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Empirical Myoelectric Feature Extraction and Pattern Recognition in Hemiplegic Distal Movement Decoding.
- Source :
- Bioengineering (Basel); Jul2023, Vol. 10 Issue 7, p866, 21p
- Publication Year :
- 2023
-
Abstract
- In myoelectrical pattern recognition (PR), the feature extraction methods for stroke-oriented applications are challenging and remain discordant due to a lack of hemiplegic data and limited knowledge of skeletomuscular function. Additionally, technical and clinical barriers create the need for robust, subject-independent feature generation while using supervised learning (SL). To the best of our knowledge, we are the first study to investigate the brute-force analysis of individual and combinational feature vectors for acute stroke gesture recognition using surface electromyography (EMG) of 19 patients. Moreover, post-brute-force singular vectors were concatenated via a Fibonacci-like spiral net ranking as a novel, broadly applicable concept for feature selection. This semi-brute-force navigated amalgamation in linkage (SNAiL) of EMG features revealed an explicit classification rate performance advantage of 10–17% compared to canonical feature sets, which can drastically extend PR capabilities in biosignal processing. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 23065354
- Volume :
- 10
- Issue :
- 7
- Database :
- Complementary Index
- Journal :
- Bioengineering (Basel)
- Publication Type :
- Academic Journal
- Accession number :
- 168588575
- Full Text :
- https://doi.org/10.3390/bioengineering10070866