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Electromyogram-Based Classification of Hand and Finger Gestures Using Artificial Neural Networks

https://doi.org/10.3390/s22010225

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-88-470-2463-2openalex-snapshot

Atlas of Muscle Innervation Zones: Understanding Surface Electromyography and Its Applications

inferredcites →https://doi.org/10.1007/bf02446704openalex-snapshot

Biomedical signal processing (in four parts)

inferredcites →https://doi.org/10.1007/s00521-019-04142-8openalex-snapshot

Surface EMG hand gesture recognition system based on PCA and GRNN

inferredcites →https://doi.org/10.1007/s11107-018-0809-1openalex-snapshot

Research on EMG segmentation algorithm and walking analysis based on signal envelope and integral electrical signal

inferredcites →https://doi.org/10.1007/s11517-019-02073-zopenalex-snapshot

Evaluation of surface EMG-based recognition algorithms for decoding hand movements

inferredcites →https://doi.org/10.1007/s11771-015-2698-0openalex-snapshot

Multi-channel electromyography pattern classification using deep belief networks for enhanced user experience

inferredcites →https://doi.org/10.1016/0013-4694(87)90003-4openalex-snapshot

EMG profiles during normal human walking: stride-to-stride and inter-subject variability

inferredcites →https://doi.org/10.1016/1050-6411(95)00015-1openalex-snapshot

EMG patterns during running: Intra- and inter-individual variability

inferredcites →https://doi.org/10.1016/j.bbe.2017.03.001openalex-snapshot

Comparative evaluation of EMG signal features for myoelectric controlled human arm prosthetics

inferredcites →https://doi.org/10.1016/j.bbe.2017.11.001openalex-snapshot

A bionic hand controlled by hand gesture recognition based on surface EMG signals: A preliminary study

inferredcites →https://doi.org/10.1016/j.bspc.2007.07.009openalex-snapshot

Myoelectric control systems—A survey

inferredcites →https://doi.org/10.1016/j.bspc.2013.06.006openalex-snapshot

The recognition of multi-finger prehensile postures using LDA

inferredcites →https://doi.org/10.1016/j.bspc.2019.101834openalex-snapshot

EMG-driven hand model based on the classification of individual finger movements

inferredcites →https://doi.org/10.1016/j.bspc.2020.102210openalex-snapshot

EMG hand gesture classification using handcrafted and deep features

inferredcites →https://doi.org/10.1016/j.cmpb.2014.06.013openalex-snapshot

Feature extraction of the first difference of EMG time series for EMG pattern recognition

inferredcites →https://doi.org/10.1016/j.compbiomed.2019.103375openalex-snapshot

A review of feature selection methods in medical applications

inferredcites →https://doi.org/10.1016/j.eswa.2012.01.102openalex-snapshot

Feature reduction and selection for EMG signal classification

inferredcites →https://doi.org/10.1016/j.jelekin.2011.12.012openalex-snapshot

High density electromyography data of normally limbed and transradial amputee subjects for multifunction prosthetic control

inferredcites →https://doi.org/10.1016/j.jelekin.2018.05.010openalex-snapshot

Low-cost electromyography – Validation against a commercial system using both manual and automated activation timing thresholds

inferredcites →https://doi.org/10.1016/j.jelekin.2019.102363openalex-snapshot

Tutorial. Surface EMG detection in space and time: Best practices

inferredcites →https://doi.org/10.1016/j.jelekin.2020.102440openalex-snapshot

Tutorial. Surface EMG detection, conditioning and pre-processing: Best practices

inferredcites →https://doi.org/10.1016/j.neunet.2014.03.010openalex-snapshot

Towards limb position invariant myoelectric pattern recognition using time-dependent spectral features

inferredcites →https://doi.org/10.1016/j.procs.2015.12.346openalex-snapshot

Electromyography (EMG) based Classification of Neuromuscular Disorders using Multi-Layer Perceptron

inferredcites →https://doi.org/10.1109/10.204774openalex-snapshot

A new strategy for multifunction myoelectric control

inferredcites →https://doi.org/10.1109/10.914793openalex-snapshot

A wavelet-based continuous classification scheme for multifunction myoelectric control

inferredcites →https://doi.org/10.1109/86.481972openalex-snapshot

EMG feature evaluation for movement control of upper extremity prostheses

inferredcites →https://doi.org/10.1109/icacomit.2015.7440146openalex-snapshot

Finger movement pattern recognition method using artificial neural network based on electromyography (EMG) sensor

