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 recognition performance of different numbers of VMFs is evaluated. Then, four features representing hand motion intentions are extracted from the VMFs to form the initial feature space. Next, the ReliefF algorithm is used to remove redundant features from the feature space. In order to select a feature space that can effectively reflect the intention of hand movements, the hand movement recognition performance of 8 low-dimensional feature spaces is evaluated. Finally, three machine learning methods are used to recognize hand movements. The proposed method was tested on the sEMG for Basic Hand movements Data Set and achieved an average accuracy of 99.14%. Compared with existing research, the proposed method achieves better hand motion recognition performance, indicating the potential for healthcare and rehabilitation applications.