通过两级分类器检测肌电信号污染，提升假肢控制准确率
Cascade of one-class classifier ensemble and dynamic naive Bayes classifier applied to the myoelectric-based upper limb prosthesis control with contaminated channels detection
- 用一类分类器集成识别信号污染，再动态调整贝叶斯分类器
- 实验表明该方法显著提升假肢控制分类精度，有效应对信号干扰
- 适合肌电假肢研发人员及生物信号处理领域研究者参考
现代上肢肌电假肢通常基于表面肌电（sEMG）信号，采用模式识别进行控制。然而，sEMG信号极易受污染，导致控制系统性能下降，影响患者日常使用。本文提出一种新型识别系统，用于肌电控制假肢，并能检测污染的sEMG信号。其创新性在于两个识别系统以级联结构协同工作：(1) 一类分类器集成用于识别污染信号；(2) 动态朴素贝叶斯分类器（NBC）利用一类别判结果，识别用户意图。尽管NBC模型动态变化，但因分类函数为乘积形式，可实现单次训练完成。实验基于真实sEMG信号数据，结果验证了该方法能显著提升分类质量。
原文摘要 · Abstract (English)
Modern upper limb bioprostheses are typically controlled by sEMG signals using a pattern recognition scheme in the control process. Unfortunately, the sEMG signal is very susceptible to contamination that deteriorates the quality of the control system and reduces the usefulness of the prosthesis in the patient's everyday life. In the paper, the authors propose a new recognition system intended for sEMG-based control of the hand prosthesis with detection of contaminated sEMG signals. The originality of the proposed solution lies in the co-operation of two recognition systems working in a cascade structure: (1) an ensemble of one-class classifiers used to recognise contaminated signals and (2) a naive Bayes classifier (NBC) which recognises the patient's intentions using the information about contaminations produced by the ensemble. Although in the proposed approach, the NBC model is changed dynamically, due to the multiplicative form of the classification functions, training can be performed in a one-shot procedure. Experimental studies were conducted using real sEMG signals. The results obtained confirm the hypothesis that the use of the one-class classifier ensemble and the dynamic NBC model leads to improved classification quality.
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