ArticleSensors (Basel, Switzerland)2019
Real-Time Surface EMG Pattern Recognition for Hand Gestures Based on an Artificial Neural Network.
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 35 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2020Pooled it
- Electrode-Level Low-Dimensionality Does Not Guarantee Sensor Redundancy: Dual-Dataset, Participant-Grouped Validation of Parsimonious Myoelectric Gesture Decoding.Biomimetics (Basel, Switzerland) · 2026Article
- Depth-Sensitive Optical Sensing for Non-Invasive Measurement of Human Muscle Activity.Sensors (Basel, Switzerland) · 2026Article
- Adaptive robotic arm control through digital twin integration and hybrid neural networks.Scientific reports · 2026Article
- Novel Wearable System to Recognize Sign Language in Real Time.Sensors (Basel, Switzerland) · 2024Article
- Reliability and validity of muscle activity analysis using wearable electromyographs.Journal of physical therapy science · 2024Article
- User identification system based on 2D CQT spectrogram of EMG with adaptive frequency resolution adjustment.Scientific reports · 2024Article
- On the Distribution of Muscle Signals: A Method for Distance-Based Classification of Human Gestures.Sensors (Basel, Switzerland) · 2023Article
- Transformer-based hand gesture recognition from instantaneous to fused neural decomposition of high-density EMG signals.Scientific reports · 2023Article
- Myoelectric Pattern Recognition Using Gramian Angular Field and Convolutional Neural Networks for Muscle-Computer Interface.Sensors (Basel, Switzerland) · 2023Article
- Exploring the EMG transient: the muscular activation sequences used as novel time-domain features for hand gestures classification.Frontiers in neurorobotics · 2023Article
- A flexible pressure sensor with highly customizable sensitivity and linearity via positive design of microhierarchical structures with a hyperelastic model.Microsystems & nanoengineering · 2023Article
- Classification of Activities of Daily Living Based on Grasp Dynamics Obtained from a Leap Motion Controller.Sensors (Basel, Switzerland) · 2022Article
- sEMG-Based Hand Posture Recognition and Visual Feedback Training for the Forearm Amputee.Sensors (Basel, Switzerland) · 2022Article
- Design and Implementation of a Prosthesis System Controlled by Electromyographic Signals Means, Characterized with Artificial Neural Networks.Micromachines · 2022Article
- Low-Cost Wearable Band Sensors of Surface Electromyography for Detecting Hand Movements.Sensors (Basel, Switzerland) · 2022Article
- Flexible Electronics and Devices as Human-Machine Interfaces for Medical Robotics.Advanced materials (Deerfield Beach, Fla.) · 2022Review
- Real-Time Analysis of Hand Gesture Recognition with Temporal Convolutional Networks.Sensors (Basel, Switzerland) · 2022Article
- A parallel classification strategy to simultaneous control elbow, wrist, and hand movements.Journal of neuroengineering and rehabilitation · 2022Article
- Improving the Robustness of Human-Machine Interactive Control for Myoelectric Prosthetic Hand During Arm Position Changing.Frontiers in neurorobotics · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
In recent years, surface electromyography (sEMG) signals have been increasingly used in pattern recognition and rehabilitation. In this paper, a real-time hand gesture recognition model using sEMG is proposed. We use an armband to acquire sEMG signals and apply a sliding window approach to segment the data in extracting features. A feedforward artificial neural network (ANN) is founded and trained by the training dataset. A test method is used in which the gesture will be recognized when recognized label times reach the threshold of activation times by the ANN classifier. In the experiment, we collected real sEMG data from twelve subjects and used a set of five gestures from each subject to evaluate our model, with an average recognition rate of 98.7% and an average response time of 227.76 ms, which is only one-third of the gesture time. Therefore, the pattern recognition system might be able to recognize a gesture before the gesture is completed.
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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.