  Surface electromyogram (sEMG) is arguably the most sought-after physiological
signal with a broad spectrum of biomedical applications, especially in
miniaturized rehabilitation robots such as multifunctional prostheses. The
widespread use of sEMG to drive pattern recognition (PR)-based control schemes
is primarily due to its rich motor information content and non-invasiveness.
Moreover, sEMG recordings exhibit non-linear and non-uniformity properties with
inevitable interferences that distort intrinsic characteristics of the signal,
precluding existing signal processing methods from yielding requisite motor
control information. Therefore, we propose a multiresolution decomposition
driven by dual-polynomial interpolation (MRDPI) technique for adequate
denoising and reconstruction of multi-class EMG signals to guarantee the
dual-advantage of enhanced signal quality and motor information preservation.
Parameters for optimal MRDPI configuration were constructed across combinations
of thresholding estimation schemes and signal resolution levels using EMG
datasets of amputees who performed up to 22 predefined upper-limb motions
acquired in-house and from the public NinaPro database. Experimental results
showed that the proposed method yielded signals that led to consistent and
significantly better decoding performance for all metrics compared to existing
methods across features, classifiers, and datasets, offering a potential
solution for practical deployment of intuitive EMG-PR-based control schemes for
multifunctional prostheses and other miniaturized rehabilitation robotic
systems that utilize myoelectric signals as control inputs.
