  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.