inferredcites →https://doi.org/10.1109/icmete.2018.00059openalex-snapshot

On the Use of Temporal and Spectral Central Moments of Forearm Surface EMG for Finger Gesture Classification

inferredcites →https://doi.org/10.1109/icom.2011.5937135openalex-snapshot

Electromygraphy (EMG) signal based hand gesture recognition using artificial neural network (ANN)

inferredcites →https://doi.org/10.1109/incae.2018.8579153openalex-snapshot

MYO Armband sensors and Neural Network Algorithm for Controlling Hand Robot

inferredcites →https://doi.org/10.1109/itaic.2019.8785894openalex-snapshot

Real-time Pattern Recognition for Hand Gesture Based on ANN and Surface EMG

inferredcites →https://doi.org/10.1109/micai.2012.23openalex-snapshot

EMG Pattern Recognition System Based on Neural Networks

inferredcites →https://doi.org/10.1109/mipro.2015.7160458openalex-snapshot

A review of feature selection methods with applications

inferredcites →https://doi.org/10.1109/sii.2017.8279278openalex-snapshot

Identification of finger movements from forearm surface EMG using an augmented probabilistic neural network

inferredcites →https://doi.org/10.1109/tbme.2003.813539openalex-snapshot

A robust, real-time control scheme for multifunction myoelectric control

inferredcites →https://doi.org/10.1109/tbme.2006.883695openalex-snapshot

A Real-Time EMG Pattern Recognition System Based on Linear-Nonlinear Feature Projection for a Multifunction Myoelectric Hand

inferredcites →https://doi.org/10.1109/tbme.2006.889192openalex-snapshot

A Comparison of Surface and Intramuscular Myoelectric Signal Classification

inferredcites →https://doi.org/10.1109/tbme.2013.2238939openalex-snapshot

Confidence-Based Rejection for Improved Pattern Recognition Myoelectric Control

inferredcites →https://doi.org/10.1109/tii.2018.2869394openalex-snapshot

Spatial Information Enhances Myoelectric Control Performance With Only Two Channels

inferredcites →https://doi.org/10.1109/tnsre.2009.2039590openalex-snapshot

Multiple Binary Classifications via Linear Discriminant Analysis for Improved Controllability of a Powered Prosthesis

inferredcites →https://doi.org/10.1109/tnsre.2009.2039619openalex-snapshot

Quantifying Pattern Recognition—Based Myoelectric Control of Multifunctional Transradial Prostheses

inferredcites →https://doi.org/10.1142/s0219843612500077openalex-snapshot

DYNAMIC HAND MOTION RECOGNITION BASED ON TRANSIENT AND STEADY-STATE EMG SIGNALS

inferredcites →https://doi.org/10.1155/2016/6481282openalex-snapshot

Basic Hand Gestures Classification Based on Surface Electromyography

inferredcites →https://doi.org/10.1186/1743-0003-11-110openalex-snapshot

A state-based, proportional myoelectric control method: online validation and comparison with the clinical state-of-the-art

inferredcites →https://doi.org/10.1186/1743-0003-8-25openalex-snapshot

Influence of the training set on the accuracy of surface EMG classification in dynamic contractions for the control of multifunction prostheses

inferredcites →https://doi.org/10.1186/1751-0473-8-11openalex-snapshot

BioPatRec: A modular research platform for the control of artificial limbs based on pattern recognition algorithms

inferredcites →https://doi.org/10.1201/9781420036985.ax2openalex-snapshot

Standards for Reporting EMG Data*

inferredcites →https://doi.org/10.13140/2.1.1397.4724openalex-snapshot

Spatial Registration of Hand Muscle Electromyography Signals

inferredcites →https://doi.org/10.1371/journal.pone.0144998openalex-snapshot

Intra-Individual Variability of Surface Electromyography in Front Crawl Swimming

inferredcites →https://doi.org/10.3390/app10196960openalex-snapshot

Biometrics Verification Modality Using Multi-Channel sEMG Wearable Bracelet

inferredcites →https://doi.org/10.3390/app7101050openalex-snapshot

Assessment on Stationarity of EMG Signals with Different Windows Size During Isotonic Contractions

inferredcites →https://doi.org/10.3390/app7111163openalex-snapshot

Experimental Study of Real-Time Classification of 17 Voluntary Movements for Multi-Degree Myoelectric Prosthetic Hand

