Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb

By Mohammadreza Asghari Oskoei, Huosheng Hu. IEEE Transactions on Biomedical Engineering, 2008

811 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/3440c5ec7e8a7c441c47330ae338f7ddb294b053

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

1. Erik J. Scheme
2. Angkoon Phinyomark
3. Rami N. Khushaba
4. He (Helen) Huang
5. Levi J. Hargrove
6. Kevin B. Englehart
7. Zhi Dou
8. Fan Zhang

In Cellular and Molecular Neuroscience

1. Erik J. Scheme
2. He (Helen) Huang
3. Levi J. Hargrove
4. Angkoon Phinyomark
5. Rami N. Khushaba
6. Kevin B. Englehart
7. Zhi Dou
8. Fan Zhang

In Cognitive Neuroscience

1. Erik J. Scheme
2. Angkoon Phinyomark
3. Rami N. Khushaba
4. Levi J. Hargrove
5. He (Helen) Huang
6. Pornchai Phukpattaranont
7. Chusak Limsakul
8. Zhi Dou

Citations in the corpus

• EMG feature evaluation for improving myoelectric pattern recognition robustness (2013, methodology): “The success of this system to achieve a high classification performance depends almost entirely on the selection of EMG features (Oskoei & Hu, 2007, 2008).”
• A Novel Feature Extraction for Robust EMG Pattern Recognition (2009, methodology): “The second feature, WL is recommended by Oskoei and H. Hu [15] that it is the best single feature in their experiment.”
• Continuous Locomotion-Mode Identification for Prosthetic Legs Based on Neuromuscular–Mechanical Fusion (2011, background): “ter classification performance than LDA and artificial neural networks (ANN) in EMG pattern recognition for myoelectric prostheses, although no statistically significant differences were observed [20].”
• Nontechnical Loss Detection for Metered Customers in Power Utility Using Support Vector Machines (2010, methodology): “, , ), and that is the probability of the th class, the probability of a class can be derived via a QP problem [39]”
• Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors (2018, methodology): “It should be noted that SVMs were employed because, in the literature, this classifier has shown a better classification performance than other commonly used classifiers such as linear discriminant analysis (LDA) and artificial neural networks [34,46,47].”
• Feature reduction and selection for EMG signal classification (2012, background): “Modified mean absolute value type 2 (MAV2) is an expansion of MAV feature which is similar to the MAV1 (e.g. Oskoei & Hu, 2008; Phinyomark et al., 2009a).”
• Gesture recognition by instantaneous surface EMG images (2016, neutral): “…existing gesture recognition methods using sEMG are largely based on a conventional pattern recognition algorithms (such as support vector machine 12 , hidden Markov model 13 , etc. ) on sEMG feature space, i.e., the sequence of myoelectric signals of each channel often need to be transformed…”