Simultaneous Estimation of Manipulation Skill and Hand Grasp Force from Forearm Ultrasound Images
This work addresses the need for accurate human-robot skill transfer in teleoperation, though it is incremental as it builds on existing biosignal-based methods.
The study tackled the problem of estimating human hand configuration and forces for robotic teleoperation by using forearm ultrasound data to simultaneously classify manipulation skills and estimate grasp force, achieving 94.87% classification accuracy and 0.51 N RMSE in force estimation.
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.