EMG feature evaluation for improving myoelectric pattern recognition robustness
By Angkoon Phinyomark, Franck Quaine, Sylvie Charbonnier, Christine Servière, Franck Tarpin-Bernard, Yann Laurillau. Expert systems with applications, 2013
628 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/8212eb22fa5ec6a73cb70d100c39779255b4ebac
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
- Ales Holobar
- Rami N. Khushaba
- Erik J. Scheme
- Rosa H. M. Chan
- Xiaolong Zhai
- Chung Tin
- Beth Jelfs
- Ning Jiang
In Cognitive Neuroscience
- Ales Holobar
- Rami N. Khushaba
- Erik J. Scheme
- Rosa H. M. Chan
- Xiaolong Zhai
- Chung Tin
- Beth Jelfs
- Oskar C. Aszmann
Citations in the corpus
- Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning (2018, methodology): “4) SampEn Pipeline [36]: This last feature combination was selected among fifty features that were evaluated and ranked to find the most discriminating ones.”
- Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network (2017, background): “…has also been extensively researched in the past decade, with support vector machines (SVM; Ameri et al., 2014) and linear discriminant analysis (LDA, Chu et al., 2007; Linderman et al., 2009; Phinyomark et al., 2013) having emerged as the common choice for sEMG-based movement classification.”
- The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges (2014, background): “Other factors that reduce the practical accuracy of pattern recognition systems are the electrode shifts with doffing and donning of the prosthesis [31], [43], the changes in arm posture [32], [33], [44], the variability in EMG features over different days [63]–[66], and slow time-dependent changes,”
- Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors (2018, background): “The first sub-group consists of maximum fractal length (MFL), detrended fluctuation analysis (DFA) and sample entropy (SampEn) [24,40]; and the second sub-group comprises zero crossing (ZC), slope sign change (SSC), Willison amplitude (WAMP), median frequency (MDF), and mean frequency (MNF) [12].”