Department of Biomedical Engineering, Faculty of Engineering, Science, Technology and Management, Ziauddin University, Karachi, Pakistan.
Department of Electrical Engineering, Faculty of Engineering, Science, Technology and Management, Ziauddin University, Karachi, Pakistan.
Proc Inst Mech Eng H. 2022 May;236(5):628-645. doi: 10.1177/09544119221074770. Epub 2022 Feb 4.
Upper limb myoelectric prosthetic control is an essential topic in the field of rehabilitation. The technique controls prostheses using surface electromyogram (sEMG) and intramuscular EMG (iEMG) signals. EMG signals are extensively used in controlling prosthetic upper and lower limbs, virtual reality entertainment, and human-machine interface (HMI). EMG signals are vital parameters for machine learning and deep learning algorithms and help to give an insight into the human brain's function and mechanisms. Pattern recognition techniques pertaining to support vector machine (SVM), k-nearest neighbor (KNN) and Bayesian classifiers have been utilized to classify EMG signals. This paper presents a review on current EMG signal techniques, including electrode array utilization, signal acquisition, signal preprocessing and post-processing, feature selection and extraction, data dimensionality reduction, classification, and ultimate application to the community. The paper also discusses using alternatives to EMG signals, such as force sensors, to measure muscle activity with reliable results. Future implications for EMG classification include employing deep learning techniques such as artificial neural networks (ANN) and recurrent neural networks (RNN) for achieving robust results.
上肢肌电假肢控制是康复领域的一个重要课题。该技术使用表面肌电图（sEMG）和肌内肌电图（iEMG）信号来控制假肢。EMG 信号广泛应用于控制假肢上下肢、虚拟现实娱乐和人机界面（HMI）。EMG 信号是机器学习和深度学习算法的重要参数，有助于深入了解人脑的功能和机制。支持向量机（SVM）、k-最近邻（KNN）和贝叶斯分类器等模式识别技术已被用于对 EMG 信号进行分类。本文对当前的 EMG 信号技术进行了综述，包括电极阵列的利用、信号采集、信号预处理和后处理、特征选择和提取、数据降维、分类以及最终在社区中的应用。本文还讨论了使用力传感器等 EMG 信号替代物来可靠地测量肌肉活动。未来的 EMG 分类应用包括采用深度学习技术，如人工神经网络（ANN）和递归神经网络（RNN），以获得稳健的结果。
