Surface Feature Recognition and Grasped Object Slip Prevention With a Liquid Metal Tactile Sensor for a Prosthetic Hand

Abstract

There is a need to gather rich, real-time tactile information to enhance prosthetic hand performance during object manipulation. To that end, a highly stretchable liquid metal tactile sensor was designed, manufactured, and integrated into the fingertip of an i-limb Ultra prosthetic hand. With this novel tactile sensor, the feasibility of real-time slip detection and prevention of a grasped object was demonstrated. Furthermore, this liquid metal tactile sensor was used to distinguish between five different surface patterns with high accuracy using three different classification algorithms: K Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF). The K-nearest neighbors (KNN) classifier produced the highest classification accuracy of 98% to distinguish between five different surface textures. Taken together, this novel prosthetic fingertip tactile sensor has the potential to improve grasp control and object manipulation operations for upper limb amputees. Additionally, this paper documents the first time that a liquid metal tactile sensor has been used to distinguish between different surface features, to the best knowledge of the authors.