inferredcites →https://doi.org/10.3390/app9245343openalex-snapshot

Exploration of Feature Extraction Methods and Dimension for sEMG Signal Classification

inferredcites →https://doi.org/10.3390/bdcc2030021openalex-snapshot

EMG Pattern Recognition in the Era of Big Data and Deep Learning

inferredcites →https://doi.org/10.3390/fi11010025openalex-snapshot

Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network

inferredcites →https://doi.org/10.3390/s16081304openalex-snapshot

A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions

inferredcites →https://doi.org/10.3390/s18051615openalex-snapshot

Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors

inferredcites →https://doi.org/10.3390/s19143170openalex-snapshot

Real-Time Surface EMG Pattern Recognition for Hand Gestures Based on an Artificial Neural Network

inferredcites →https://doi.org/10.3390/s19204596openalex-snapshot

Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation

inferredcites →https://doi.org/10.3390/s19235214openalex-snapshot

Validation of a Low-Cost Electromyography (EMG) System via a Commercial and Accurate EMG Device: Pilot Study

inferredcites →https://doi.org/10.3390/s20061642openalex-snapshot

Performance Evaluation of Convolutional Neural Network for Hand Gesture Recognition Using EMG

inferredcites →https://doi.org/10.3390/s20164359openalex-snapshot

Analysis of Influence of Segmentation, Features, and Classification in sEMG Processing: A Case Study of Recognition of Brazilian Sign Language Alphabet

inferredcites →https://doi.org/10.3390/s21165677openalex-snapshot

Real-Time and Offline Evaluation of Myoelectric Pattern Recognition for the Decoding of Hand Movements

inferredcites →https://doi.org/10.3390/sym12040541openalex-snapshot

Pattern Recognition of Single-Channel sEMG Signal Using PCA and ANN Method to Classify Nine Hand Movements

inferredcites →https://doi.org/10.5772/52556openalex-snapshot

Signal Acquisition Using Surface EMG and Circuit Design Considerations for Robotic Prosthesis

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Basic Hand Movement Classification Using Q Factor Based Wavelet Scattering Transform

inferred← citeshttps://doi.org/10.1007/978-3-032-16102-4_6openalex-snapshot

Advanced Hand Gesture Classification Using Dual Modalities: EMG-FMG

inferred← citeshttps://doi.org/10.1007/978-981-96-2482-9_12openalex-snapshot

Electromyography-Based Hand Movement Detection Using Machine Learning Algorithm

inferred← citeshttps://doi.org/10.1007/978-981-96-4543-5_45openalex-snapshot

Hand Gesture Recognition Based on Surface Electromyography Signals with Wavelet Packet Transform Using ANN and LDA

inferred← citeshttps://doi.org/10.1007/978-981-99-6062-0_22openalex-snapshot

Finger Movement Classification from EMG Signals Using Gaussian Mixture Model

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Improvement in the classification of EMG signals through a convolutional neural network

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Towards semi-supervised myoelectric finger motion recognition based on spatial motor units activation

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EMG-based prosthetics control using NeuroAttend-EMG: A deep learning approach with explainable AI (XAI) on edge devices

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Hand gesture classification using time–frequency images and transfer learning based on CNN

inferred← citeshttps://doi.org/10.1016/j.bspc.2025.108845openalex-snapshot

DSRANet: A lightweight Depthwise Separable Residual Attention Network for sEMG gesture recognition

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From Pixels to sEMG: Signal Processing and Fusion Architectures for Multimodal Gesture Recognition — A Systematic Review

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Towards objective identification of myofascial trigger points: A high-density surface electromyography method

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Optimized k-nearest neighbors for classification of prosthetic hand movements using electromyography signal

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A systematic review on surface electromyography-based classification system for identifying hand and finger movements

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A novel machine learning algorithm for finger movement classification from surface electromyogram signals using welch power estimation

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Investigation of gesture recognition based on optical fiber Bragg grating sensors

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Hand gesture classification of EMG signal processing using a hyperparameter-tuned deep learning model

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Multi-scale EMG classification with spatial-temporal attention for prosthetic hands

