ArticleSensors (Basel, Switzerland)2019
putEMG-A Surface Electromyography Hand Gesture Recognition Dataset.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- A Co-Located sEMG-pFMG Dataset for Hand Gesture Recognition Under Varying Arm-Position Conditions.Sensors (Basel, Switzerland) · 2026Article
- An iontronic reservoir for highly robust neuromorphic prosthesis.Nature materials · 2026Article
- Muscle myography for human-machine interfaces: a review of sensing modalities and control interfaces.Frontiers in bioengineering and biotechnology · 2026Review
- Stage-specific EMG feature optimization for enhanced post-stroke hand gesture recognition.Journal of neuroengineering and rehabilitation · 2025Article
- 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
- EMG Dataset for Gesture Recognition with Arm Translation.Scientific data · 2025Article
- Estimation of Lower Limb Joint Angles Using sEMG Signals and RGB-D Camera.Bioengineering (Basel, Switzerland) · 2024Article
- A Multimodal Bracelet to Acquire Muscular Activity and Gyroscopic Data to Study Sensor Fusion for Intent Detection.Sensors (Basel, Switzerland) · 2024Article
- Article
- A Surface Electromyography (sEMG) System Applied for Grip Force Monitoring.Sensors (Basel, Switzerland) · 2024Article
- Synthesis of sEMG Signals for Hand Gestures Using a 1DDCGAN.Bioengineering (Basel, Switzerland) · 2023Article
- Mechanical disturbances applied by motorized ankle foot orthosis to adapt ankle muscles activation-A validation study.Frontiers in bioengineering and biotechnology · 2023Article
- Multi-dimensional task recognition for human-robot teaming: literature review.Frontiers in robotics and AI · 2023Review
- sEMG-Based Continuous Hand Action Prediction by Using Key State Transition and Model Pruning.Sensors (Basel, Switzerland) · 2022Article
- Multi-day dataset of forearm and wrist electromyogram for hand gesture recognition and biometrics.Scientific data · 2022Article
- Dataset for multi-channel surface electromyography (sEMG) signals of hand gestures.Data in brief · 2022Article
- Deep learning and session-specific rapid recalibration for dynamic hand gesture recognition from EMG.Frontiers in bioengineering and biotechnology · 2022Article
- Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography.Sensors (Basel, Switzerland) · 2021Article
- Qualitative Recognition of Primary Taste Sensation Based on Surface Electromyography.Sensors (Basel, Switzerland) · 2021Article
- Data Augmentation of Surface Electromyography for Hand Gesture Recognition.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In this paper, we present a putEMG dataset intended for the evaluation of hand gesture recognition methods based on sEMG signal. The dataset was acquired for 44 able-bodied subjects and include 8 gestures (3 full hand gestures, 4 pinches and idle). It consists of uninterrupted recordings of 24 sEMG channels from the subject's forearm, RGB video stream and depth camera images used for hand motion tracking. Moreover, exemplary processing scripts are also published. The putEMG dataset is available under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). The dataset was validated regarding sEMG amplitudes and gesture recognition performance. The classification was performed using state-of-the-art classifiers and feature sets. An accuracy of 90% was achieved for SVM classifier utilising RMS feature and for LDA classifier using Hudgin's and Du's feature sets. Analysis of performance for particular gestures showed that LDA/Du combination has significantly higher accuracy for full hand gestures, while SVM/RMS performs better for pinch gestures. The presented dataset can be used as a benchmark for various classification methods, the evaluation of electrode localisation concepts, or the development of classification methods invariant to user-specific features or electrode displacement.
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Registered trials
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.