Filament Sliding Linear Potentiometer-Based Data Glove (FLiPo) for Precisely Annotating Human Finger Poses

Abstract

Data gloves offer excellent portability and a strong ability to handle occluded movements, making them more advantageous over other methods for capturing complex hand motions in unstructured environments. However, the majority of existing hand-motion-capture gloves do not preserve visual features of the hand, which critically hinders their applicability for automatic pose annotation in RGB images. Here, we propose a data glove based on filament-sliding linear potentiometers (FLiPo), which can maintain finger appearance and ensure high accuracy as well as robustness, paving the way for automatic annotation. In FLiPo, fine filaments (Φ.1 mm) are deployed on finger skin to transmit joint arc length variations as well as preserve the hand's visual features, while linear potentiometers used to capture filament length changes are positioned on the arm. Simultaneously, a quantitative occlusion scoring metric is proposed to evaluate the degree of finger occlusion caused by the device. Further, we experimentally analyze the nonlinearities induced by biaxial joint coupling and skin tissue artifact (STA)-related hysteresis, and employ a fully connected neural network to map arc length to joint angles with an MAE of joint angles of 2.15°. Meanwhile, tests under challenging environmental conditions, including heat, moisture, and magnetic interference, are conducted to evaluate its stability. Finally, the system's capability for real-time pose capture with high accuracy, robustness, and low occlusion was demonstrated.