The developments in engineering fields have extended the use of electromyography (EMG)
beyond traditional diagnostic applications to multifarious areas like movement analysis …

Abstract Recently, Convolutional Neural Networks (CNNs) have been used for the
classification of hand activities from surface Electromyography (sEMG) signals. However …

Hand gestures are crucial for develo** prosthetic and rehabilitation devices, enabling
intuitive human–computer interaction (HCI) and improving accessibility for individuals with …

Currently, research on gesture recognition systems has been on the rise due to the
capabilities these systems provide to the field of human–machine interaction, however …

Myocardial infarction (MI) is a heart condition arising due to partial or complete blockage of
blood flow to heart muscles. This can lead to permanent damage to the heart and can be …

To improve the classification accuracy of hand movements from sEMG signals, this paper
puts forward a unified hand gesture classification framework which exploits the potentials of …

In the rapidly advancing field of biomedical engineering, effective real-time control of
artificial limbs is a pressing research concern. Addressing this, the current study introduces a …

Decoding human action intention prior to motion onset with surface electromyograms
(sEMG) is an emerging neuroengineering topic with interesting clinical applications such as …

Traditional signal processing methods cannot effectively segment the optimal frequency
band, resulting in the fault characteristics are not obvious. Therefore, this paper proposes a …

Healthy sleep is essential for the rejuvenation of the body and helps in maintaining good
health. Many people suffer from sleep disorders that are characterized by abnormal sleep …