Martin Bernard J, Acosta-Sojo Yadrianna
SensoriMotor Systems-and Human Performance Laboratory, Center for Ergonomics, Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, United States.
Front Neurol. 2021 Feb 5;11:588451. doi: 10.3389/fneur.2020.588451. eCollection 2020.
Surface electromyography (sEMG) may not be a simple 1,2,3 (muscle, electrodes, signal)-step operation. Lists of sEMG characteristics and applications have been extensively published. All point out the noise mimicking perniciousness of the sEMG signal. This has resulted in ever more complex manipulations to interpret muscle functioning and sometimes gobbledygook. Hence, as for all delicate but powerful tools, sEMG presents challenges in terms of precision, knowledge, and training. The theory is usually reviewed in courses concerning sensorimotor systems, motor control, biomechanics, ergonomics, etc., but application requires creativity, training, and practice. Software has been developed to navigate the essence extraction (step 4); however, each software requires some parametrization, which returns back to the theory of sEMG and signal processing. Students majoring in Ergonomics or Biomedical Engineering briefly learn about the sEMG method but may not necessarily receive extensive training in the laboratory. Ergonomics applications range from a simple estimation of the muscle load to understanding the sense of effort and sensorimotor asymmetries. In other words, it requires time and the basics of multiple disciplines to acquire the necessary knowledge and skills to perform these studies. As an example, sEMG measurements of left/right limb asymmetries in muscle responses to vibration-induced activity of proprioceptive receptors, which vary with gender, provide insight into the functioning of sensorimotor systems. Beyond its potential clinical benefits, this example also shows that lack of testing time and lack of practitioner's sufficient knowledge are barriers to the utilization of sEMG as a clinical tool.
表面肌电图(sEMG)可能并非简单的1、2、3步操作(肌肉、电极、信号)。关于sEMG特征和应用的列表已大量发表。所有这些都指出了模拟sEMG信号有害性的噪声。这导致了越来越复杂的操作来解释肌肉功能,有时甚至是晦涩难懂的内容。因此,就像所有精密但强大的工具一样,sEMG在精度、知识和培训方面都带来了挑战。该理论通常在有关感觉运动系统、运动控制、生物力学、人体工程学等课程中进行复习,但应用需要创造力、培训和实践。已经开发了软件来进行本质提取(第4步);然而,每个软件都需要一些参数设置,这又回到了sEMG和信号处理的理论。主修人体工程学或生物医学工程的学生简要学习sEMG方法,但不一定在实验室接受广泛培训。人体工程学应用范围从简单估计肌肉负荷到理解用力感觉和感觉运动不对称性。换句话说,需要时间和多学科的基础知识来获得进行这些研究所需的知识和技能。例如,对本体感受器振动诱发活动的肌肉反应中左右肢体不对称性的sEMG测量,其因性别而异,有助于深入了解感觉运动系统的功能。除了其潜在的临床益处外,这个例子还表明,缺乏测试时间和从业者缺乏足够的知识是将sEMG用作临床工具的障碍。