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A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
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Enhanced spectro-temporal feature extraction for prosthetic control using variational mode decomposition.

Uzma Shafiq1, Asim Waris2, Faisal Amin1

  • 1Department of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan.

Scientific Reports
|November 22, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel spectro-temporal feature set to improve rehabilitation systems for amputees by reducing noise sensitivity. The new method achieved high accuracy in distinguishing gestures, enhancing prosthetic control.

Keywords:
ElectromyographyPattern recognitionRehabilitationSpectro-temporalVariational mode decomposition

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Rehabilitation Technology

Background:

  • Current rehabilitation systems struggle with noise sensitivity and poor gesture recognition.
  • Existing pattern recognition techniques have limited application for amputees.
  • Feature extraction is crucial for distinguishing similar gestures in prosthetic control.

Purpose of the Study:

  • To propose a new spectro-temporal feature set for enhanced rehabilitation system implementation.
  • To improve the accuracy and robustness of prosthetic control systems.
  • To address the limitations of existing noise-sensitive techniques.

Main Methods:

  • Utilized Variational Mode Decomposition (VMD) to decompose electromyography (EMG) signals into variational mode functions (VMFs).
  • Applied Singular Value Decomposition (SVD) to obtain singular value representations from each VMF.
  • Compared the proposed spectro-temporal features against time-domain and other spectro-temporal feature sets.

Main Results:

  • The proposed feature set achieved 97% accuracy on Dataset I (healthy subjects, 4 hand/wrist motions).
  • Demonstrated high generalizability across datasets (Ninapro DB2, DB3, DB9) with accuracies of 91.43%, 95.66%, and 98.16% respectively.
  • Identified 6 VMFs as an optimal number for balancing accuracy and execution time.

Conclusions:

  • The novel spectro-temporal feature set significantly enhances the performance of rehabilitation systems.
  • The proposed technique offers improved noise resilience and generalizability for both healthy and amputee users.
  • This advancement holds promise for more intuitive and effective prosthetic control.