Electromyography (EMG) signals are essential, as they are used to measure muscular activity in different parts of the human body. The measurement and analysis of EMG signal lead to various applications of muscle disorders such as muscular dystrophy, myopathy, hand movements, etc. In this paper, an improved and effective hand movement classification model is developed for amputee subjects. It includes: (1) EMG feature extraction using Discrete Wavelet Transform (DWT), (2) EMG feature selection using binary Global Best Guided Gaussian Artificial Bee Colony (BGGABC), (3) Hand movements classification using Optimized k-nearest neighbors (OKNN) classifier. The EMG signal is taken from the DB3 of NinaPro dataset comprising 17 different prosthetic hand movements recorded from 11 amputee subjects. Thereafter, DWT is applied to decompose the EMG signal for extracting features. An improved wrapper-based feature selection technique (BGGABC) is used to select the optimal feature subset for effective classification. The two variants of KNN, i.e. Smallest Modified KNN and Largest Modified KNN are taken in which item strength to a class is optimized for efficient classification. The strength of an item to a class depends on distance and weight of an item to a class. Therefore, a multi-objective Non-dominated sorting genetic algorithm-II (NSGA-II) is used for optimizing these two contradictory parameters (distance and weight) simultaneously to have optimized variants, namely: Optimized Smallest KNN (OSKNN) and Optimized Largest KNN (OLKNN). Extensive results show that the proposed method OKNN achieved the highest classification accuracy of 93.07% (OLKNN) and 89.43% (OSKNN) compared with KNN variants and competitors.

中文翻译：

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使用肌电图信号对假手运动进行优化的 k 近邻分类

肌电图 (EMG) 信号至关重要，因为它们用于测量人体不同部位的肌肉活动。肌电信号的测量和分析导致了肌肉疾病的各种应用，如肌营养不良、肌病、手部运动等。本文为截肢者开发了一种改进且有效的手部运动分类模型。它包括：（1）使用离散小波变换（DWT）进行肌电图特征提取，（2）使用二进制全局最佳引导高斯人工蜂群（BGGABC）进行肌电图特征选择，（3）使用优化k近邻（OKNN）进行手部运动分类) 分类器。 EMG 信号取自 NinaPro 数据集的 DB3，其中包括 11 名截肢者记录的 17 种不同的假手运动。此后，应用DWT来分解EMG信号以提取特征。使用改进的基于包装器的特征选择技术（BGGABC）来选择最佳特征子集以进行有效分类。采用 KNN 的两种变体，即最小修改 KNN 和最大修改 KNN，其中优化了类别的项目强度以实现有效分类。物品对类别的强度取决于物品与类别的距离和权重。因此，采用多目标非支配排序遗传算法-II（NSGA-II）同时优化这两个矛盾参数（距离和权重），得到优化变体，即：优化最小KNN（OSKNN）和优化最大KNN （OLKNN）。大量结果表明，与 KNN 变体和竞争对手相比，所提出的方法 OKNN 实现了 93.07%（OLKNN）和 89.43%（OSKNN）的最高分类精度。