EMG-centered multisensory based technologies for pattern recognition in rehabilitation: state of the art and challenges
In the field of rehabilitation, the electromyography (EMG) signal plays an important role in
interpreting patients' intentions and physical conditions. Nevertheless, utilizing merely the …
interpreting patients' intentions and physical conditions. Nevertheless, utilizing merely the …
Deep learning for electromyographic hand gesture signal classification using transfer learning
In recent years, deep learning algorithms have become increasingly more prominent for
their unparalleled ability to automatically learn discriminant features from large amounts of …
their unparalleled ability to automatically learn discriminant features from large amounts of …
A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition
The surface electromyography (sEMG)-based gesture recognition with deep learning
approach plays an increasingly important role in human-computer interaction. Existing deep …
approach plays an increasingly important role in human-computer interaction. Existing deep …
Advancing muscle-computer interfaces with high-density electromyography
In this paper we present our results on using electromyographic (EMG) sensor arrays for
finger gesture recognition. Sensing muscle activity allows to capture finger motion without …
finger gesture recognition. Sensing muscle activity allows to capture finger motion without …
Recognizing hand and finger gestures with IMU based motion and EMG based muscle activity sensing
Session-and person-independent recognition of hand and finger gestures is of utmost
importance for the practicality of gesture based interfaces. In this paper we evaluate the …
importance for the practicality of gesture based interfaces. In this paper we evaluate the …
Subject-independent hand gesture recognition using normalization and machine learning algorithms
Hand gestures can be recognized using the upper limb's electromyography (EMG) that
measures the electrical activity of the skeletal muscles. However, generalization of muscle …
measures the electrical activity of the skeletal muscles. However, generalization of muscle …
Cooperative sensing and wearable computing for sequential hand gesture recognition
X Zhang, Z Yang, T Chen, D Chen… - IEEE Sensors …, 2019 - ieeexplore.ieee.org
Hand gestures recognition (HGR) has been considered as one of the crucial research fields
of human-computer interaction (HCI). Computer vision is a very active research field in the …
of human-computer interaction (HCI). Computer vision is a very active research field in the …
Gated recurrent neural networks for EMG-based hand gesture classification. A comparative study
A Samadani - 2018 40th annual international conference of the …, 2018 - ieeexplore.ieee.org
Electromyographic activities (EMG) generated during contraction of upper limb muscles can
be mapped to distinct hand gestures and movements, posing them as a promising modality …
be mapped to distinct hand gestures and movements, posing them as a promising modality …
Learning effective spatial–temporal features for sEMG armband-based gesture recognition
Surface electromyography (sEMG) armband-based gesture recognition is an active research
topic that aims to identify hand gestures with a single row of sEMG electrodes. As a typical …
topic that aims to identify hand gestures with a single row of sEMG electrodes. As a typical …
User-independent real-time hand gesture recognition based on surface electromyography
In this paper, we present a novel real-time hand gesture recognition system based on
surface electromyography. We employ a user-independent approach based on a support …
surface electromyography. We employ a user-independent approach based on a support …