Dataset for multi-channel surface electromyography (sEMG) signals of hand gestures

Published: 22 December 2021| Version 2 | DOI: 10.17632/ckwc76xr2z.2


This dataset contains electromyography (EMG) signals for use in human-computer interaction studies. The dataset includes 4-channel surface EMG data from 40 participants with an equal gender distribution. The gestures in the data are rest or neutral state, extension of the wrist, flexion of the wrist, ulnar deviation of the wrist, radial deviation of the wrist, grip, abduction of all fingers, adduction of all fingers, supination, and pronation. Data were collected from 4 forearm muscles when simulating 10 unique hand gestures and recorded with the BIOPAC MP36 device using Ag/AgCl surface bipolar electrodes. Each participant's data contains five repetitive cycles of ten hand gestures. A demographic survey was applied to the participants before the signal recording process. This data can be utilized for recognition, classification, and prediction studies in order to develop EMG-based hand movement controller systems. The dataset can also be useful as a reference to create an artificial intelligence model (especially a deep learning model) to detect gesture-related EMG signals. Additionally, it is encouraged to use the proposed dataset for benchmarking of current datasets or for validation of machine learning and deep learning models created with different datasets in accordance with the participant-independent validation strategy.


Steps to reproduce

A demographic survey was done to choose proper individuals for data collection and collect background information about participants' data before the signal recording. Then, EMG signals were acquired from a 4-channel MP36 model BIOPAC device (BIOPAC Co., USA). MP36 Data Acquisition Unit includes 4 certified human-safe input channels and built-in amplifiers and uses BSL 4 software. In the data collection stage, SS2LB electrode lead sets with smart and simple sensors connectors, and non-invasive 3M brand Red Dot monitoring Ag/AgCl surface electrodes (3.3x3.99 cm sizes) with disposable and highly adhesive were used. Before electrodes were attached to the forearm, the skin surface is cleaned by alcohol to remove dead cells and oils. The approximate locations of four forearm muscles were determined by an expert physician, and then BIOPAC system electrode gel (GEL1) was applied to the skin. The sensors were calibrated when the electrodes were placed on the participant. The procedure slide was begun with recording simultaneously. The whole data were collected in the same environment and conditions by minimizing environmental conditions to prevent noises.


Izmir Katip Celebi Universitesi


Signal Processing, Biomedical Engineering, Muscle, Movement, Gesture Analysis, Hand, Electromyography, Biomedical Signal Processing, Gesture, Gesture Recognition