A Surface Electromyography Dataset for Hand Gesture Recognition

Surface electromyography (sEMG) is one of the most prevalent methods of upper-limb prosthesis control nowadays, especially when combined with machine learning. However, this approach requires a significant amount of annotated data, including gesture classes and joint angles, the recording of which o...

Full description

Saved in:
Bibliographic Details
Main Authors: Eftimiu, Nikomidisz Jorgosz, Kollod, Csaba, Ulbert, Istvan, Marton, Gergely
Format: Conference Proceeding
Language:English
Subjects:
Cameras
Dataset
Depth camera
Electromyography
Estimation
Joint angle estimation
Location awareness
Point cloud compression
Recording
Three-dimensional displays
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Surface electromyography (sEMG) is one of the most prevalent methods of upper-limb prosthesis control nowadays, especially when combined with machine learning. However, this approach requires a significant amount of annotated data, including gesture classes and joint angles, the recording of which often requires expensive equipment and a major time expenditure. In this paper, we present an sEMG dataset intended for use in prosthesis control system development, and a novel method for the accurate reconstruction of finger movements using depth cameras. We recruited 6 able-bodied volunteers and recorded a set of 7 hand gestures, which we designed to be easily classifiable and similar to those found in other, major publicly available datasets. For data capture, we used an 8-channel device with semi-dry electrodes and a sampling rate of 500 samples-per-second, while also recording finger movements using a pair of commercially available depth cameras. Additionally, we developed an algorithm for the accurate localization of the joints of the hands using 3D point clouds.
ISSN:1949-0488
DOI:10.1109/SISY56759.2022.10036305