Surface EMG signal classification using ternary pattern and discrete wavelet transform based feature extraction for hand movement recognition
Hands are two of the most crucial organs and they play major role for human activities.
Therefore, amputee people experience many difficulties in daily life. To overcome these …
Therefore, amputee people experience many difficulties in daily life. To overcome these …
Surface EMG signal classification using TQWT, Bagging and Boosting for hand movement recognition
Hands play a significant role in grasping and manipulating different objects. The loss of even
a single hand have impact on the human activity. In this regard, a prosthetic hand is an …
a single hand have impact on the human activity. In this regard, a prosthetic hand is an …
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 …
Application of min-max normalization on subject-invariant EMG pattern recognition
Surface electromyography (EMG) is one of the promising signals for the recognition of the
intended hand movement of an amputee. Nevertheless, there are several barriers to its …
intended hand movement of an amputee. Nevertheless, there are several barriers to its …
[HTML][HTML] A systematic review on surface electromyography-based classification system for identifying hand and finger movements
The developments in engineering fields have extended the use of electromyography (EMG)
beyond traditional diagnostic applications to multifarious areas like movement analysis …
beyond traditional diagnostic applications to multifarious areas like movement analysis …
Classification of electromyographic hand gesture signals using machine learning techniques
The electromyogram (EMG) signals from an individual's muscles can reflect the
biomechanics of human movement. The accurate classification of individual and combined …
biomechanics of human movement. The accurate classification of individual and combined …
Hand movement recognition from sEMG signals using Fourier decomposition method
Surface electromyogram (sEMG) provides a non-invasive way to collect EMG signals. The
sEMG signals acquired from the muscles of the forearm can be used to recognize the hand …
sEMG signals acquired from the muscles of the forearm can be used to recognize the hand …
[PDF][PDF] Classification of hand movements based on discrete wavelet transform and enhanced feature extraction
Extraction of potential electromyography (EMG) features has become one of the important
roles in EMG pattern recognition. In this paper, two EMG features, namely, enhanced …
roles in EMG pattern recognition. In this paper, two EMG features, namely, enhanced …
Hand gesture classification using time–frequency images and transfer learning based on CNN
Hand gesture-based systems are one of the most effective technological advances and
continue to develop with improvements in the field of human–computer interaction. Surface …
continue to develop with improvements in the field of human–computer interaction. Surface …
Hand gesture recognition based on surface electromyography using convolutional neural network with transfer learning method
X Chen, Y Li, R Hu, X Zhang… - IEEE Journal of …, 2020 - ieeexplore.ieee.org
This paper presents an effective transfer learning (TL) strategy for the realization of surface
electromyography (sEMG)-based gesture recognition with high generalization and low …
electromyography (sEMG)-based gesture recognition with high generalization and low …