Surface electromyography dataset from different movements of the hand using a portable and a non-portable devicegithub.com/emoralesv/MuscleTracker-Hand-Movement

Abstract

This work presents the MuscleTracker Hand Movement dataset, containing Surface Electromyography (sEMG) data from the right arm of 49 healthy subjects without neuromuscular or cardiovascular issues. Subjects performed five hand movements—pronation with extended fingers, flexion, extension, pronation with flexed fingers, and relaxation—while standing, with one hand palm-down. Data was recorded from two sEMG channels using Biopac MP36 (1000 Hz) and MuscleTracker (512 Hz), with three and four repetitions per device, respectively, for each movement. The dataset includes 825 samples, along with subject details such as gender, age, physical condition, and, for MuscleTracker subjects, anthropometric measurements. This data supports machine-learning development for classifying hand gestures in sEMG signals, with applications in prosthetics control and human-computer interaction. In addition, validation experiments were performed to validate the database and stablish a comparison baseline.

Keywords

• Electromyographic signals
• Machine learning
• Hand movement dataset
• Biomedical engineering
• Human-computer interaction