ArticleScientific reports2025
Enhanced spectro-temporal feature extraction for prosthetic control using variational mode decomposition.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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.
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