpler registration model can be used that requires fewer calculations.

This paper demonstrates fast, accurate, and stable force control in three axes simultaneously when a flat surface is pressed against the human fingerpad. The primary application of this force control is for the automated calibration of a fingernail imaging system, where video images of the human fingernail are used to predict the normal and shear forces that occur when the fingerpad is pressed against a flat surface.The system consists of a six degree-of-freedom magnetic levitation device (MLD), whose flotor has been modified to apply forces to the human fingerpad, which is resting in a passive restraint. The system is capable of taking simultaneous steps in normal force and two axes of shear forces with a settling time of less than 0.2 seconds, and achieves a steady-state error as small as 0.05 N in all three axes. The system is also capable of tracking error of less than 0.2 N when the shear force vector rotates with a frequency of 1 rad/s. This paper also demonstrates the successful tracking of a desired force trajectory in three dimensions for calibrating a fingernail imaging system.

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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.