An active area of research in adaptive structures focuses on the use of continuous wing shape changing methods as a means of replacing conventional discrete control surfaces and increasing aerodynamic efficiency. Although many shape-changing methods have been used since the beginning of heavier-than-air flight, the concept of performing camber actuation on a fully-deformable airfoil has not been widely applied. A fundamental problem of applying this concept to real-world scenarios is the fact that camber actuation is a continuous, time-dependent process. Therefore, if camber actuation is to be used in a closed-loop feedback system, one must be able to determine the instantaneous airfoil shape, as well as the aerodynamic loads, in real time. One approach is to utilize a new type of artificial hair sensors (AHS) developed at the Air Force Research Laboratory (AFRL) to determine the flow conditions surrounding deformable airfoils. In this study, AHS measurement data will be simulated by using the flow solver XFoil, with the assumption that perfect data with no noise can be collected from the AHS measurements. Such measurements will then be used in an artificial neural network (ANN) based process to approximate the instantaneous airfoil camber shape, lift coefficient, and moment coefficient at a given angle of attack. Additionally, an aerodynamic formulation based on the finite-state inflow theory has been developed to calculate the aerodynamic loads on thin airfoils with arbitrary camber deformations. Various aerodynamic properties approximated from the AHS/ANN system will be compared with the results of the finite-state inflow aerodynamic formulation in order to validate the approximation approach.