IEEE Trans Neural Syst Rehabil Eng. 2019 May;27(5):1071-1080. doi: 10.1109/TNSRE.2019.2911316. Epub 2019 Apr 16.
Traditional myoelectric prostheses that employ a static pattern recognition model to identify human movement intention from surface electromyography (sEMG) signals hardly adapt to the changes in the sEMG characteristics caused by interferences from daily activities, which hinders the clinical applications of such prostheses. In this paper, we focus on methods to reduce or eliminate the impacts of three types of daily interferences on myoelectric pattern recognition (MPR), i.e., outlier motion, muscle fatigue, and electrode doffing/donning. We constructed an adaptive incremental hybrid classifier (AIHC) by combining one-class support vector data description and multi-class linear discriminant analysis in conjunction with two specific update schemes. We developed an AIHC-based MPR strategy to improve the robustness of MPR against the three interferences. Extensive experiments on hand-motion recognition were conducted to demonstrate the performance of the proposed method. Experimental results show that the AIHC has significant advantages over non-adaptive classifiers under various interferences, with improvements in the classification accuracy ranging from 7.1% to 39% ( ). The additional evaluations on data deviations demonstrate that the AIHC can accommodate large-scale changes in the sEMG characteristics, revealing the potential of the AIHC-based MPR strategy in the development of clinical myoelectric prostheses.
传统的肌电假肢采用静态模式识别模型来识别人体运动意图从表面肌电图 (sEMG) 信号几乎适应 sEMG 特征的变化由日常活动的干扰引起的，这阻碍了这种假肢的临床应用。在本文中，我们专注于减少或消除三种类型的日常干扰对肌电模式识别 (MPR) 的影响的方法，即离群运动、肌肉疲劳和电极脱落/佩戴。我们通过结合单类支持向量数据描述和多类线性判别分析以及两种特定的更新方案构建了自适应增量混合分类器 (AIHC)。我们开发了一种基于 AIHC 的 MPR 策略，以提高 MPR 对三种干扰的鲁棒性。对手动运动识别进行了广泛的实验，以证明所提出方法的性能。实验结果表明，在各种干扰下，AIHC 明显优于非自适应分类器，分类准确率提高了 7.1%到 39% ( )。对数据偏差的额外评估表明，AIHC 可以适应 sEMG 特征的大规模变化，这揭示了基于 AIHC 的 MPR 策略在开发临床肌电假肢方面的潜力。
