Current force sensors used to capture fingertip interaction forces lack compliance to the fingertip tissue resulting in the loss of touch sensation of the user. 3D printing offers the possibility to create personalized soft sensing structures. This work evaluates a 3D printed soft sensor that measures normal and shear interactions forces based on the deformations of the thumb and index fingertips of 7 subjects using an instrumented object. Due to the use of (carbon doped) thermoplastic materials, the signals provided by these sensing structures suffer from nonlinearities. Therefore, two compensation models, based on a neural network and recur- rent neural network analogous to an electrical model are used to compensate for the nonlinear effects. The performance of the sensors was analysed using the normalized cross-correlation and the root-mean-square error. The output of the force sensors are highly correlated with the applied shear and normal force components. When paired with compensation models the correlation and error of the sensor output can be further improved. These results indicate that the proposed flexible fingertip interaction force sensors have a high potential for future applications.