ArticleSensors (Basel, Switzerland)2020
A Novel Surface Electromyographic Signal-Based Hand Gesture Prediction Using a Recurrent Neural Network.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- EMG-based hand gesture recognition using multi-scale deep residual network with SE-module.Scientific reports · 2026Article
- Optimizing RNNs for EMG Signal Classification: A Novel Strategy Using Grey Wolf Optimization.Bioengineering (Basel, Switzerland) · 2024Article
- sEMG-Based Continuous Hand Action Prediction by Using Key State Transition and Model Pruning.Sensors (Basel, Switzerland) · 2022Article
- Multi-Category Gesture Recognition Modeling Based on sEMG and IMU Signals.Sensors (Basel, Switzerland) · 2022Article
- Deep learning and session-specific rapid recalibration for dynamic hand gesture recognition from EMG.Frontiers in bioengineering and biotechnology · 2022Article
- Long short-term memory (LSTM) recurrent neural network for muscle activity detection.Journal of neuroengineering and rehabilitation · 2021Article
- Surface EMG-Based Instantaneous Hand Gesture Recognition Using Convolutional Neural Network with the Transfer Learning Method.Sensors (Basel, Switzerland) · 2021Article
- Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future.Frontiers in neuroscience · 2021Review
- Development of Sign Language Motion Recognition System for Hearing-Impaired People Using Electromyography Signal.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Surface electromyographic signal (sEMG) is a kind of bioelectrical signal, which records the data of muscle activity intensity. Most sEMG-based hand gesture recognition, which uses machine learning as the classifier, depends on feature extraction of sEMG data. Recently, a deep leaning-based approach such as recurrent neural network (RNN) has provided a choice to automatically learn features from raw data. This paper presents a novel hand gesture prediction method by using an RNN model to learn from raw sEMG data and predict gestures. The sEMG signals of 21 short-term hand gestures of 13 subjects were recorded with a Myo armband, which is a non-intrusive, low cost, commercial portable device. At the start of the gesture, the trained model outputs an instantaneous prediction for the sEMG data. Experimental results showed that the more time steps of data that were known, the higher instantaneous prediction accuracy the proposed model gave. The predicted accuracy reached about 89.6% when the data of 40-time steps (200 ms) were used to predict hand gesture. This means that the gesture could be predicted with a delay of 200 ms after the hand starts to perform the gesture, instead of waiting for the end of the gesture.
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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.