Surface electromyography as a natural human–machine interface: a review
Surface electromyography (sEMG) is a non-invasive method of measuring neuromuscular
potentials generated when the brain instructs the body to perform both fine and coarse …
potentials generated when the brain instructs the body to perform both fine and coarse …
Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications
Soft electromechanical sensors have led to a new paradigm of electronic devices for novel
motion-based wearable applications in our daily lives. However, the vast amount of random …
motion-based wearable applications in our daily lives. However, the vast amount of random …
Hand gesture classification using time–frequency images and transfer learning based on CNN
Hand gesture-based systems are one of the most effective technological advances and
continue to develop with improvements in the field of human–computer interaction. Surface …
continue to develop with improvements in the field of human–computer interaction. Surface …
Convolutional neural network for drowsiness detection using EEG signals
S Chaabene, B Bouaziz, A Boudaya, A Hökelmann… - Sensors, 2021 - mdpi.com
Drowsiness detection (DD) has become a relevant area of active research in biomedical
signal processing. Recently, various deep learning (DL) researches based on the EEG …
signal processing. Recently, various deep learning (DL) researches based on the EEG …
Empower dexterous robotic hand for human-centric smart manufacturing: A perception and skill learning perspective
Recent rapid developments of dexterous robotic hands have greatly enhanced the
manipulative capabilities of robots, enabling them to perform industrial tasks in human-like …
manipulative capabilities of robots, enabling them to perform industrial tasks in human-like …
EMG-Based Hand gesture recognition for myoelectric prosthetic hand control
The work presented in this paper aims to contribute to the development of a deep learning-
based approach to recognize hand movements using surface electromyography signals …
based approach to recognize hand movements using surface electromyography signals …
Enhancing sEMG-Based Finger Motion Prediction with CNN-LSTM Regressors for Controlling a Hand Exoskeleton
In recent years, the number of people with disabilities has increased hugely, especially in
low-and middle-income countries. At the same time, robotics has made significant advances …
low-and middle-income countries. At the same time, robotics has made significant advances …
Comprehensive Review of Feature Extraction Techniques for sEMG Signal Classification: From Handcrafted Features to Deep Learning Approaches
SM Sid'El Moctar, I Rida, S Boudaoud - IRBM, 2024 - Elsevier
Surface Electromyography (sEMG) has become an essential tool in various fields, including
prosthetic control and clinical evaluation of the neuromusculoskeletal system. In recent …
prosthetic control and clinical evaluation of the neuromusculoskeletal system. In recent …
Comparing Teaching Strategies of a Machine Learning-based Prosthetic Arm
Pattern-recognition-based arm prostheses rely on recognizing muscle activation to trigger
movements. The effectiveness of this approach depends not only on the performance of the …
movements. The effectiveness of this approach depends not only on the performance of the …
Silent speech recognition based on surface electromyography using a few electrode sites under the guidance from high-density electrode arrays
Although surface electromyogram (sEMG) recorded from high-density electrode array is
believed to carry sufficient spatial information that can benefit the decoding of motor …
believed to carry sufficient spatial information that can benefit the decoding of motor …