Simultaneous Estimation of Manipulation Skill and Hand Grasp Force from Forearm Ultrasound Images

TL;DR

This paper introduces a deep learning approach to simultaneously estimate hand manipulation skills and grasp force from forearm ultrasound images, enhancing teleoperation and human-robot interaction.

Contribution

It presents a novel method using ultrasound data and deep learning to accurately estimate manipulation skills and forces, advancing human-machine interfacing.

Findings

Achieved 94.87% average accuracy in classifying manipulation skills.

Estimated grasp force with an average RMSE of 0.51 Newtons.

Demonstrated effectiveness of forearm ultrasound for human-robot interaction.

Abstract

Accurate estimation of human hand configuration and the forces they exert is critical for effective teleoperation and skill transfer in robotic manipulation. A deeper understanding of human interactions with objects can further enhance teleoperation performance. To address this need, researchers have explored methods to capture and translate human manipulation skills and applied forces to robotic systems. Among these, biosignal-based approaches, particularly those using forearm ultrasound data, have shown significant potential for estimating hand movements and finger forces. In this study, we present a method for simultaneously estimating manipulation skills and applied hand force using forearm ultrasound data. Data collected from seven participants were used to train deep learning models for classifying manipulation skills and estimating grasp force. Our models achieved an average classification accuracy of 94.87 percent plus or minus 10.16 percent for manipulation skills and an average root mean square error (RMSE) of 0.51 plus or minus 0.19 Newtons for force estimation, as evaluated using five-fold cross-validation. These results highlight the effectiveness of forearm ultrasound in advancing human-machine interfacing and robotic teleoperation for complex manipulation tasks. This work enables new and effective possibilities for human-robot skill transfer and tele-manipulation, bridging the gap between human dexterity and robotic control.