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When the human fingertip is pressed against a surface or bent, the hemodynamic state of the fingertip is altered due to mechanical interactions between the fingernail and bone. Normal force, shear force, and finger extension/flexion all result in different patterns of blood volume beneath the fingernail. This phenomenon has been exploited in order to detect finger forces and finger posture by creating a photoplethysmograph "fingernail sensor," which measures the two-dimensional pattern of blood volume beneath the fingernail. In this paper, a filter is designed to predict the normal force, lateral shear force, longitudinal shear force, and bending angle based on readings from the fingernail sensor. Linear, polynomial, and neural network models relating the bending angle and touch forces to optical sensor outputs are developed and tested. A method is developed to uniformly calibrate the predictor for each user. Calibration experiments are performed to train and validate the predictor for seven human subjects. Results show that on average, shear forces can be predicted with 0.5 N root mean square (rms) error, normal force with 1 N rms error, and posture angle with 10 degrees rms error. Applications and methods for improving performance are discussed.