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The Relationship between Anthropometric Variables and Features of Electromyography Signal for Human–Computer Interface

The Relationship between Anthropometric Variables and Features of Electromyography Signal for Human–Computer Interface

Angkoon Phinyomark, Franck Quaine, Yann Laurillau
Copyright: © 2014 |Pages: 33
ISBN13: 9781466660908|ISBN10: 1466660902|EISBN13: 9781466660915
DOI: 10.4018/978-1-4666-6090-8.ch015
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MLA

Phinyomark, Angkoon, et al. "The Relationship between Anthropometric Variables and Features of Electromyography Signal for Human–Computer Interface." Applications, Challenges, and Advancements in Electromyography Signal Processing, edited by Ganesh R. Naik, IGI Global, 2014, pp. 321-353. https://doi.org/10.4018/978-1-4666-6090-8.ch015

APA

Phinyomark, A., Quaine, F., & Laurillau, Y. (2014). The Relationship between Anthropometric Variables and Features of Electromyography Signal for Human–Computer Interface. In G. Naik (Ed.), Applications, Challenges, and Advancements in Electromyography Signal Processing (pp. 321-353). IGI Global. https://doi.org/10.4018/978-1-4666-6090-8.ch015

Chicago

Phinyomark, Angkoon, Franck Quaine, and Yann Laurillau. "The Relationship between Anthropometric Variables and Features of Electromyography Signal for Human–Computer Interface." In Applications, Challenges, and Advancements in Electromyography Signal Processing, edited by Ganesh R. Naik, 321-353. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-6090-8.ch015

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Abstract

Muscle-computer interfaces (MCIs) based on surface electromyography (EMG) pattern recognition have been developed based on two consecutive components: feature extraction and classification algorithms. Many features and classifiers are proposed and evaluated, which yield the high classification accuracy and the high number of discriminated motions under a single-session experimental condition. However, there are many limitations to use MCIs in the real-world contexts, such as the robustness over time, noise, or low-level EMG activities. Although the selection of the suitable robust features can solve such problems, EMG pattern recognition has to design and train for a particular individual user to reach high accuracy. Due to different body compositions across users, a feasibility to use anthropometric variables to calibrate EMG recognition system automatically/semi-automatically is proposed. This chapter presents the relationships between robust features extracted from actions associated with surface EMG signals and twelve related anthropometric variables. The strong and significant associations presented in this chapter could benefit a further design of the MCIs based on EMG pattern recognition.

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