Application of a sEMG hand motion recognition method based on variational mode decomposition and ReliefF algorithm in rehabilitation medicine.

ABSTRACT: Hand motion intention recognition has been considered as one of the crucial research fields for prosthetic control and rehabilitation medicine. In recent years, surface electromyogram (sEMG) signals that directly reflect human motion information are ideal input sources for prosthetic control and rehabilitation. However, how to effectively extract components from sEMG signals containing abundant limb movement information to improve the accuracy of hand recognition still is a difficult problem. To achieve this goal, this paper proposes a novel hand motion recognition method based on variational mode decomposition (VMD) and ReliefF. First, VMD is used to decompose the sEMG signal into multiple variational mode functions (VMFs). To efficiently extract the intrinsic components of the sEMG, the

SUBMITTER: Yuan Y 

PROVIDER: S-EPMC11602058 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature