Abstract

The dataset is related 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 assessing real-time control of a wearable anthropomorphic robot hand. The data were produced in the framework of Horizon Europe INTELLIMAN project and are presented in the publication: R. Meattini, A. Caporali, A. Bernardini, G. Palli and C. Melchiorri, "Self-Supervised Regression of sEMG Signals Combining Non-Negative Matrix Factorization With Deep Neural Networks for Robot Hand Multiple Grasping Motion Control," in IEEE Robotics and Automation Letters, vol. 8, no. 12, pp. 8533-8540, Dec. 2023, doi: 10.1109/LRA.2023.3329764.