Venugopal G, Deepak P, Ghosh Diptasree M, Ramakrishnan S
1 Non-Invasive Imaging and Diagnostics Laboratory, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India.
2 Department of Instrumentation and Control Engineering, N. S. S. College of Engineering, Palakkad, Kerala, India.
Proc Inst Mech Eng H. 2017 Nov;231(11):1025-1033. doi: 10.1177/0954411917727307. Epub 2017 Aug 23.
Surface electromyography is a non-invasive technique used for recording the electrical activity of neuromuscular systems. These signals are random, complex and multi-component. There are several techniques to extract information about the force exerted by muscles during any activity. This work attempts to generate surface electromyography signals for various magnitudes of force under isometric non-fatigue and fatigue conditions using a feedback model. The model is based on existing current distribution, volume conductor relations, the feedback control algorithm for rate coding and generation of firing pattern. The result shows that synthetic surface electromyography signals are highly complex in both non-fatigue and fatigue conditions. Furthermore, surface electromyography signals have higher amplitude and lower frequency under fatigue condition. This model can be used to study the influence of various signal parameters under fatigue and non-fatigue conditions.
表面肌电图是一种用于记录神经肌肉系统电活动的非侵入性技术。这些信号是随机、复杂且多成分的。有几种技术可用于提取在任何活动期间肌肉施加力的相关信息。这项工作试图使用反馈模型在等长非疲劳和疲劳条件下生成不同力大小的表面肌电图信号。该模型基于现有的电流分布、容积导体关系、速率编码的反馈控制算法以及放电模式的生成。结果表明,合成表面肌电图信号在非疲劳和疲劳条件下都高度复杂。此外,表面肌电图信号在疲劳条件下具有更高的幅度和更低的频率。该模型可用于研究疲劳和非疲劳条件下各种信号参数的影响。