AIMC Topic: Electromyography
Clear Filters Showing 311 to 320 of 659 articles
Adaptive robot mediated upper limb training using electromyogram-based muscle fatigue indicators.
PloS one
May 29, 2020
Studies on improving the adaptability of upper limb rehabilitation training do not often consider the implications of muscle fatigue sufficiently. In this study, electromyogram features were used as fatigue indicators in the context of human-robot in...
Pilot study: can machine learning analyses of movement discriminate between leg movements in sleep (LMS) with vs. without cortical arousals?
Sleep & breathing = Schlaf & Atmung
May 26, 2020
PURPOSE: Clinical and animal studies indicate frequent small micro-arousals (McA) fragment sleep leading to health complications. McA in humans is defined by changes in EEG and EMG during sleep. Complex EEG recordings during the night are usually req...
Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states.
NeuroImage
May 18, 2020
Predicting biomedical outcomes from Magnetoencephalography and Electroencephalography (M/EEG) is central to applications like decoding, brain-computer-interfaces (BCI) or biomarker development and is facilitated by supervised machine learning. Yet, m...
Measurement and identification of mental workload during simulated computer tasks with multimodal methods and machine learning.
Ergonomics
May 7, 2020
This study attempted to multimodally measure mental workload and validate indicators for estimating mental workload. A simulated computer work composed of mental arithmetic tasks with different levels of difficulty was designed and used in the experi...
Parkinson's Disease EMG Data Augmentation and Simulation with DCGANs and Style Transfer.
Sensors (Basel, Switzerland)
May 3, 2020
This paper proposes two new data augmentation approaches based on Deep Convolutional Generative Adversarial Networks (DCGANs) and Style Transfer for augmenting Parkinson's Disease (PD) electromyography (EMG) signals. The experimental results indicate...
Accurate recognition of lower limb ambulation mode based on surface electromyography and motion data using machine learning.
Computer methods and programs in biomedicine
Apr 29, 2020
Background and Objective The lower limb activity of recognition of the elderly, the weak, the disabled and the sick is an irreplaceable role in the caring of daily life. The main purpose of this study is to assess the feasibility of using the surface...
Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review.
Sensors (Basel, Switzerland)
Apr 27, 2020
Today, daily life is composed of many computing systems, therefore interacting with them in a natural way makes the communication process more comfortable. Human-Computer Interaction (HCI) has been developed to overcome the communication barriers bet...
Identification of Upper-Limb Movements Based on Muscle Shape Change Signals for Human-Robot Interaction.
Computational and mathematical methods in medicine
Apr 14, 2020
Towards providing efficient human-robot interaction, surface electromyogram (EMG) signals have been widely adopted for the identification of different limb movement intentions. Since the available EMG signal sensors are highly susceptible to external...
Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Apr 13, 2020
Understanding and classifying electromyogram (EMG) signals is of significance for dexterous prosthetic hand control, sign languages, grasp recognition, human-machine interaction, etc.. The existing research of EMG-based hand gesture classification fa...
Automatic detection of cortical arousals in sleep and their contribution to daytime sleepiness.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Apr 2, 2020
OBJECTIVE: Significant interscorer variability is found in manual scoring of arousals in polysomnographic recordings (PSGs). We propose a fully automatic method, the Multimodal Arousal Detector (MAD), for detecting arousals.
Stay Ahead of Medical AI
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 31059
- Neural Networks, Computer 29727
- Algorithms 26848
- Deep Learning 26366
- Artificial Intelligence 24311
- Robotics 13314
- Image Processing, Computer-Assisted 9527
- Retrospective Studies 9114
- Magnetic Resonance Imaging 6186
- Reproducibility of Results 5593
- Tomography, X-Ray Computed 4772
- Support Vector Machine 4688
- Computational Biology 4240
- Brain 4053
- Computer Simulation 3882
Recent Journals
- Scientific reports 5971
- Sensors (Basel, Switzerland) 4794
- PloS one 4022
- Neural networks : the official journal of the International Neural Network Society 2930
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1860
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1278
- IEEE journal of biomedical and health informatics 1131
- Journal of chemical information and modeling 1000
- Nature communications 949
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 861
- Briefings in bioinformatics 853
- IEEE transactions on neural networks and learning systems 826