Al Taee Ahmed A, Khushaba Rami N, Al-Timemy Ali H, Al-Jumaily Adel
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:657-661. doi: 10.1109/EMBC44109.2020.9175602.
Controlling powered prostheses with myoelectric pattern recognition (PR) provides a natural human-robot interfacing scheme for amputees who lost their limbs. Research in this direction reveals that the challenges prohibiting reliable clinical translation of myoelectric interfaces are mainly driven by the quality of the extracted features. Hence, developing accurate and reliable feature extraction techniques is of vital importance for facilitating clinical implementation of Electromyogram (EMG) PR systems. To overcome this challenge, we proposed a combination of Range Spatial Filtering (RSF) and Recurrent Fusion of Time Domain Descriptors (RFTDD) in order to improve the classifier performance and make the prosthetic hand control more appropriate for clinical applications. RSF is used to increase the number of EMG signals available for feature extraction by focusing on the spatial information between all possible logical combinations of the physical EMG channels. RFTDD is then used to capture the temporal information by applying a recurrent data fusion process on the resulting orientation-based time-domain (TD) features, with a sigmoidal function to limit the features range and overcome the vanishing amplitudes problem. The main advantages of the proposed method include 1) its potential in capturing the temporal-spatial dependencies of the EMG signals, leading to reduced classification errors, and 2) the simplicity with which the features are extracted, as any kind of simple TD features can be adopted with this method. The performance of the proposed RFTDD is then benchmarked across many well-known TD features individually and as sets to prove the power of the RFTDD method on two EMG datasets with a total of 31 subjects. Testing results revealed an approximate reduction of 12% in classification errors across all subjects when using the proposed method against traditional feature extraction methods.Clinical Relevance-Establishing significance and importance of RFTDD, with simple time-domain features, for robust and low-cost clinical applications.
利用肌电模式识别（PR）控制动力假肢为肢体缺失的截肢者提供了一种自然的人机交互方案。这一方向的研究表明，阻碍肌电接口可靠临床应用的挑战主要源于所提取特征的质量。因此，开发准确可靠的特征提取技术对于推动肌电图（EMG）PR系统的临床应用至关重要。为了克服这一挑战，我们提出了范围空间滤波（RSF）和时域描述符循环融合（RFTDD）相结合的方法，以提高分类器性能，使假肢手控制更适合临床应用。RSF通过关注物理EMG通道所有可能逻辑组合之间的空间信息，用于增加可用于特征提取的EMG信号数量。然后，RFTDD通过对基于方向的时域（TD）特征应用循环数据融合过程来捕获时间信息，并使用 sigmoid 函数限制特征范围，克服幅度消失问题。所提方法的主要优点包括：1）能够捕获EMG信号的时空依赖性，从而减少分类误差；2）特征提取简单，因为该方法可以采用任何一种简单的TD特征。然后，在所提RFTDD的性能在许多著名的TD特征上分别和作为集合进行基准测试，以证明RFTDD方法在两个共有31名受试者的EMG数据集上的能力。测试结果表明，与传统特征提取方法相比，使用所提方法时所有受试者的分类误差大约降低了12%。临床相关性——确立了具有简单时域特征的RFTDD对于强大且低成本临床应用的意义和重要性。
