Self-Supervised Regression of sEMG Signals Combining Non-Negative Matrix Factorization With Deep Neural Networks for Robot Hand Multiple Grasping Motion Control

Advanced Human-In-The-Loop (HITL) control strategies for robot hands based on surface electromyography (sEMG) are among major research questions in robotics. Due to intrinsic complexity and inaccuracy of labeling procedures, unsupervised regression of sEMG signals has been employed in literature, ho...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2023-12, Vol.8 (12), p.8533-8540
Main Authors: Meattini, Roberto, Caporali, Alessio, Bernardini, Alessandra, Palli, Gianluca, Melchiorri, Claudio
Format: Article
Language:English
Subjects:
Artificial neural networks
Electromyography
End effectors
Factorization
Grasping
Grasping (robotics)
Grippers
Human factors
human factors and human-in-the-loop
Human in the loop
Human motion
Human-robot interaction
intention recognition
Kinematics
Labelling
Man-machine interfaces
Motion control
Multifingered Hands
Neural networks
Regression
Robot control
Robot dynamics
Robotics
Robots
Statistical analysis
Thumb
Wearable technology
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Summary:Advanced Human-In-The-Loop (HITL) control strategies for robot hands based on surface electromyography (sEMG) are among major research questions in robotics. Due to intrinsic complexity and inaccuracy of labeling procedures, unsupervised regression of sEMG signals has been employed in literature, however showing several limitations in realizing multiple grasping motion control. In this letter, we propose a novel Human-Robot interface (HRi) based on self-supervised regression of sEMG signals, combining Non-Negative Matrix Factorization (NMF) with Deep Neural Networks (DNN) in order to both avoid explicit labeling procedures and have powerful nonlinear fitting capabilities. Experiments involving 10 healthy subjects were carried out, consisting of an offline session for systematic evaluations and comparisons with traditional unsupervised approaches, and an online session for assessing real-time control of a wearable anthropomorphic robot hand. The offline results demonstrate that the proposed self-supervised regression approach overcame traditional unsupervised methods, even considering different robot hands with dissimilar kinematic structures. Furthermore, the subjects were able to successfully perform online control of multiple grasping motions of a real wearable robot hand, reporting for high reliability over repeated grasp-transportation-release tasks with different objects. Statistical support is provided along with experimental outcomes.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2023.3329764