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\approx ~92 %. MATLAB 2015a has been deployed in Intel Core i7, 3.40-GHz RAM for all offline analyses.

中文翻译：

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基于先进能量核的特征提取方案，用于改进基于 EMG-PR 的假肢控制力变化


EMG 信号是一种广泛关注的、临床上可行且可靠的信号源，用于借助机器学习算法控制仿生学和假肢装置。基于肌电图模式识别（EMG-PR）的控制方案的决定性步骤是提取神经信息损失最小的特征。本文提出了一种基于高级能量核特征（AEKF）的新颖特征提取方法。所提出的方法在科学数据集上进行评估，该数据集包含具有三种不同力变化的六种类型的上肢运动。此外，还采集了八个上肢姿势的肌电信号，用于 DSP 处理器上的测试算法。使用分类精度 (CA)、基于 Davies_Bouldin (DB) 索引的可分离性测量和时间复杂度作为性能指标来研究所提出的特征集的效率。此外，所提出的 AEKF 功能以及 LDA 分类器已在 DSP 处理器（ARM Cortex M4）上实现，以实现实时可行性。与现有方法的离线指标比较证明，AEKF 特征表现出较低的时间复杂度以及高达 97.33% 的 CA。该算法在 DSP 处理器上进行了测试，CA 约为 92\approx ~92 %。 MATLAB 2015a 已部署在 Intel Core i7、3.40-GHz RAM 中，用于所有离线分析。