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

2014 ◽  
pp. 321-353 ◽  
Author(s):  
Angkoon Phinyomark ◽  
Franck Quaine ◽  
Yann Laurillau
Keyword(s):  
Pattern Recognition ◽  
Classification Accuracy ◽  
Recognition System ◽  
Surface Emg ◽  
High Accuracy ◽  
Computer Interfaces ◽  
Emg Pattern Recognition ◽  
Body Compositions ◽  
The Relationship ◽  
Over Time

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.

scholarly journals The Relationship Between Anthropometric Variables and Features of Electromyography Signal for Human–Computer Interface

2018 ◽  
pp. 2234-2268
Author(s):  
Angkoon Phinyomark ◽  
Franck Quaine ◽  
Yann Laurillau
Keyword(s):  
Pattern Recognition ◽  
Classification Accuracy ◽  
Recognition System ◽  
Surface Emg ◽  
High Accuracy ◽  
Computer Interfaces ◽  
Emg Pattern Recognition ◽  
Body Compositions ◽  
The Relationship ◽  
Over Time

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.

EMG Pattern Recognition System Based on Neural Networks

2012 ◽  
Author(s):  
Juan Carlos Gonzalez-Ibarra ◽  
Carlos Soubervielle-Montalvo ◽  
Omar Vital-Ochoa ◽  
Hector Gerardo Perez-Gonzalez
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Recognition System ◽  
Pattern Recognition System ◽  
Emg Pattern Recognition

scholarly journals Classification of Simultaneous Movements Using Surface EMG Pattern Recognition

2013 ◽  
Vol 60 (5) ◽  
pp. 1250-1258 ◽  
Author(s):  
A. J. Young ◽  
L. H. Smith ◽  
E. J. Rouse ◽  
L. J. Hargrove
Keyword(s):  
Pattern Recognition ◽  
Surface Emg ◽  
Emg Pattern Recognition

Self-Correcting Pattern Recognition System of Surface EMG Signals for Upper Limb Prosthesis Control

2014 ◽  
Vol 61 (4) ◽  
pp. 1167-1176 ◽  
Author(s):  
Sebastian Amsuss ◽  
Peter M. Goebel ◽  
Ning Jiang ◽  
Bernhard Graimann ◽  
Liliana Paredes ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Upper Limb ◽  
Recognition System ◽  
Surface Emg ◽  
Pattern Recognition System ◽  
Upper Limb Prosthesis ◽  
Prosthesis Control ◽  
Limb Prosthesis

How do healthcare practitioners read electrocardiograms? A dual-process model of electrocardiogram interpretation

2019 ◽  
Vol 14 (10) ◽  
pp. 1-19
Author(s):  
Alan Davies ◽  
Julia Mueller ◽  
Laura Horseman ◽  
Bruno Splendiani ◽  
Elspeth Hill ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Process Model ◽  
Recognition System ◽  
Dual Process ◽  
Structured Interviews ◽  
Medical Practitioners ◽  
Process System ◽  
Electrocardiogram Interpretation ◽  
Inductive Thematic Analysis ◽  
Over Time

Background/Aims: This article aims to improve the understanding of the applied cognitive processes when interpreting electrocardiograms in clinical practice. It will do this by examining the self-reported approach practitioners take to interpret any barriers they encounter. Methods: This was a qualitative study in which medical practitioners, who routinely interpret electrocardiograms (n=31), were interviewed. The semi-structured interviews covered: their experience of interpretation; use of a system; pitfalls; changes to approach over time. An inductive thematic analysis was used to identify commonly occurring themes. A further set of practitioners (n=31), completed surveys that concerned their approach to an interpretation and use of interpretation frameworks/systems. Results: Practitioners find it easier to interpret electrocardiograms as they gain experience, but the process remains difficult. Barriers to successful interpretation include artefacts altering the waveform, lack of familiarity with the presenting condition, stress/panic at the prospect of making an inaccurate judgement, and overconfidence in one's interpretation abilities. Conclusions: The results support a dual-process system model that is developed with experience and enhances performance. Over time, experienced practitioners become able to move fluidly between a more formal systematic method and an experience-driven pattern recognition system. Potential errors that may arise from a reliance on pattern recognition (e.g. missing details) can be mitigated by using a systematic approach.

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