Myoelectric signal processing for control of powered limb prostheses.

By Philip A. Parker, Kevin B. Englehart, B. Hudgins. Journal of Electromyography & Kinesiology, 2006

649 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cellular and Molecular Neuroscience

View Paper: https://www.semanticscholar.org/paper/6c6141387d36710e130828c226ca620e3ff3822c

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. Angkoon Phinyomark
2. Richard S. Sutton
3. Pornchai Phukpattaranont
4. Patrick M. Pilarski
5. Brenna D. Argall
6. J. V. van Dieën
7. Chusak Limsakul
8. Dorsa Sadigh

In Cellular and Molecular Neuroscience

1. Richard S. Sutton
2. Pornchai Phukpattaranont
3. Angkoon Phinyomark
4. A. Mahmood
5. Patrick M. Pilarski
6. Brenna D. Argall
7. Chusak Limsakul

Citations in the corpus

• True Online Temporal-Difference Learning (2015, background): “Interactions of this kind are known as myoelectric control (see, for example, Parker et al., 2006).”
• A Novel Feature Extraction for Robust EMG Pattern Recognition (2009, background): “Main application of these fields is the control of the prosthesis or other assistive devices using the different patterns of sEMG signal [1-2].”
• Methodological aspects of SEMG recordings for force estimation--a tutorial and review. (2010, background): “, 1998), prosthetics (Parker et al., 2006; Zecca et al., 2002), and biofeedback (Basmajian, 1988; Ince et al.”
• Autonomy in Rehabilitation Robotics: An Intersection (2018, positive): “A pattern-recognition paradigm is able to handle a larger number of control classes, using machine learning techniques to map the signals from an array of electrodes to a set of movement classes that typically each control one half of a single DoF (22, 32).”