Electromyography and ultrasound investigation of upper-arm muscles

上臂肌肉的肌电图和超声检查

• 批准号: RGPIN-2018-06662
• 负责人: Mathieu, PierreA
• 金额: $ 2.04万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=714385
• 关键词: Electromyography; ultrasound; investigation; upper; arm

My research program is oriented toward the study of upper limb muscles activation. Focus is on the multifunctional biceps brachii because physiologically, its motor units are activated in different regions of the muscle depending on the arm position, and anatomically because of up to 6 individually innervated compartments can be observed on its inner surface. Our objective is to find how those compartments could voluntarily be contracted and thus behave as “muscles” within a muscle. This fundamental aspect has a practical impact for upper limb amputees who use modern prostheses requiring many electromyographic (EMG) control signals to fully benefit from their ability to produce many movements. In our lab, EMG recordings were done with 10 surface electrode pairs positioned across the biceps of 10 normal subjects who experimented different upper limb and hand positions while their biceps was contracted. To those surface signals, will be associated the presence of electric dipoles within the biceps tissue. Location of those dipoles will be done with an inverse problem solving method applied on a finite element upper arm model. This model will be established with information obtained on upper arm magnetic resonance images. To include the presence of compartments within this model, high resolution magnetic images obtained from human biceps will be segmented to identify location and size of the compartments all along the length of their biceps. As our hand posture changes, tissues under the skin are subjected to deformations which can be imaged with an ultrasound (US) equipment. On the obtained images a semi-automatic segmentation algorithm will be used to identify the short and long head of the biceps, the top of the humerus bone and the cephalic and basilic veins. Between those identified structures, distance and angular position will be measured and area of the short and long heads of the biceps measured. Those under the skin changes will be used in the analysis of surface EMG signals. Within a contracted biceps, zones of high stiffness stress will be identified with US elastography, Obtained in condition similar to those of EMG recording, high stiffness zones are expected to overlap the electric dipole positions. Recently, extraction of muscular synergies from many surface EMG signals proved to be useful to control a prosthesis. We found that muscle synergy could also be extracted from a single muscle such as the biceps. As a proof of concept, muscle synergies of 5 signals of the biceps will be used to have a small humanoid robot to on-line duplicate arm movements produced by normal subjects. Fusion of several modalities, exploration of muscle compartments and muscular synergy could lead to an easier control of myoelectric prostheses.

我的研究项目面向上肢肌肉激活的研究。重点关注多功能肱二头肌，因为从生理学上讲，其运动单位根据手臂位置在不同的肌肉区域被激活；从解剖学上讲，因为在其内表面上可以观察到多达 6 个独立神经支配的隔室。我们的目标是找到这些隔室如何自愿收缩，从而表现得像肌肉中的“肌肉”。这一基本方面对于使用现代假肢的上肢截肢者来说具有实际影响，这些假肢需要许多肌电图（EMG）控制信号才能充分受益于他们产生多种运动的能力。 在我们的实验室中，肌电图记录是通过放置在 10 名正常受试者的二头肌上的 10 对表面电极完成的，这些受试者在二头肌收缩时尝试了不同的上肢和手部位置。对于这些表面信号，将与二头肌组织内电偶极子的存在相关联。这些偶极子的定位将通过应用于有限元上臂模型的逆问题求解方法来完成。该模型将利用上臂磁共振图像获得的信息建立。为了在该模型中包含隔室的存在，将从人类二头肌获得的高分辨率磁图像进行分割，以识别沿着二头肌长度的隔室的位置和大小。 当我们的手部姿势发生变化时，皮肤下的组织会发生变形，可以使用超声波（美国）设备对其进行成像。 在获得的图像上，将使用半自动分割算法来识别二头肌的短头和长头、肱骨顶部以及头静脉和贵要静脉。在这些识别的结构之间，将测量距离和角位置，并测量二头肌的短头和长头的面积。皮肤下的变化将用于分析表面肌电图信号。 在收缩的二头肌内，高硬度应力区域将通过US弹性成像来识别，在与EMG记录相似的条件下获得，高硬度区域预计与电偶极子位置重叠。 最近，从许多表面肌电图信号中提取肌肉协同作用被证明对于控制假肢很有用。我们发现肌肉协同作用也可以从单一肌肉（例如二头肌）中提取。作为概念验证，二头肌 5 个信号的肌肉协同作用将用于让小型人形机器人在线复制正常受试者产生的手臂运动。 多种模式的融合、肌肉区室的探索和肌肉协同作用可以使肌电假肢的控制变得更容易。