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A Simple Resha** Method of sEMG Training Data for Faster Convergence in CNN-Based HAR Applications
Convolutional neural networks (CNNs) have demonstrated excellent image recognition performance. CNNs have also been successfully extended to human...
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Training Strategy and sEMG Sensor Positioning for Finger Force Estimation at Various Elbow Angles
Recently, several simultaneous proportional control techniques using surface electromyography (sEMG) have been developed to control protheses....
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Robust Machine Learning Map** of sEMG Signals to Future Actuator Commands in Biomechatronic Devices
A machine learning model for regression of interrupted Surface Electromyography (sEMG) signals to future control-oriented signals (e.g., robot’s...
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Action recognition through fusion of sEMG and skeletal data in feature level
Human action can be recognized through a unimodal way. However, the information obtained from a single mode is limited due to the fact that a single...
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sEMG-based automatic characterization of swallowed materials
Monitoring of ingestive activities is critically important for managing the health and wellness of individuals with various health conditions,...
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STGNN-LMR: A Spatial–Temporal Graph Neural Network Approach Based on sEMG Lower Limb Motion Recognition
Lower limb motion recognition techniques commonly employ Surface Electromyographic Signal (sEMG) as input and apply a machine learning classifier or...
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Stretchable and durable HD-sEMG electrodes for accurate recognition of swallowing activities on complex epidermal surfaces
Surface electromyography (sEMG) is widely used in monitoring human health. Nonetheless, it is challenging to capture high-fidelity sEMG recordings in...
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TFN-FICFM: sEMG-Based Gesture Recognition Using Temporal Fusion Network and Fuzzy Integral-based Classifier Fusion
Surface electromyography (sEMG)-based gesture recognition is a key technology in the field of human–computer interaction. However, existing gesture...
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Multi-modality deep forest for hand motion recognition via fusing sEMG and acceleration signals
Bio-signal based hand motion recognition plays a critical role in the tasks of human-machine interaction, such as the natural control of...
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Estimation and early prediction of grip force based on sEMG signals and deep recurrent neural networks
Hands are used for communicating with the surrounding environment and have a complex structure that enables them to perform various tasks with their...
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sEMG Classification of Upper Limb Movements Under Different Loads
Surface Electromyography (sEMG) might provide new ways of communication or control with devices or surroundings through the use of a Human-Machine...
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sEMG-Based Lower Limb Motion Prediction Using CNN-LSTM with Improved PCA Optimization Algorithm
In recent years, sEMG (surface electromyography) signals have been increasingly used to operate wearable devices. The development of mechanical lower...
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On the use of power-based connectivity between EEG and sEMG signals for three-weight classification during object manipulation tasks
PurposeBrain-machine interfaces (BMIs) have been used for motor rehabilitation of complex movements, such as those based on object manipulation....
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sEMG-Based Gesture Recognition with Spiking Neural Networks on Low-Power FPGA
Classification of surface electromyographic (sEMG) signals for the precise identification of hand gestures is a crucial area in the advancement of...
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Human lower limb activity recognition techniques, databases, challenges and its applications using sEMG signal: an overview
Human lower limb activity recognition (HLLAR) has grown in popularity over the last decade mainly because to its applications in the identification...
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sEMG based hand gesture recognition with deformable convolutional network
There is a growing interest in human machine interface and their applications using surface electromyography (sEMG). sEMG based gesture recognition...
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A Convolutional Neural Network with Narrow Kernel and Dual-View Feature Fusion for sEMG-Based Gesture Recognition
For gesture recognition based on surface Electromyography (sEMG), Convolutional Neural Network (CNN)-based techniques have made prominent progress to...
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Simulative and Experimental Evaluation of a Soft-DTW Neural Network for sEMG-Based Robotic Gras**
In this paper, we present a neural network architecture for minimally supervised regression of surface electromyographic (sEMG) signals into control...
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Enhancing Classification of Gras** Tasks Using Hybrid EEG-sEMG Features
Systems based on multimodal Human-Machine Interface (HMI) propose a significant advance in rehabilitation engineering. This advance is due to their...
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High dimensional feature data reduction of multichannel sEMG for gesture recognition based on double phases PSO
Surface electromyography (sEMG) is a kind of valuable bioelectric signal and very potential in the field of human–machine interaction. Ideal...
