前腕超音波画像からの操作スキルと把持力の同時推定
Simultaneous Estimation of Manipulation Skill and Hand Grasp Force from Forearm Ultrasound Images
前腕の超音波画像を用いて、ヒトの操作スキル分類と把持力を同時に推定する深層学習手法を提案し、7名のデータで高い精度を達成した。
著者: Keshav Bimbraw, Srikar Nekkanti, Daniel B. Tiller, Mihir Deshmukh, Berk Calli, Robert D. Howe, Haichong K. Zhang
分類: cs.RO, cs.CV, cs.ET, cs.HC
原文アブストラクト
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.
