ArticleSensors (Basel, Switzerland)2023
Myoelectric Pattern Recognition Using Gramian Angular Field and Convolutional Neural Networks for Muscle-Computer Interface.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- NewRep: a classification model for online pattern recognition based on HD-EMG signals.Medical & biological engineering & computing · 2026Article
- Dual-Stream SPP-CNN for High-Precision sEMG Gesture Recognition in Human-Machine Interfaces.Biomimetics (Basel, Switzerland) · 2026Article
- A Lightweight Frozen Multi-Convolution Dual-Branch Network for Efficient sEMG-Based Gesture Recognition.Sensors (Basel, Switzerland) · 2026Article
- sEMG-Based Gesture Recognition Using Sigimg-GADF-MTF and Multi-Stream Convolutional Neural Network.Sensors (Basel, Switzerland) · 2025Article
- An efficient surface electromyography-based gesture recognition algorithm based on multiscale fusion convolution and channel attention.Scientific reports · 2024Article
- Predicting Adherence to Computer-Based Cognitive Training Programs Among Older Adults: Study of Domain Adaptation and Deep Learning.JMIR aging · 2024Article
- The Impact of Feature Extraction on Classification Accuracy Examined by Employing a Signal Transformer to Classify Hand Gestures Using Surface Electromyography Signals.Sensors (Basel, Switzerland) · 2024Article
- Wi-Fi sensing gesture control algorithm based on semi-supervised generative adversarial network.PeerJ. Computer science · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In the field of the muscle-computer interface, the most challenging task is extracting patterns from complex surface electromyography (sEMG) signals to improve the performance of myoelectric pattern recognition. To address this problem, a two-stage architecture, consisting of Gramian angular field (GAF)-based 2D representation and convolutional neural network (CNN)-based classification (GAF-CNN), is proposed. To explore discriminant channel features from sEMG signals, sEMG-GAF transformation is proposed for time sequence signal representation and feature modeling, in which the instantaneous values of multichannel sEMG signals are encoded in image form. A deep CNN model is introduced to extract high-level semantic features lying in image-form-based time sequence signals concerning instantaneous values for image classification. An insight analysis explains the rationale behind the advantages of the proposed method. Extensive experiments are conducted on benchmark publicly available sEMG datasets, i.e., NinaPro and CagpMyo, whose experimental results validate that the proposed GAF-CNN method is comparable to the state-of-the-art methods, as reported by previous work incorporating CNN models.
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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.