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双相和高密度表面肌电图的基本概念理解和教学临床、职业和运动应用:起源、检测和主要错误。

Fundamental Concepts of Bipolar and High-Density Surface EMG Understanding and Teaching for Clinical, Occupational, and Sport Applications: Origin, Detection, and Main Errors.

机构信息

LAM-Motion Analysis Laboratory, Neuromotor and Rehabilitation Department, S. Sebastiano Hospital, Azienda USL-IRCCS di Reggio Emilia, Via Circondaria 29, 42015 Correggio, Italy.

Merlo Bioengineering, 43121 Parma, Italy.

出版信息

Sensors (Basel). 2022 May 30;22(11):4150. doi: 10.3390/s22114150.

Abstract

Surface electromyography (sEMG) has been the subject of thousands of scientific articles, but many barriers limit its clinical applications. Previous work has indicated that the lack of time, competence, training, and teaching is the main barrier to the clinical application of sEMG. This work follows up and presents a number of analogies, metaphors, and simulations using physical and mathematical models that provide tools for teaching sEMG detection by means of electrode pairs (1D signals) and electrode grids (2D and 3D signals). The basic mechanisms of sEMG generation are summarized and the features of the sensing system (electrode location, size, interelectrode distance, crosstalk, etc.) are illustrated (mostly by animations) with examples that teachers can use. The most common, as well as some potential, applications are illustrated in the areas of signal presentation, gait analysis, the optimal injection of botulinum toxin, neurorehabilitation, ergonomics, obstetrics, occupational medicine, and sport sciences. The work is primarily focused on correct sEMG detection and on crosstalk. Issues related to the clinical transfer of innovations are also discussed, as well as the need for training new clinical and/or technical operators in the field of sEMG.

摘要

表面肌电图(sEMG)已经成为数千篇科学文章的主题,但许多障碍限制了其临床应用。以前的工作表明,缺乏时间、能力、培训和教学是 sEMG 临床应用的主要障碍。这项工作是对使用物理和数学模型进行的一系列类比、隐喻和模拟的跟进,并提供了通过电极对(1D 信号)和电极网格(2D 和 3D 信号)检测 sEMG 的教学工具。总结了 sEMG 产生的基本机制,并说明了传感系统的特征(电极位置、大小、电极间距离、串扰等)(主要通过动画),并举例说明了教师可以使用的示例。最常见的以及一些潜在的应用领域包括信号呈现、步态分析、肉毒毒素最佳注射、神经康复、人体工程学、产科、职业医学和运动科学。这项工作主要集中在正确的 sEMG 检测和串扰上。还讨论了与创新的临床转化相关的问题,以及在 sEMG 领域培训新的临床和/或技术操作人员的必要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b74/9185290/572e59970096/sensors-22-04150-g001.jpg

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