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Surface electromyography (sEMG) and accelerometer (Acc) signals play crucial roles
in controlling prosthetic and upper limb orthotic devices, as well as in assessing
electrical muscle activity for various biomedical engineering and rehabilitation
applications. In this study, an advanced discrimination system is proposed for the
identification of seven distinct shoulder girdle motions, aimed at improving prosthesis
control. Feature extraction from Time-Dependent Power Spectrum Descriptors (TDPSD)
is employed to enhance motion recognition. Subsequently, the Spectral
Regression (SR) method is utilized to reduce the dimensionality of the extracted
features. A comparative analysis is conducted between the Linear Discriminant
Analysis (LDA) class
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