The tracking and prediction of hand and finger movements along with gesture recognition is an active subject of research due to its vast applications in many fields, such as prosthetic control and robotic telemanipulation of rehabilitation and assistive devices. The common challenge in all of these fields is developing a control interface for operating an automated assistive device in a more precise and human-like manner. In this paper, we aim to study the possibility of using a force-sensing resistors (FSRs) strap for the continuous prediction of finger movements, specifically those of the thumb and middle and index fingers. An array of 8 FSRs is used to construct a wearable band that wraps around the forearm. Ten healthy volunteers participated in this study. The subjects were asked to perform 3 representative grasping motions. The relative displacements of the tips of the thumb and middle and index fingers with respect to a reference marker placed on the hand (slightly above the wrist) was captured using a camera and colored marker system. An epsilon-based function of support vector regression with a radial basis function kernel is employed to train a regression model using the force myography (FMG) signal acquired from the forearm with the displacement of each fingertip. The results demonstrate the feasibility of using an FSR band for continuously predicting the displacement of the fingertips with an average squared correlation coefficient of 0.96 for the thumb and middle and index fingers, spanning three types of grasp.