[HTML][HTML] Neuromechanical biomarkers for robotic neurorehabilitation
One of the current challenges for translational rehabilitation research is to develop the
strategies to deliver accurate evaluation, prediction, patient selection, and decision-making …
strategies to deliver accurate evaluation, prediction, patient selection, and decision-making …
DepHNN: a novel hybrid neural network for electroencephalogram (EEG)-based screening of depression
Depression is a psychological disorder characterized by the continuous occurrence of bad
mood state. It is critical to understand that this disorder is severely affecting people of …
mood state. It is critical to understand that this disorder is severely affecting people of …
AutoDep: automatic depression detection using facial expressions based on linear binary pattern descriptor
M Tadalagi, AM Joshi - Medical & biological engineering & computing, 2021 - Springer
The psychological health of a person plays an important role in their daily life activities. The
paper addresses depression issues with the machine learning model using facial …
paper addresses depression issues with the machine learning model using facial …
Advanced energy kernel-based feature extraction scheme for improved EMG-PR-based prosthesis control against force variation
S Pancholi, AM Joshi - IEEE Transactions on Cybernetics, 2020 - ieeexplore.ieee.org
The EMG signal is a widely focused, clinically viable, and reliable source for controlling
bionics and prosthesis devices with the aid of machine-learning algorithms. The decisive …
bionics and prosthesis devices with the aid of machine-learning algorithms. The decisive …
A multiscale feature extraction network based on channel-spatial attention for electromyographic signal classification
B Sun, B Song, J Lv, P Chen, X Sun… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The applications of myoelectrical interfaces are majorly limited by the efficacy of decoding
motion intent in the electromyographic (EMG) signal. Currently, EMG classification methods …
motion intent in the electromyographic (EMG) signal. Currently, EMG classification methods …
Detection of false data injection in smart grid using PCA based unsupervised learning
Advanced metering infrastructure (AMI) is one of the core aspects of the smart grid, and
offers numerous possible benefits, such as load control and demand response. AMI enables …
offers numerous possible benefits, such as load control and demand response. AMI enables …
Intelligent upper-limb prosthetic control (iULP) with novel feature extraction method for pattern recognition using EMG
S Pancholi, AM Joshi - Journal of Mechanics in Medicine and …, 2021 - World Scientific
EMG signal-based pattern recognition (EMG-PR) techniques have gained lots of focus to
develop myoelectric prosthesis. The performance of the prosthesis control-based …
develop myoelectric prosthesis. The performance of the prosthesis control-based …
DepML: An efficient machine learning-based MDD detection system in IoMT framework
This paper aims to propose an automated and less complex machine learning-based
depression detection system DepML utilizing the IoMT framework in smart hospitals. This …
depression detection system DepML utilizing the IoMT framework in smart hospitals. This …
Novel features extraction from eeg signals for epilepsy detection using machine learning model
Epilepsy is a neurological disorder that affects the brain, as well as the human body's nerves
and spinal cord, adversely causing unusual and uncontrollable behavior. This letter …
and spinal cord, adversely causing unusual and uncontrollable behavior. This letter …
Classification of EMG signals with CNN features and voting ensemble classifier
Electromyography (EMG) signals are primarily used to control prosthetic hands. Classifying
hand gestures efficiently with EMG signals presents numerous challenges. In addition to …
hand gestures efficiently with EMG signals presents numerous challenges. In addition to …