A hybrid approach to EMG pattern analysis for classification of arm movements using statistical and fuzzy techniques
Abstract
In this paper, a hybrid approach is presented for discriminating a few upper limb movements by processing the electromyographic (EMG) signals from selected shoulder muscles. Statistical techniques, such as the Generalized Likelihood Ratio test, the Principal Component Analysis, autoregressive parametric modeling techniques and cepstral analysis techniques, combined with a fuzzy logic based classifier (the Abe-Lan network) are used to construct low-dimensional feature spaces with high classification rates. The experimental results show the ability of the algorithm to correctly classify all the EMG patterns related to the selected planar arm pointing movements. Moreover, the structure presented offers promise for real-time applications because of the low computation costs of the overall algorithm.
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The formal scheme for EMG acquisition and analysis for prosthetic control includes signal conditioning, pre-processing, onset detection, feature extraction, dimensionality reduction, pattern recognition, and off-line and on-line learning.
“the formal scheme for the acquisition and analysis of EMG signals for the control of prosthetic devices is composed of several modules (Zecca et al., 2002) : (1) signal conditioning, pre-processing, and detection of onset movement (Micera et al., 1999) ; (2) feature extraction; (3) dimensionality reduction; (4) pattern recognition; and (5) off-line and on-line learning.”
From A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems (2005) · cites this paper as Micera et al., 1999
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- Authors
- Micera, S., Sabatini, A. M., Dario, P., & Rossi, B.
- Affiliations
- Scuola Superiore Sant'Anna (IT)
- University of Pisa (IT)
- Corresponding author
- Sabatini, Angelo M.
- Venue
- Medical Engineering & Physics
- Publisher
- IOP Publishing
- Published
- 1999-06-01
- Volume
- 21
- Issue
- 5
- Pages
- 303-311
- ISSN
- 1350-4533 (ISSN-L)
- 1873-4030
- OpenAlex
- W2160968236
- Cited by
- 90
- References
- 38
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 3 · 2020: 2 · 2019: 3 · 2018: 2 · 2017: 6 · 2016: 3 · 2015: 3 · 2014: 5 · 2013: 3 · 2012: 7
- FWCI
- 1.07
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Medical Engineering & Physics (publishedVersion)
- PubMed (publishedVersion)
- CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- electromyography, upper limb movement, shoulder muscles, fuzzy logic classifier, generalized likelihood ratio test, principal component analysis, autoregressive models, cepstral analysis, low-dimensional feature extraction, real-time classification, myoelectric control
- MeSH terms
- Adult, Arm, Data Interpretation, Statistical, Electromyography, Humans, Male, Movement, Muscle Contraction, Signal Processing, Computer-Assisted, Fuzzy Logic, Muscle, Skeletal
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