Myopotential pattern recognition to decode the intent of the user is the most advanced approach to controlling a powered bioprosthesis. Unfortunately, many factors make this a difficult problem and achieving acceptable recognition quality in real-word conditions is a serious challenge. The aim of the paper is to develop a recognition system that will mitigate factors related to multimodality and multichannel recording of biosignals and their high susceptibility to contamination. The proposed method involves the use of two co-operating multiclassifier systems. The first system is composed of one-class classifiers related to individual electromyographic (EMG) and mechanomyographic (MMG) biosignal recording channels, and its task is to recognise contaminated channels. The role of the second system is to recognise the class of movement resulting from the patient's intention. The ensemble system consists of base classifiers using the representation (extracted features) of biosignals from different channels. The system uses a dynamic selection mechanism, eliminating those base classifiers that are associated with biosignal channels that are recognised by the one-class ensemble system as being contaminated. Experimental studies were conducted using signals from an able-bodied person with simulation of amputation. The results obtained allow us to reject the null hypothesis that the application of the dual ensemble foes not lead to improved classification quality.

翻译：基于肌电位模式识别解码用户意图是控制动力型生物假肢的最先进方法。然而，多种因素使得该问题极为复杂，在真实场景中达到可接受的识别质量是一项严峻挑战。本文旨在开发一种识别系统，以缓解与生物信号多模态多通道记录及其高污染敏感性相关的干扰因素。所提方法采用两个协同工作的多分类器系统：第一个系统由与各肌电（EMG）及肌动信号（MMG）生物信号记录通道相关的单类分类器构成，其任务是识别受污染通道；第二个系统的作用是识别患者意图产生的运动类别。该集成系统由基于不同通道生物信号表征（提取特征）的基分类器组成，采用动态选择机制，剔除被单类集成系统判定为受污染生物信号通道所关联的基分类器。实验研究采用模拟截肢状况的健康受试者信号进行。所得结果允许我们拒绝“双集成系统应用不会提升分类质量”的原假设。