A database of hand kinematics, high-density sEMG of forearm and wrist for motion intent recognition

Surface electromyography (sEMG) signals reflect spinal motor neuron activities
and can be used as intuitive inputs for human-machine interaction (HMI) via
movement intent recognition. The motor neuron potentials of far-field (wrist)
and near-field (forearm) decomposed from high-density (HD) sEMG prospectively
provide robust neural drives for HMI, which is a challenging research hotspot.
However, there are no publicly available databases that include HD sEMG
signals of forearm-wrist (FW) muscles, and hand kinematics (KIN). This paper
presents the HD-FW KIN dataset that comprises HD 448-channel sEMG arrays
distributed on the forearm and wrist with simultaneous recording of finger
joint angles and finger flexion forces. This dataset contains muscle
activities of 21 subjects while performing 20 hand gestures, and 9 individual
or combined finger flexion under two force levels. The usability of HD-sEMG
for hand gesture recognition, finger angle, and force prediction was
validated. The proposed database allows a comprehensive extraction of the
neural drive from the forearm and wrist, providing neural interfaces for the
development of advanced prosthetic hand and wrist-worn consumer electronics.