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Corticomuscular coupling alterations in subacute stroke patients: insights from fNIRS and sEMG. [PDF]
BMC NeurolWei X, Wei X, Li Z, Jamid S, Wang H.
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A linear-attention based network for estimating continuous upper limb movement from surface electromyography. [PDF]
Sci RepLin C, Zhao C, Li N, Dai M.
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IFIANet: Frequency Attention Network for Time-Frequency in sEMG-Based Motion Intent Recognition. [PDF]
Sensors (Basel)Zheng G +5 more
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Analysis of the sEMG/force relationship using HD-sEMG technique and data fusion: A simulation study
Computers in Biology and Medicine, 2017The relationship between the surface Electromyogram (sEMG) signal and the force of an individual muscle is still ambiguous due to the complexity of experimental evaluation. However, understanding this relationship should be useful for the assessment of neuromuscular system in healthy and pathological contexts.
Vincent Carriou +2 more
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sEMG time–frequency features for hand movements classification
Expert Systems With Applications, 2022 Les signaux électromyogrammes de surface (sEMG) enregistrés sur l'avant-bras peuvent fournir des informations sur le mouvement de la main, ce qui peut aider à contrôler un implant prothétique pour les personnes handicapées.
Somar Karheily, Jean-Baptiste Courbot
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HAR-sEMG: A Dataset for Human Activity Recognition on Lower-Limb sEMG
Knowledge and Information Systems, 2021In the past decade, human activity recognition (HAR) has grown in popularity due to its applications in security and entertainment. As recent years have witnessed the emergence of health care and exoskeleton robotics which make use of wearable suits, human–machine interaction based on action recognition performs an important role in multimedia ...
Yu Luan +5 more
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A CNN-Transformer Hybrid Recognition Approach for sEMG-Based Dynamic Gesture Prediction
IEEE Transactions on Instrumentation and Measurement, 2023As a unique physiological electrical signal in the human body, surface electromyography (sEMG) signals always include human movement intention and muscle state. Through the collection of sEMG signals, different gestures can be effectively recognized.
Yanhong Liu +4 more
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