A direct, ready-to-use surface electromyogram (sEMG) pattern classification algorithm that does not require prerequisite training, regardless of the user, is proposed herein. In addition to data collection, conventional supervised learning approaches for sEMG require labeling and segmenting the data and additional time for the learning algorithm. Consequently, these approaches cannot cope well with sEMG patterns during motion transitions of various movement speeds. The proposed unsupervised and self-adaptive method employs an iterative self-adaptive procedure realized by the probabilistic methods of diffusion, updating, and registration to cluster the activation patterns simultaneously in real time, and classify the current sEMG as new clustered patterns. Experiments demonstrated that even for the same motion, the proposed method could autonomously detect changes in muscular activation patterns varying with the speed of motion. Furthermore, some patterns of both steady- and transient-state motions could be distinguished. In addition, it was verified that the classified sEMG pattern could be correlated consistently with the actual motion, thereby realizing a high level of motion classification.

中文翻译：

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用于包括运动转换在内的 sEMG 模式识别的免训练贝叶斯自适应分类

本文提出了一种直接的、随时可用的表面肌电图 (sEMG) 模式分类算法，它不需要先决训练，无论用户如何。除了数据收集之外，sEMG 的传统监督学习方法需要标记和分割数据以及额外的学习算法时间。因此，这些方法不能很好地处理各种运动速度的运动过渡期间的 sEMG 模式。所提出的无监督自适应方法采用由扩散、更新和配准的概率方法实现的迭代自适应过程，实时同时对激活模式进行聚类，并将当前的 sEMG 分类为新的聚类模式。实验证明，即使是同样的运动，所提出的方法可以自主检测随运动速度变化的肌肉激活模式的变化。此外，可以区分稳态和瞬态运动的一些模式。此外，已验证分类的 sEMG 模式可以与实际运动一致，从而实现高水平的运动分类。