Abstract

The datasets contain the data related to an augmented kinesthetic teaching system, which is based on surface electromyographic (sEMG) measurements from the operator forearm. Specifically, sEMG signals are used for minimal-training unsupervised estimation of forearm's muscles co-contraction level. In this way, also exploiting a vibrotactile bio-feedback, we evaluate the ability of operators in stiffening their hand - during kinesthetic teaching - in order to modulate the estimated level of muscle co-contraction to (i) match target levels and (ii) command the opening/closing of a gripper, i.e. in exploiting their sEMG signals for effective augmented robot kinesthetic teaching tasks. The data are related to the publication: R. Meattini, D. Chiaravalli, L. Biagiotti, G. Palli and C. Melchiorri, "Combining Unsupervised Muscle Co-Contraction Estimation With Bio-Feedback Allows Augmented Kinesthetic Teaching," in IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 6180-6187, Oct. 2021, doi: 10.1109/LRA.2021.3092269.