A hybrid approach to EMG pattern analysis for classification of arm movements using statistical and fuzzy techniques
✍ Scribed by Silvestro Micera; Angelo M. Sabatini; Paolo Dario; Bruno Rossi
- Publisher
- Elsevier Science
- Year
- 1999
- Tongue
- English
- Weight
- 921 KB
- Volume
- 21
- Category
- Article
- ISSN
- 1350-4533
No coin nor oath required. For personal study only.
✦ Synopsis
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.