Exploiting the Intertemporal Structure of the Upper-Limb sEMG: Comparisons between an LSTM Network and Cross-Sectional Myoelectric Pattern Recognition Methods
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IEEE
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http://xplorestaging.ieee.org/ielx7/8844528/8856280/08856648.pdf?arnumber=8856648
Cited by 14 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. A Multidataset Characterization of Window-Based Hyperparameters for Deep CNN-Driven sEMG Pattern Recognition;IEEE Transactions on Human-Machine Systems;2024-02
2. Deep Scattering Transform with Attention Mechanisms Improves EMG-based Hand Gesture Recognition;2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC);2023-07-24
3. LSTM-MSA: A Novel Deep Learning Model With Dual-Stage Attention Mechanisms Forearm EMG-Based Hand Gesture Recognition;IEEE Transactions on Neural Systems and Rehabilitation Engineering;2023
4. Myoelectric Control With Fixed Convolution-Based Time-Domain Feature Extraction: Exploring the Spatio–Temporal Interaction;IEEE Transactions on Human-Machine Systems;2022-12
5. Analyzing the Impact of Varied Window Hyper-parameters on Deep CNN for sEMG based Motion Intent Classification;2022 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT);2022-06-07
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