ArticleSensors (Basel, Switzerland)2021
Surface EMG-Based Instantaneous Hand Gesture Recognition Using Convolutional Neural Network with the Transfer Learning Method.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Intelligent Assessment Techniques for Abnormal Movement Patterns in Neurological Disorders: Applications and Advances.Behavioural neurology · 2025Review
- Hand gestures classification of sEMG signals based on BiLSTM-metaheuristic optimization and hybrid U-Net-MobileNetV2 encoder architecture.Scientific reports · 2024Article
- Dual Stream Long Short-Term Memory Feature Fusion Classifier for Surface Electromyography Gesture Recognition.Sensors (Basel, Switzerland) · 2024Article
- On the Distribution of Muscle Signals: A Method for Distance-Based Classification of Human Gestures.Sensors (Basel, Switzerland) · 2023Article
- Empowering Hand Rehabilitation with AI-Powered Gesture Recognition: A Study of an sEMG-Based System.Bioengineering (Basel, Switzerland) · 2023Article
- sEMG-Based Hand Gesture Recognition Using Binarized Neural Network.Sensors (Basel, Switzerland) · 2023Article
- Feasibility study on the application of a spiking neural network in myoelectric control systems.Frontiers in neuroscience · 2023Article
- Best Research Papers in the Field of Sensors, Signals, and Imaging Informatics 2021.Yearbook of medical informatics · 2022Review
- Compound motion decoding based on sEMG consisting of gestures, wrist angles, and strength.Frontiers in neurorobotics · 2022Article
- No Interface, No Problem: Gesture Recognition on Physical Objects Using Radar Sensing.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
In recent years, surface electromyography (sEMG)-based human-computer interaction has been developed to improve the quality of life for people. Gesture recognition based on the instantaneous values of sEMG has the advantages of accurate prediction and low latency. However, the low generalization ability of the hand gesture recognition method limits its application to new subjects and new hand gestures, and brings a heavy training burden. For this reason, based on a convolutional neural network, a transfer learning (TL) strategy for instantaneous gesture recognition is proposed to improve the generalization performance of the target network. CapgMyo and NinaPro DB1 are used to evaluate the validity of our proposed strategy. Compared with the non-transfer learning (non-TL) strategy, our proposed strategy improves the average accuracy of new subject and new gesture recognition by 18.7% and 8.74%, respectively, when up to three repeated gestures are employed. The TL strategy reduces the training time by a factor of three. Experiments verify the transferability of spatial features and the validity of the proposed strategy in improving the recognition accuracy of new subjects and new gestures, and reducing the training burden. The proposed TL strategy provides an effective way of improving the generalization ability of the gesture recognition system.
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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.