Dataset of Surface Electromyographic (sEMG) Signals and Finger Kinematics

Accurate proportional myo-electric control of the hand is important in replicating dexterous manipulation in robot prostheses. Many studies in this field have focused on recording discrete hand gestures, while few have focused on the proportional and multiple-DOF control of the human hand using EMG signals. To aid researchers on advanced myoelectric hand control and estimation, we present this data from our work Extraction of nonlinear muscle synergies for proportional and simultaneous estimation of finger kinematics. In our study, surface lectromyographic (sEMG) signals from the forearm and finger joint marker data were recorded from able-bodied subjects while they were tasked to do individual, simultaneous and random multiple finger flexion and extension movements. Included in this dataset are the EMG signals from 8 extrinsic muscles along the forearm, and as much as 23 joint markers attached on the hand obtained from 10 subjects. More description about the experimental protocol, signal processing methods and equipments used are described in the paper below. - S.K.Dwivedi, J. Ngeo, T.Shibata, Transaction of Biomedical Engineering, In Press, Extraction of Nonlinear Synergies for Proportional and Simultaneous Estimation of Finger Kinematics.