From Tactile Signals to Grasp Classification: Exploring Patterns with Machine Learning

Abstract

Grasping, a fundamental aspect of human motor skills, involves the intricate coordination of sensory and motor systems to manipulate objects effectively. Accurate grasp classification, crucial for advancing human-machine interaction in robotics and prosthetics, involves categorizing various hand-grasping patterns. This study explores a grasp classification method employing tactile sensors measuring fingertip grasp forces and machine learning algorithms. The study evaluates the performance of Decision Tree, Random Forest (RF), k-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Neural Network classifiers in identifying and categorizing eight grasp types. The study involves the participation of 10 subjects, utilizing capacitive-based Finger TPS force sensors. The RF classifier achieves the highest accuracy at 90%, outperforming others with accuracies ranging from 71% to 87%. The results demonstrate the efficacy of data gloves and machine learning classifiers in efficient and accurate grasp identification, offering valuable insights for controlling grasping force and planning robotic motions.