A hybrid approach to EMG pattern analysis for classification of arm movements using statistical and fuzzy techniques

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 parame...

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Bibliographic Details
Published inMedical engineering & physics Vol. 21; no. 5; pp. 303 - 311
Main Authors Micera, Silvestro, Sabatini, Angelo M., Dario, Paolo, Rossi, Bruno
Format Journal Article
LanguageEnglish
Published Oxford Elsevier Ltd 01.06.1999
Elsevier Science
Subjects
Adult
Algorithms
Arm - physiology
Biological and medical sciences
Data Interpretation, Statistical
Electrodiagnosis. Electric activity recording
Electromyography
EMG signal
Fuzzy Logic
Fuzzy sets
Humans
Investigative techniques, diagnostic techniques (general aspects)
Male
Medical sciences
Miscellaneous. Technology
Movement
Muscle
Muscle Contraction
Muscle, Skeletal - physiology
Regression analysis
Signal Processing, Computer-Assisted
Space life sciences
Statistical signal processing
Statistical tests
Upper limb movements
Fuzzy logic
EMG signal
Statistical signal processing
Upper limb movements
Human
Statistical method
Electrodiagnosis
Classification
Body movement
Lower limb
Electromyography
Pattern analysis
Signal analysis
Hybrid system
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Summary: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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ISSN:1350-4533
1873-4030
DOI:10.1016/S1350-4533(99)00055-7