A Robust Data-Driven Soft Sensory Glove for Human Hand Motions Identification and Replication

Abstract

Biomechanical sensors are essential components for wearable robots because of their broad spectrum of industrial and medical applications. A wide range of these sensors uses soft materials with deformation-related electrical properties, namely variable resistors and capacitors. Previously, conductive materials were injected into silicone structures that require several molding steps to build the microchannels. This paper proposes a versatile soft sensing glove, using a simple process for preparing sensors of different sizes without molding. We used commercially available silicone tubes to host the conductive liquid. Ten sensors were attached to the back side of the glove to measure flexion-extension and four sensors were placed on the glove at the interdigital folds between the fingers to measure abduction-adduction. The sensory glove successfully replicates hand motion. We used machine learning algorithms to estimate the angles of the joints in the hand and also to identify 15 gestures. The system's robustness was evaluated in two experiments. The gesture prediction is robust to shocks from contact with a punching ball and also submersion of the glove in water. The proposed sensory glove overcomes challenges of comfort, rigidity, and robustness. It can be used, therefore, to replicate human hand motion in industrial and medical applications.