Understanding Grasp Synergies During Reach-to-Grasp Using an Instrumented Data Glove

Abstract

Grasp synergies lead to the identification of underlying patterns to develop control strategies for five-fingered prosthetic hands or exoskeletons. Data gloves play a crucial role in the study of human grasping and could provide insights into grasp synergies. This article presents the design and implementation of a data glove that has been fabricated using 3-D-printing technology and enhanced with instrumentation. The glove utilizes flexible sensors for the fingers and force sensors integrated into the glove at the fingertips to accurately capture grasp postures and forces. Understanding the kinematics and dynamics of human grasp including reach-to-grasp is undertaken. A comprehensive study involving ten healthy subjects was conducted. Grasp synergy analysis is carried out to identify underlying patterns for grasping. Correlation analysis showed a strong synergy, especially between index and middle fingers with a 0.95 correlation coefficient. Principal component analysis (PCA) facilitated dimensionality reduction, revealing that three principal components (PCs) capture over 97% of the variance in grasp postures, underscoring the complexity and synergy of hand movements. Grasp classification experiments validated the efficacy of PCA-based synergy, achieving high classification accuracies (95.84%–92.34%) and demonstrating the method’s competitive performance in scenarios requiring reduced sensor complexity, as confirmed by confusion matrices and comparative analysis with existing methodologies. The t-distributed stochastic neighbor embedding (t-SNE) visualization showcased clusters of grasp postures and forces, unveiling similarities and patterns among different grasp types (GTs). These findings could serve as a comprehensive guide in the design and control of five-fingered robotic hands and exoskeletons for rehabilitation applications, enabling the replication of natural hand movements.