Hand kinematics, high-density sEMG comprising forearm and far-field potentials for motion intent recognition.

Abstract

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 forearm and wrist with simultaneously 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 usabilities of HD sEMG for hand gesture recognition, finger angle and force prediction were validated. The proposed database allows a comprehensive extraction of the neural drive from forearm and wrist, providing neural interfaces for the development of advanced prosthetic hand and wrist-worn consumer electronics.