IEEE Trans Cybern. 2022 May;52(5):3819-3828. doi: 10.1109/TCYB.2020.3016595. Epub 2022 May 19.
The EMG signal is a widely focused, clinically viable, and reliable source for controlling bionics and prosthesis devices with the aid of machine-learning algorithms. The decisive step in the EMG pattern recognition (EMG-PR)-based control scheme is to extract the features with minimum neural information loss. This article proposes a novel feature extraction method based on advanced energy kernel-based features (AEKFs). The proposed method is evaluated on a scientific dataset which contains six types of upper limb motion with three different force variations. Furthermore, the EMG signal is acquired for eight upper limb gestures for the testing algorithm on the DSP processor. The efficiency of the proposed feature set has been investigated using classification accuracy (CA), Davies-Bouldin (DB) index-based separability measurement, and time complexity as performance metrics. Moreover, the proposed AEKF features, along with the LDA classifier, have been implemented on the DSP processor (ARM cortex M4) for real-time viability. Offline metrics comparison with the existing approaches prove that AEKF features exhibit lower time complexity along with a higher CA of 97.33%. The algorithm is tested on the DSP processor and CA is reported ≈ 92 %. MATLAB 2015a has been deployed in Intel Core i7, 3.40-GHz RAM for all offline analyses.
肌电图信号是一种广泛关注的、临床可行的、可靠的资源，可借助机器学习算法来控制仿生学和假肢设备。在基于肌电图模式识别（EMG-PR）的控制方案中，决定性的步骤是用最小的神经信息损失来提取特征。本文提出了一种基于先进能量核特征（AEKF）的新特征提取方法。该方法在一个科学数据集上进行了评估，该数据集包含六种上肢运动类型和三种不同的力变化。此外，还在上肢的八个动作上采集了肌电信号，以便在 DSP 处理器上测试算法。使用分类精度（CA）、基于 Davies-Bouldin（DB）指数的可分离性度量和时间复杂度作为性能指标，研究了所提出的特征集的效率。此外，还在 DSP 处理器（ARM cortex M4）上实现了基于 LDA 分类器的 AEKF 特征，以实现实时可行性。与现有方法的离线指标比较证明，AEKF 特征具有较低的时间复杂度和较高的 CA（97.33%）。该算法在 DSP 处理器上进行了测试，报告的 CA 约为 92%。所有离线分析均在配备 Intel Core i7、3.40-GHz RAM 的 MATLAB 2015a 上进行。
