Abstract

The dataset contain the data related to a novel sEMG-based minimally supervised regression approach capable of performing nonlinear fitting without the necessity for point-by-point training data labelling, exploiting a differentiable version of the Dynamic Time Warping (DTW) similarity – referred to as soft-DTW divergence – as loss function for a flexible neural network architecture. This is a different paradigm with respect to state-of-the-art approaches in which sEMG-based control of robot hands is mainly realized using supervised or unsupervised machine learning based regression. The data are presented in the publication: R. Meattini, A. Bernardini, G. Palli and C. Melchiorri, "sEMG-Based Minimally Supervised Regression Using Soft-DTW Neural Networks for Robot Hand Grasping Control," in IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 10144-10151, Oct. 2022, doi: 10.1109/LRA.2022.3193247.