The interpretation of surface electromyographic (sEMG) signals facilitates intuitive gesture recognition. However, sEMG signals are highly dependent on measurement conditions. The relationship between sEMG signals and gestures identified from a specific subject cannot be applied to other subjects owing to anatomical differences between the subjects. Furthermore, an sEMG signal varies even according to the electrode placement on the same subject. These limitations reduce the practicability of sEMG signal applications. This letter proposes a subject-independent gesture recognition method based on a muscle source activation model; a reference source model facilitates parameter transfer from a specific subject, i.e., donor to any subject, donee. The proposed method can compensate for the angular difference of the interface between subjects. A donee only needs to perform ulnar deviation for approximately 2s for the overall process. Ten subjects participated in the experiment, and the results show that, in the best configuration, the subject-independent classifier achieved a reasonable accuracy of 78.3% compared with the subject-specific classifier (88.7%) for four wrist/hand motions.

中文翻译：

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使用肌肉源激活模型进行独立于主体的 sEMG 模式识别

表面肌电 (sEMG) 信号的解释有助于直观的手势识别。然而，sEMG 信号高度依赖于测量条件。由于受试者之间的解剖学差异，从特定受试者识别出的 sEMG 信号和手势之间的关系不能应用于其他受试者。此外，即使在同一对象上放置电极，sEMG 信号也会发生变化。这些限制降低了 sEMG 信号应用的实用性。这封信提出了一种基于肌肉源激活模型的与主体无关的手势识别方法；参考源模型有助于从特定对象（即捐赠者）到任何对象、受赠者的参数转移。所提出的方法可以补偿主体之间界面的角度差异。受赠人整个过程只需要进行大约2s的尺偏。十名受试者参与了实验，结果表明，在最佳配置下，与受试者特定分类器 (88.7%) 相比，受试者独立分类器在四个手腕/手部运动中实现了 78.3% 的合理准确率。