A review on electromyography decoding and pattern recognition for human-machine interaction
This paper presents a literature review on pattern recognition of electromyography (EMG)
signals and its applications. The EMG technology is introduced and the most relevant …
signals and its applications. The EMG technology is introduced and the most relevant …
Machine learning approach to detect cardiac arrhythmias in ECG signals: A survey
Cardiac arrhythmia is a condition when the heart rate is irregular either the beat is too slow
or too fast. It occurs due to improper electrical impulses that coordinates the heart beats …
or too fast. It occurs due to improper electrical impulses that coordinates the heart beats …
Automated ASD detection using hybrid deep lightweight features extracted from EEG signals
Background Autism spectrum disorder is a common group of conditions affecting about one
in 54 children. Electroencephalogram (EEG) signals from children with autism have a …
in 54 children. Electroencephalogram (EEG) signals from children with autism have a …
Selective encryption on ECG data in body sensor network based on supervised machine learning
Abstract Body Sensor Networks (BSNs) are develo** rapidly in recent years as it
combines the Internet-of-Things (IoT) and data analytic techniques for building a remote …
combines the Internet-of-Things (IoT) and data analytic techniques for building a remote …
Continuous and simultaneous estimation of lower limb multi-joint angles from sEMG signals based on stacked convolutional and LSTM models
The smooth and natural interaction between human and lower limb exoskeleton is
important. However, one of the challenges is that obtaining the joint rotation angles in time …
important. However, one of the challenges is that obtaining the joint rotation angles in time …
A review of algorithm & hardware design for AI-based biomedical applications
Y Wei, J Zhou, Y Wang, Y Liu, Q Liu… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
This paper reviews the state of the arts and trends of the AI-Based biomedical processing
algorithms and hardware. The algorithms and hardware for different biomedical applications …
algorithms and hardware. The algorithms and hardware for different biomedical applications …
Machine learning in electromagnetics with applications to biomedical imaging: A review
Biomedical imaging is a relevant noninvasive technique aimed at generating an image of
the biological structure under analysis. The arising visual representation of the …
the biological structure under analysis. The arising visual representation of the …
sEMG-based identification of hand motion commands using wavelet neural network combined with discrete wavelet transform
F Duan, L Dai, W Chang, Z Chen… - IEEE Transactions on …, 2015 - ieeexplore.ieee.org
Surface electromyogram (sEMG) signals can be applied in medical, rehabilitation, robotic,
and industrial fields. As a typical application, a myoelectric prosthetic hand is controlled by …
and industrial fields. As a typical application, a myoelectric prosthetic hand is controlled by …
Comparison of bagging and boosting ensemble machine learning methods for automated EMG signal classification
The neuromuscular disorders are diagnosed using electromyographic (EMG) signals.
Machine learning algorithms are employed as a decision support system to diagnose …
Machine learning algorithms are employed as a decision support system to diagnose …
Single-channel EMG classification with ensemble-empirical-mode-decomposition-based ICA for diagnosing neuromuscular disorders
An accurate and computationally efficient quantitative analysis of electromyography (EMG)
signals plays an inevitable role in the diagnosis of neuromuscular disorders, prosthesis, and …
signals plays an inevitable role in the diagnosis of neuromuscular disorders, prosthesis, and …