Disselhorst-Klug Catherine, Williams Sybele
Department of Rehabilitation & Prevention Engineering, Institute of Applied Medical Engineering, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University, Aachen, Germany.
Front Neurol. 2020 Dec 3;11:603550. doi: 10.3389/fneur.2020.603550. eCollection 2020.
Coordinated activation of muscles is the basis for human locomotion. Impaired muscular activation is related to poor movement performance and disability. To restore movement performance, information about the subject's individual muscular activation is of high relevance. Surface electromyography (sEMG) allows the pain-free assessment of muscular activation and many ready-to-use technologies are available. They enable the usage of sEMG measurements in several applications. However, due to the fact that in most rehabilitation applications dynamic conditions are analyzed, the correct interpretation of sEMG signals remains difficult which hinders the spread of sEMG in clinical applications. From biomechanics it is well-known that the sEMG signal depends on muscle fiber length, contraction velocity, contraction type and on the muscle's biomechanical moment. In non-isometric conditions these biomechanical factors have to be considered when analyzing sEMG signals. Additionally, the central nervous system control strategies used to activate synergistic and antagonistic muscles have to be taken into consideration. These central nervous system activation strategies are rarely known in physiology and are hard to manage in pathology. In this perspective report we discuss how the consideration of biomechanical factors leads to more reliable information extraction from sEMG signals and how the limitations of sEMG can be overcome in dynamic conditions. This is a prerequisite if the use of sEMG in rehabilitation applications is to extend. Examples will be given showing how the integration of biomechanical knowledge into the interpretation of sEMG helps to identify the central nervous system activation strategies involved and leads to relevant clinical information.
肌肉的协同激活是人类运动的基础。肌肉激活受损与运动表现不佳和残疾有关。为了恢复运动表现，有关受试者个体肌肉激活的信息具有高度相关性。表面肌电图（sEMG）可以在无痛的情况下评估肌肉激活情况，并且有许多现成的技术可供使用。它们使得sEMG测量能够应用于多种场合。然而，由于在大多数康复应用中需要分析动态条件，sEMG信号的正确解读仍然很困难，这阻碍了sEMG在临床应用中的推广。从生物力学角度可知，sEMG信号取决于肌肉纤维长度、收缩速度、收缩类型以及肌肉的生物力学力矩。在非等长条件下，分析sEMG信号时必须考虑这些生物力学因素。此外，还必须考虑用于激活协同肌和拮抗肌的中枢神经系统控制策略。这些中枢神经系统激活策略在生理学中鲜为人知，在病理学中也难以处理。在这份观点报告中，我们讨论了如何通过考虑生物力学因素从sEMG信号中提取更可靠的信息，以及如何在动态条件下克服sEMG的局限性。如果要扩大sEMG在康复应用中的使用范围，这是一个先决条件。将给出一些例子，展示将生物力学知识整合到sEMG解读中如何有助于识别所涉及的中枢神经系统激活策略，并得出相关的临床信息。
