AI Medical Compendium Topic:
Electromyography

Clear Filters Showing 561 to 570 of 643 articles

Multi-feature gait recognition with DNN based on sEMG signals.

Mathematical biosciences and engineering : MBE
This study proposed a gait recognition method based on the deep neural network of surface electromyography (sEMG) signals to improve the stability and accuracy of gait recognition using sEMG signals of the lower limbs. First, we determined the parame...

Robotic-assisted locomotor treadmill therapy does not change gait pattern in children with cerebral palsy.

International journal of rehabilitation research. Internationale Zeitschrift fur Rehabilitationsforschung. Revue internationale de recherches de readaptation
Although robotic-assisted locomotor treadmill therapy is utilized on children with cerebral palsy (CP), its impact on the gait pattern in childhood is not fully described. We investigated the outcome of robotized gait training focusing on the gait pa...

Regression of Hand Movements from sEMG Data with Recurrent Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Most wearable human-machine interfaces concerning hand movements only focus on classifying a limited number of hand gestures. With the introduction of deep learning, surface electromyography based hand gesture classification systems improved drastica...

Dexterous Force Estimation during Finger Flexion and Extension Using Motor Unit Discharge Information.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
With the development of advanced robotic hands, a reliable neural-machine interface is essential to take full advantage of the functional dexterity of the robots. In this preliminary study, we developed a novel method to estimate isometric forces of ...

Real-time finger force prediction via parallel convolutional neural networks: a preliminary study.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Continuous and accurate decoding of intended motions is critical for human-machine interactions. Here, we developed a novel approach for real-time continuous prediction of forces in individual fingers using parallel convolutional neural networks (CNN...

S-Convnet: A Shallow Convolutional Neural Network Architecture for Neuromuscular Activity Recognition Using Instantaneous High-Density Surface EMG Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The recent progress in recognizing low-resolution instantaneous high-density surface electromyography (HD-sEMG) images opens up new avenues for the development of more fluid and natural muscle-computer interfaces. However, the existing approaches emp...

Pose Estimation from Electromyographical Data using Convolutional Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This work demonstrates the effectiveness of Convolutional Neural Networks in the task of pose estimation from Electromyographical (EMG) data. The Ninapro DB5 dataset was used to train the model to predict the hand pose from EMG data. The models predi...

A normalisation approach improves the performance of inter-subject sEMG-based hand gesture recognition with a ConvNet.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Recently, the subject-specific surface electromyography (sEMG)-based gesture classification with deep learning algorithms has been widely researched. However, it is not practical to obtain the training data by requiring a user to perform hand gesture...

An Improved Performance of Deep Learning Based on Convolution Neural Network to Classify the Hand Motion by Evaluating Hyper Parameter.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
High accuracy in pattern recognition based on electromyography(EMG) contributes to the effectiveness of prosthetics hand development. This study aimed to improve performance and simplify the deep learning pre-processing based on the convolution neura...