Electromyogram (EMG) signals have had a great impact on many applications, including prosthetic or rehabilitation devices, human-machine interactions, clinical and biomedical areas. In recent years, EMG signals have been used as a popular tool to generate device control commands for rehabilitation equipment, such as robotic prostheses. This intention of this study was to design an EMG signal-based expert model for hand-grasp classification that could enhance prosthetic hand movements for people with disabilities. The study, thus, aimed to introduce an innovative framework for recognising hand movements using EMG signals. The proposed framework consists of logarithmic spectrogram-based graph signal (LSGS), AdaBoost k-means (AB-k-means) and an ensemble of feature selection (FS) techniques. First, the LSGS model is applied to analyse and extract the desirable features from EMG signals. Then, to assist in selecting the most influential features, an ensemble FS is added to the design. Finally, in the classification phase, a novel classification model, named AB-k-means, is developed to classify the selected EMG features into different hand grasps. The proposed hybrid model, LSGS-based scheme is evaluated with a publicly available EMG hand movement dataset from the UCI repository. Using the same dataset, the LSGS-AB-k-means design model is also benchmarked with several classifications including the state-of-the-art algorithms. The results demonstrate that the proposed model achieves a high classification rate and demonstrates superior results compared to several previous research works. This study, therefore, establishes that the proposed model can accurately classify EMG hand grasps and can be implemented as a control unit with low cost and a high classification rate.

肌电图 (EMG) 信号对许多应用产生了巨大影响，包括假肢或康复设备、人机交互、临床和生物医学领域。近年来，EMG 信号已被用作一种流行的工具，用于为康复设备（例如机器人假肢）生成设备控制命令。本研究的目的是设计一个基于 EMG 信号的手握分类专家模型，该模型可以增强残疾人的假肢手部运动。因此，该研究旨在引入一种使用 EMG 信号识别手部运动的创新框架。所提出的框架由基于对数谱图的图信号 (LSGS)、AdaBoost k-means (AB-k-means) 和特征选择 (FS) 技术的集合组成。第一的，LSGS 模型用于从 EMG 信号中分析和提取所需的特征。然后，为了帮助选择最具影响力的特征，在设计中添加了一个集成 FS。最后，在分类阶段，开发了一种名为 AB-k-means 的新型分类模型，将选定的 EMG 特征分类为不同的手部抓握。所提出的混合模型，基于 LSGS 的方案使用来自 UCI 存储库的公开可用的 EMG 手部运动数据集进行评估。使用相同的数据集，LSGS-AB-k-means 设计模型也使用包括最先进算法在内的多个分类进行基准测试。结果表明，与之前的几项研究工作相比，所提出的模型实现了高分类率并显示出优异的结果。因此，本研究