A Real-Time EMG Pattern Recognition System Based on Linear-Nonlinear Feature Projection for a Multifunction Myoelectric Hand
By J. Chu, Inhyuk Moon, Museong Mun. IEEE Transactions on Biomedical Engineering, 2006
353 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Cognitive Neuroscience, Human-Computer Interaction
View Paper: https://www.semanticscholar.org/paper/fd4e42b2a9abae17eee51b45f3ab150533f41277
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
- Christian Fleischer
- Mamun Bin Ibne Reaz
- Tae Gyu Chang
- Ahmad Ashrif A. Bakar
- Günter Hommel
- Mohd. Alauddin Mohd. Ali
- Kalaivani Chellappan
- Rubana H. Chowdhury
In Cognitive Neuroscience
- Christian Fleischer
- Mamun Bin Ibne Reaz
- Tae Gyu Chang
- Ahmad Ashrif A. Bakar
- Günter Hommel
- Mohd. Alauddin Mohd. Ali
- Kalaivani Chellappan
- Rubana H. Chowdhury
Citations in the corpus
- A Human--Exoskeleton Interface Utilizing Electromyography (2008, background): “Upon recognition of certain EMG patterns, predefined trajectories are replayed, making it a feasible approach for hand prostheses control as, for example, in [21]–[23].”
- Autonomy in Rehabilitation Robotics: An Intersection (2018, neutral): “Therefore, a large body of pattern-recognition research focuses on advancing EMG decoding paradigms—evaluating a variety, even cascades (40), of machine learning algorithms (41, 42) and different input features for these algorithms (41, 42, 43), as well as exploring different output classes (44, 45,”
- Surface Electromyography Signal Processing and Classification Techniques (2013, methodology): “This method simplifies the structure of the classifier and provides greater classification performance compared to using only PCA [92].”