School of Information Engineering, Shenyang University, Shenyang, Liaoning, China.
PLoS One. 2024 Nov 27;19(11):e0314611. doi: 10.1371/journal.pone.0314611. eCollection 2024.
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.
手部运动意图识别被认为是假肢控制和康复医学的关键研究领域之一。近年来，表面肌电 (sEMG) 信号直接反映了人体运动信息，是假肢控制和康复的理想输入源。然而，如何从包含丰富肢体运动信息的 sEMG 信号中有效地提取成分，以提高手部识别的准确性，仍然是一个难题。为了实现这一目标，本文提出了一种基于变分模态分解 (VMD) 和 ReliefF 的新型手部运动识别方法。首先，使用 VMD 将 sEMG 信号分解为多个变分模态函数 (VMF)。为了有效地提取 sEMG 的内在成分，评估了不同数量的 VMF 的识别性能。然后，从 VMF 中提取四个代表手部运动意图的特征，形成初始特征空间。接下来，使用 ReliefF 算法从特征空间中去除冗余特征。为了选择一个能够有效反映手部运动意图的特征空间，评估了 8 个低维特征空间的手部运动识别性能。最后，使用三种机器学习方法对手部运动进行识别。该方法在 Basic Hand movements Data Set 上进行了测试，平均准确率达到 99.14%。与现有研究相比，所提出的方法实现了更好的手部运动识别性能，表明其在医疗保健和康复应用方面具有潜力。