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MS-CLSTM: Myoelectric Manipulator Gesture Recognition Based on Multi-Scale Feature Fusion CNN-LSTM Network

inferred← citeshttps://doi.org/10.3390/bios14110553openalex-snapshot

Hybrid Functional Near-Infrared Spectroscopy System and Electromyography for Prosthetic Knee Control

inferred← citeshttps://doi.org/10.3390/data10120194openalex-snapshot

SurfaceEMG Datasets for Hand Gesture Recognition Under Constant and Three-Level Force Conditions

inferred← citeshttps://doi.org/10.3390/ecsa-11-20351openalex-snapshot

An Internet of Medical Things-Based Smart Electromyogram Device for Monitoring of Musculoskeletal Disorders

inferred← citeshttps://doi.org/10.3390/electronics13091791openalex-snapshot

Predicting Gait Parameters of Leg Movement with sEMG and Accelerometer Using CatBoost Machine Learning

inferred← citeshttps://doi.org/10.3390/electronics13153072openalex-snapshot

Optimizing Sensor Placement and Machine Learning Techniques for Accurate Hand Gesture Classification

inferred← citeshttps://doi.org/10.3390/electronics14061129openalex-snapshot

Comparing Physiological Synchrony and User Copresent Experience in Virtual Reality: A Quantitative–Qualitative Gap

inferred← citeshttps://doi.org/10.3390/mi13101681openalex-snapshot

Design and Implementation of a Prosthesis System Controlled by Electromyographic Signals Means, Characterized with Artificial Neural Networks

inferred← citeshttps://doi.org/10.3390/mi15070891openalex-snapshot

The Latest Research Progress on Bionic Artificial Hands: A Systematic Review

inferred← citeshttps://doi.org/10.3390/s22093393openalex-snapshot

Machine Learning for Detection of Muscular Activity from Surface EMG Signals

inferred← citeshttps://doi.org/10.3390/s24113638openalex-snapshot

Comparative Study of sEMG Feature Evaluation Methods Based on the Hand Gesture Classification Performance

inferred← citeshttps://doi.org/10.3390/s24206710openalex-snapshot

Pattern Recognition in the Processing of Electromyographic Signals for Selected Expressions of Polish Sign Language

inferred← citeshttps://doi.org/10.3390/s25072292openalex-snapshot

Enhancing Connected Health Ecosystems Through IoT-Enabled Monitoring Technologies: A Case Study of the Monit4Healthy System

inferred← citeshttps://doi.org/10.3390/s25226980openalex-snapshot

Improving Fast EMG Classification for Hand Gesture Recognition: A Comprehensive Analysis of Temporal, Spatial, and Algorithm Configurations for Healthy and Post-Stroke Subjects

inferred← citeshttps://doi.org/10.3390/technologies14060371openalex-snapshot

Sensor Fusion-Based Smart Glove for Deterministic Sign Language Recognition: An IoT-Enabled System

inferred← citeshttps://doi.org/10.35429/jten.2022.17.6.27.34openalex-snapshot

Implementation of a neural network of low computational cost for its application in arm prostheses

inferred← citeshttps://doi.org/10.35784/acs_6867openalex-snapshot

Spatial identification of manipulable objects for a bionic hand prosthesis

inferred← citeshttps://doi.org/10.36548/jei.2022.3.007openalex-snapshot

Evaluating Performance of Different Machine Learning Algorithms for the Acute EMG Hand Gesture Datasets

inferred← citeshttps://doi.org/10.4108/eai.7-11-2023.2342938openalex-snapshot

Discrete Wavelet and Neural Network Algorithm for Real-time Identification Finger Movement

inferred← citeshttps://doi.org/10.48084/etasr.7040openalex-snapshot

A Data Acquisiton System with sEMG Signal and Camera Images for Finger Classification with Machine Learning Algorithms

inferred← citeshttps://doi.org/10.5120/ijca2025924776openalex-snapshot

Hand Gesture Recognition Systems: A Review of Methods, Datasets, and Emerging Trends

inferred← citeshttps://doi.org/10.51466/jeeit2382212109hopenalex-snapshot

MECHANICAL DESIGN OF A LOW-COST 3D PRINTED ARTIFICIAL FOREARM PROSTHESIS WITH THE ABILITY FOR INDIVIDUAL FINGER CONTROL

inferred← citeshttps://doi.org/10.5377/nexo.v36i06.17458openalex-snapshot

Design a 1D-CNN'S to classify surface EMG (SEMG) signals

inferred← citeshttps://doi.org/10.70675/c23bccf4z1ec3z4a62zab95z4257318e6e97openalex-snapshot

SEMG time-frequency features for hand movements classification

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