Liu Jianwei, Sheng Xinjun, Zhang Dingguo, Jiang Ning, Zhu Xiangyang
IEEE Trans Neural Syst Rehabil Eng. 2016 Apr;24(4):444-54. doi: 10.1109/TNSRE.2015.2420654. Epub 2015 Apr 13.
In spite of several decades of intense research and development, the existing algorithms of myoelectric pattern recognition (MPR) are yet to satisfy the criteria that a practical upper extremity prostheses should fulfill. This study focuses on the criterion of the short, or even zero subject training. Due to the inherent nonstationarity in surface electromyography (sEMG) signals, current myoelectric control algorithms usually need to be retrained daily during a multiple days' usage. This study was conducted based on the hypothesis that there exist some invariant characteristics in the sEMG signals when a subject performs the same motion in different days. Therefore, given a set of classifiers (models) trained on several days, it is possible to find common characteristics among them. To this end, we proposed to use common model component analysis (CMCA) framework, in which an optimized projection was found to minimize the dissimilarity among multiple models of linear discriminant analysis (LDA) trained using data from different days. Five intact-limbed subjects and two transradial amputee subjects participated in an experiment including six sessions of sEMG data recording, which were performed in six different days, to simulate the application of MPR over multiple days. The results demonstrate that CMCA has a significant better generalization ability with unseen data (not included in the training data), leading to classification accuracy improvement and increase of completion rate in a motion test simulation, when comparing with the baseline reference method. The results indicate that CMCA holds a great potential in the effort of developing zero retraining of MPR.
尽管经过了几十年的深入研发，但现有的肌电模式识别（MPR）算法仍未满足实用上肢假肢应具备的标准。本研究聚焦于短时间甚至零受试者训练的标准。由于表面肌电图（sEMG）信号固有的非平稳性，当前的肌电控制算法在多天使用过程中通常每天都需要重新训练。本研究基于这样的假设进行：当受试者在不同日期执行相同动作时，sEMG信号中存在一些不变特征。因此，给定一组在几天内训练的分类器（模型），就有可能在它们之间找到共同特征。为此，我们提出使用通用模型成分分析（CMCA）框架，其中找到一个优化投影以最小化使用来自不同日期的数据训练的多个线性判别分析（LDA）模型之间的差异。五名肢体健全的受试者和两名经桡骨截肢受试者参与了一项实验，该实验包括六个阶段的sEMG数据记录，这些记录在六个不同日期进行，以模拟MPR在多天内的应用。结果表明，与基线参考方法相比，CMCA对于未见过的数据（未包含在训练数据中）具有显著更好的泛化能力，在运动测试模拟中导致分类准确率提高和完成率增加。结果表明，CMCA在开发零重新训练的MPR方面具有巨大潜力。
