Surface electromyogram-based finger motion classification has shown its potential for prosthetic control. However, most current finger motion classification models are subject-specific, requiring calibration when applied to new subjects. Generalized subject-nonspecific models are essential for real-world applications. In this study, the authors developed a subject-nonspecific model based on motor unit (MU) voting. A high-density surface electromyogram was first decomposed into individual MUs. The features extracted from each MU were then fed into a random forest classifier to obtain the finger label (primary prediction). The final prediction was selected by voting for all primary predictions provided by the decomposed MUs. Experiments conducted on 14 subjects demonstrated that our method significantly outperformed traditional methods in the context of subject-nonspecific finger motion classification models.

基于表面肌电图的手指运动分类已显示出其用于假肢控制的潜力。然而，大多数当前的手指运动分类模型都是特定于主题的，在应用于新主题时需要进行校准。广义主题非特定模型对于现实世界的应用至关重要。在这项研究中，作者开发了一个基于运动单位 (MU) 投票的主题非特定模型。首先将高密度表面肌电图分解为单个 MU。然后将从每个 MU 中提取的特征输入随机森林分类器以获得手指标签（初级预测）。最终预测是通过对分解的 MU 提供的所有主要预测进行投票来选择的。