#6284. Contrast of multi-resolution analysis approach to transhumeral phantom motion decoding

September 2026publication date
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Journal’s subject area:
Computer Networks and Communications;
Human-Computer Interaction;
Information Systems;
Computer Vision and Pattern Recognition;
Artificial Intelligence;
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Abstract:
In signal processing, multiresolution decomposition techniques allow for the separation of an acquired signal into sub levels, where the optimal level within the signal minimises redundancy, uncertainties, and contains the information required for the characterisation of the sensed phenomena. In the area of physiological signal processing for prosthesis control, scenarios where a signal decomposition analysis are required: the wavelet decomposition (WD) has been seen to be the favoured time-frequency approach for the decomposition of non-stationary signals. From a research perspective, the WD in certain cases has allowed for a more accurate motion intent decoding process following feature extraction and classification. Despite this, there is yet to be a widespread adaptation of the WD in a practical setting due to perceived computational complexity. Here, for neuromuscular (electromyography) and brainwave (electroencephalography) signals acquired from a transhumeral amputee, a computationally efficient time domain signal decomposition method based on a series of heuristics was applied to process the acquired signals before feature extraction.
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