Myoelectric pattern recognition is a useful tool for identifying the user’s intended motion. However, the inherent nonstationary properties of Electromyography (EMG) signals usually limited the use of real time commercial prostheses. These variations cause the degradation of myoelectric control performance and make it unstable over time, across subjects and sessions. In this study, this challenge is overcome by combining the use of robust spatial features and the supervised adaptive learning method to improve the myoelectric performance. Three types of spatial features are proposed based on histogram oriented gradient (HOG) algorithm and intensity features namely H, HI, and AIH features. H features correspond to extracting HOG features from the HD-sEMG map. HI feature is obtained by concatenating the H features with scalar intensity feature that calculated from HD-sEMG map. Finally, the hybrid AIH features are produced by combining the H features with the intensity features matrix (AI) that obtained from the segmented maps. Three sub-databases are used for evaluation. The proposal feature sets are compared with time-domain (TD) and a combination of intensity and center of gravity features (ICG) to show the powerful of these features. The offline results report the superiority of the classifier’s performance in term of precision and sensitivity based on AIH features than other feature sets (i.e. H, HI, TD, ICG) with improvement 4.1%, 3.5%, 2.24%, 5.3% and 6%, 5%, 2.2%, 6.9% respectively. The adaptive classifier based on AIH features outperforms adaptive myoelectric control based on other feature sets and the original version. The adaptive classifier utilized testing data that update the original dataset which in turn has a significant effect on improving the myoelectric performance in the presence of the variation of EMG signal properties.

中文翻译：

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基于HD-sEMG信号混合空间特征的自适应肌电模式识别

肌电模式识别是识别用户预期运动的有用工具。然而，肌电图 (EMG) 信号固有的非平稳特性通常限制了实时商业假肢的使用。这些变化会导致肌电控制性能下降，并使其随着时间的推移、跨学科和会话变得不稳定。在这项研究中，通过结合使用稳健的空间特征和有监督的自适应学习方法来提高肌电性能，克服了这一挑战。基于直方图定向梯度 (HOG) 算法和强度特征，即 H、HI 和 AIH 特征，提出了三种类型的空间特征。H 特征对应于从 HD-sEMG 图中提取 HOG 特征。HI 特征是通过将 H 特征与从 HD-sEMG 图计算的标量强度特征连接起来获得的。最后，混合 AIH 特征是通过将 H 特征与从分割图获得的强度特征矩阵（AI）相结合而产生的。三个子数据库用于评估。建议特征集与时域（TD）以及强度和重心特征（ICG）的组合进行比较，以展示这些特征的强大功能。离线结果报告了基于 AIH 特征的分类器在精度和灵敏度方面的性能优于其他特征集（即 H、HI、TD、ICG），分别提高了 4.1%、3.5%、2.24%、5.3% 和 6% 、5%、2.2%、6.9%。基于 AIH 特征的自适应分类器优于基于其他特征集和原始版本的自适应肌电控制。自适应分类器利用更新原始数据集的测试数据，这反过来对在存在 EMG 信号特性变化的情况下改善肌电性能有显着影响。