[en] Rehabilitation systems play a vital role in improving the lifestyle of amputees or individuals with congenitally deficient limbs. However, the currently used pattern recognition techniques for different rehabilitation systems are highly noise sensitive. Inadequate feature extraction results in difficulty distinguishing similar gestures. Moreover, the implementation of previously proposed techniques on amputees is minimal. To address the issue of high sensitivity to noise, this study proposes a new spectro-temporal feature set for the enhanced implementation of rehabilitation systems. The efficacy of the proposed feature extraction technique was evaluated by comparing it with four other features aimed at addressing the broader applicability aspect. The Variational Mode Decomposition (VMD) of electromyography signals decomposes the signal into multiple variational mode functions (VMFs). A singular value representation is obtained from each VMF with a distinct spectral band using Singular Value Decomposition (SVD). The feature vectors employed consisted of a time-domain feature set, two spectro-temporal feature sets utilizing VMD and Empirical Mode Decomposition (EMD), and two combined feature sets. The results indicated that the proposed feature extraction technique outperforms the remaining feature sets with an accuracy of 97% for Dataset I, which comprises EMG recordings from 10 healthy subjects performing four hand and wrist motions. Statistical analysis revealed overall significance among all feature vectors (p-value < 0.05). The generalizability of the proposed technique was evaluated using Ninapro DB2 for healthy subjects with 49 motions, DB3 for amputees with 52 motions, and DB8 for both healthy and amputee subjects with 9 motions. The accuracies obtained for DB2, DB3, and DB9 were 91.43%, 95.66%, and 98.16% respectively. The analysis for the optimum number of VMFs revealed that 6 VMFs provide a reasonable tradeoff between accuracy and execution time.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Shafiq, Uzma; Department of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan
Waris, Asim; Department of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan. asim.waris@smme.nust.edu.pk
Amin, Faisal; Department of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan
Ullah, Ajdar; Department of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan
Ashraf, Hassan ; Université de Liège - ULiège > Aérospatiale et Mécanique (A&M)
Khan, Muhammad Jawad; Department of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan
Ijaz, Muhammad Adeel; Department of Biomedical Engineering and Sciences, School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan
Hazzazi, Fawwaz; Department of Electrical Engineering, College of Engineering, Prince Sattam bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
Ansari, Khalid; Faculty of Medical and Health Sciences, Liwa University, Abu Dhabi, United Arab Emirates
Gilani, Syed Omer; Department of Electrical, Computer, and Biomedical Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates
Language :
English
Title :
Enhanced spectro-temporal feature extraction for prosthetic control using variational mode decomposition.
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