Integrated biosignal data analysis for performance assessment

用于绩效评估的综合生物信号数据分析

• 批准号: RGPIN-2016-04788
• 负责人: Morin, Evelyn
• 金额: $ 2.26万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=660212
• 关键词: Integrated; biosignal; data; analysis; performance

Biological signals provide a window into essential functions in the human body. Human movement involves transmission of control signals from the brain to activate skeletal muscles, which in turn, contract and generate force and movement. The activation signal within the muscle is detected and recorded as the electromyogram (EMG). Given the connection between activation and force, an extensive body of work on predicting force from recorded EMG has been generated. We have developed EMG-force models based on single site recordings, and more recently on multi-site high-density EMG recordings, in which muscle activation is detected over a large surface area of the muscle under study. These models, however, are still limited by an incomplete picture of the muscle activation. As well, we are unable to directly measure output forces for individual muscles (as a surrogate, joint moment is measured, where the moment is controlled by several muscles acting across the joint). Reasonable force estimation has been achieved only under highly restricted, usually isometric (constant position), experimental conditions. This is in part because muscle activation changes with postural changes, which alter the biomechanics of the system.***Our work has focussed on EMG-based prediction of forces about the elbow joint, in healthy individuals, using advanced signal processing and system modeling techniques. In order to advance our work to develop accurate and reliable force prediction under less restricted, dynamic conditions, we will explicitly include information on the biomechanics of the limb, and on how muscle activation is coordinated, in our force prediction models. The innovative signal processing techniques and model structures that we develop will contribute to our fundamental understanding of normal muscle function and control. In the course of this work, six graduate and four undergraduate students will be trained in the acquisition of EMG data, and the use of advanced processing and modeling techniques. The results of our research will be directly applicable to problems in ergonomics, athletics, and rehabilitation, providing significant benefits to Canada and Canadians.***

生物信号提供了了解人体基本功能的窗口。人体运动涉及从大脑传输控制信号以激活骨骼肌，进而收缩并产生力和运动。肌肉内的激活信号被检测并记录为肌电图 (EMG)。鉴于激活和力之间的联系，已经产生了根据记录的肌电图预测力的广泛工作。我们开发了基于单点记录的肌电图力模型，最近又开发了基于多点高密度肌电图记录的肌电图力模型，其中在所研究的肌肉的大表面积上检测肌肉激活。然而，这些模型仍然受到肌肉激活不完整图像的限制。同样，我们无法直接测量单个肌肉的输出力（作为替代，测量关节力矩，其中力矩由作用于关节的几块肌肉控制）。只有在高度受限的、通常是等距（恒定位置）的实验条件下才能实现合理的力估计。部分原因是肌肉激活随着姿势变化而变化，从而改变了系统的生物力学。***我们的工作重点是使用先进的信号处理和系统建模技术，基于肌电图预测健康个体的肘关节力。为了推进我们的工作，在限制较少的动态条件下开发准确可靠的力预测，我们将在力预测模型中明确包含有关肢体生物力学以及如何协调肌肉激活的信息。我们开发的创新信号处理技术和模型结构将有助于我们对正常肌肉功能和控制的基本理解。在这项工作过程中，六名研究生和四名本科生将接受肌电图数据采集以及先进处理和建模技术使用的培训。我们的研究结果将直接适用于人体工程学、体育和康复方面的问题，为加拿大和加拿大人带来重大利益。***