ArticleData in brief2022
Dataset for multi-channel surface electromyography (sEMG) signals of hand gestures.
Article in Data in brief, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 53 citations in OpenAlex.
- Evaluation of MPEG-4 AAC Audio Codec Using EEG and EMG Signals for Use with DICOM® Neurophysiology.Journal of medical systems · 2026Article
- Design and Evaluation of a Compact CNN for EMG-Based Wearable Systems Under Embedded Constraints.Sensors (Basel, Switzerland) · 2026Article
- EMG-Spectrogram-Empowered CNN Stroke-Classifier Model Development.Life (Basel, Switzerland) · 2026Article
- Muscle myography for human-machine interfaces: a review of sensing modalities and control interfaces.Frontiers in bioengineering and biotechnology · 2026Review
- Improving Fast EMG Classification for Hand Gesture Recognition: A Comprehensive Analysis of Temporal, Spatial, and Algorithm Configurations for Healthy and Post-Stroke Subjects.Sensors (Basel, Switzerland) · 2025Article
- Assisting hand gesture classification and rehabilitation assessment via sEMG and finger motion data.Frontiers in bioengineering and biotechnology · 2025Article
- Hand gestures classification of sEMG signals based on BiLSTM-metaheuristic optimization and hybrid U-Net-MobileNetV2 encoder architecture.Scientific reports · 2024Article
- Development of an Electromyography Signal Acquisition Prototype and Statistical Validation Against a Commercial Device.Sensors (Basel, Switzerland) · 2024Article
- A dataset for fatigue estimation during shoulder internal and external rotation movements using wearables.Scientific data · 2024Article
- Synthesis of sEMG Signals for Hand Gestures Using a 1DDCGAN.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper presents an electromyography (EMG) signal dataset 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 current datasets in the literature or for validation of machine learning and deep learning models created with different datasets in accordance with the participant-independent validation strategy.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.