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Surface electromyogram (sEMG) is arguably the most sought-after physiologicalsignal with a broad spectrum of biomedical applications, especially inminiaturized rehabilitation robots such as multifunctional prostheses. Thewidespread use of sEMG to drive pattern recognition (PR)-based control schemesis primarily due to its rich motor information content and non-invasiveness.Moreover, sEMG recordings exhibit non-linear and non-uniformity properties withinevitable interferences that distort intrinsic characteristics of the signal,precluding existing signal processing methods from yielding requisite motorcontrol information. Therefore, we propose a multiresolution decompositiondriven by dual-polynomial interpolation (MRDPI) technique for adequatedenoising and reconstruction of multi-class EMG signals to guarantee thedual-advantage of enhanced signal quality and motor information preservation.Parameters for optimal MRDPI configuration were constructed across combinationsof thresholding estimation schemes and signal resolution levels using EMGdatasets of amputees who performed up to 22 predefined upper-limb motionsacquired in-house and from the public NinaPro database. Experimental resultsshowed that the proposed method yielded signals that led to consistent andsignificantly better decoding performance for all metrics compared to existingmethods across features, classifiers, and datasets, offering a potentialsolution for practical deployment of intuitive EMG-PR-based control schemes formultifunctional prostheses and other miniaturized rehabilitation roboticsystems that utilize myoelectric signals as control inputs.

Multiresolution Dual-Polynomial Decomposition Approach for Optimized Characterization of Motor Intent in Myoelectric Control Systems