Understanding Grasp Synergies during Reach-to-grasp using an Instrumented Data Glove

TL;DR

This study introduces an instrumented 3D-printed data glove with flexible and force sensors to analyze human grasp synergies during reach-to-grasp, aiding the development of more natural hand exoskeletons.

Contribution

The paper presents a novel, sensor-enhanced data glove and a comprehensive analysis of grasp synergies using t-SNE visualization with data from 10 subjects.

Findings

Clusters of grasp postures identified

Patterns in grasp forces revealed

Insights for exoskeleton control strategies

Abstract

Data gloves play a crucial role in study of human grasping, and could provide insights into grasp synergies. Grasp synergies lead to identification of underlying patterns to develop control strategies for hand exoskeletons. This paper presents the design and implementation of a data glove that has been enhanced with instrumentation and fabricated using 3D printing technology. 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 10 healthy subjects was conducted. Grasp synergy analysis is carried out to identify underlying patterns for robotic grasping. The t-SNE visualization showcased clusters of grasp postures and forces, unveiling similarities and patterns among different GTs. These findings could serve as a comprehensive guide in design and control of tendon-driven soft hand exoskeletons for rehabilitation applications, enabling the replication of natural hand movements and grasp forces.