Although soft robotic assistive gloves have high potential for restoring functional independence for individuals with motor impairment, their lack of rigid components makes it difficult to obtain accurate position sensing to validate their performance. To track soft device motion, standard practices rely on costly optical motion capture techniques, which have reduced accuracy due to limitations in marker occlusion and device deformation.