Nazmi Nurhazimah, Abdul Rahman Mohd Azizi, Yamamoto Shin-Ichiroh, Ahmad Siti Anom, Zamzuri Hairi, Mazlan Saiful Amri
Malaysia Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Malaysia.
Department of Bio-Science and Engineering, College of Systems Engineering and Science, Shibaura Institute of Technology, Fukasaku 307, Saitama-City 337-8570, Japan.
Sensors (Basel). 2016 Aug 17;16(8):1304. doi: 10.3390/s16081304.
In recent years, there has been major interest in the exposure to physical therapy during rehabilitation. Several publications have demonstrated its usefulness in clinical/medical and human machine interface (HMI) applications. An automated system will guide the user to perform the training during rehabilitation independently. Advances in engineering have extended electromyography (EMG) beyond the traditional diagnostic applications to also include applications in diverse areas such as movement analysis. This paper gives an overview of the numerous methods available to recognize motion patterns of EMG signals for both isotonic and isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who would like to select the most appropriate methodology in classifying motion patterns, especially during different types of contractions. For feature extraction, the probability density function (PDF) of EMG signals will be the main interest of this study. Following that, a brief explanation of the different methods for pre-processing, feature extraction and classifying EMG signals will be compared in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.
近年来，康复期间接受物理治疗受到了广泛关注。一些出版物已经证明了其在临床/医学和人机界面（HMI）应用中的有用性。自动化系统将指导用户在康复期间独立进行训练。工程学的进步已将肌电图（EMG）从传统的诊断应用扩展到运动分析等不同领域。本文概述了用于识别等张收缩和等长收缩的肌电信号运动模式的众多方法。通过说明各种信号分析方法在实时设置中的适用性对其进行了比较。本文将对那些希望在分类运动模式时，尤其是在不同类型收缩期间选择最合适方法的研究人员具有吸引力。对于特征提取，肌电信号的概率密度函数（PDF）将是本研究的主要关注点。在此之后，将根据不同方法的性能，对肌电信号预处理、特征提取和分类的不同方法进行简要比较。本文的关键在于回顾与上述问题相关的最新发展和研究。
