1. Empirical Myoelectric Feature Extraction and Pattern Recognition in Hemiplegic Distal Movement Decoding.
- Author
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Anastasiev, Alexey, Kadone, Hideki, Marushima, Aiki, Watanabe, Hiroki, Zaboronok, Alexander, Watanabe, Shinya, Matsumura, Akira, Suzuki, Kenji, Matsumaru, Yuji, and Ishikawa, Eiichi
- Subjects
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PATTERN recognition systems , *FEATURE extraction , *SUPERVISED learning , *FEATURE selection , *MYOELECTRIC prosthesis , *STROKE - 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]
- Published
- 2023
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