Using forearm circumference for automatic threshold calibration for simple EMG control

Cannan, James; Hu, Huosheng

doi:10.1109/aim.2013.6584303

Cited by 2 publications

(1 citation statement)

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“…To solve this problem and obtain a robust and simple multi-user interface, an automated user calibration method is highly desirable. Cannan and Hu try to partially solve this problem by establishing a linear relationship between the Maximum Voluntary Contraction (MVC) and the upper forearm circumference [52]. MVC is defined as the maximum ability to contract muscles, i.e., the maximum attainable EMG signal.…”

Section: A Signal Processing and Feature Selectionmentioning

confidence: 99%

A Review on Electromyography Decoding and Pattern Recognition for Human-Machine Interaction

et al. 2019

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This paper presents a literature review on pattern recognition of electromyography (EMG) signals and its applications. The EMG technology is introduced and the most relevant aspects for the design of an EMG-based system are highlighted, including signal acquisition and filtering. EMG-based systems have been used with relative success to control upper-and lower-limb prostheses, electronic devices and machines, and for monitoring human behavior. Nevertheless, the existing systems are still inadequate and are often abandoned by their users, prompting for further research. Besides controlling prostheses, EMG technology is also beneficial for the development of machine learning-based devices that can capture the intention of able-bodied users by detecting their gestures, opening the way for new human-machine interaction (HMI) modalities. This paper also reviews the current feature extraction techniques, including signal processing and data dimensionality reduction. Novel classification methods and approaches for detecting non-trained gestures are discussed. Finally, current applications are reviewed, through the comparison of different EMG systems and discussion of their advantages and drawbacks.INDEX TERMS EMG, human-machine interaction, pattern classification, regression.

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Section: A Signal Processing and Feature Selectionmentioning

confidence: 99%