Application of a sEMG hand motion recognition method based on variational mode decomposition and ReliefF algorithm in rehabilitation medicine
Yue Yuan · 2024
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Description
Yue Yuan reads intended hand movements from forearm muscle signals with a signal cleaning pipeline, aiming at prosthetic control and rehabilitation, in this 2024 paper. Surface electrodes pick up rich movement information but buried in noise. The method first splits each signal into layered components with a variational decomposition, keeping only the most telling layers.
Four features are drawn from those components, and a ranking algorithm prunes the redundant ones. Eight trimmed feature sets were compared, and three standard classifiers did the final recognition. On a public data set of basic hand movements, the pipeline averaged 99.14 percent accuracy, beating prior results the author cites. The paper presents the recipe as a practical front end for clinics and devices that must know what a hand means to do.
Catalog record
| CREATOR | Yue Yuan |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 45 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |