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: 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=709467
• 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.

生物信号为了解人体的基本功能提供了一个窗口。人类的运动包括从大脑传输控制信号来激活骨骼肌，骨骼肌反过来收缩并产生力量和运动。肌肉内的激活信号被检测并记录为肌电(EMG)。考虑到激活和力之间的联系，从记录的肌电中预测力的大量工作已经产生。我们已经开发了基于单部位记录的肌电力模型，以及最近基于多部位高密度肌电记录的肌电力模型，在该记录中，肌肉的激活在被研究的肌肉的大表面积上被检测到。然而，这些模型仍然受到肌肉激活的不完整图像的限制。同样，我们无法直接测量单个肌肉的输出力(作为替代，关节力矩是测量的，其中力矩由几个肌肉跨关节控制)。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。这在一定程度上是因为肌肉的激活会随着姿势的改变而改变，从而改变系统的生物力学。 我们的工作重点是使用先进的信号处理和系统建模技术，在健康个体中基于肌电预测肘关节的力。为了推进我们的工作，在限制较少的动态条件下开发准确和可靠的力预测，我们将在力预测模型中明确包含有关肢体生物力学以及肌肉激活如何协调的信息。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。在这项工作中，将对6名研究生和4名本科生进行肌电数据获取以及先进处理和建模技术的使用方面的培训。我们的研究结果将直接适用于人体工程学、田径和康复方面的问题，为加拿大和加拿大人带来重大好处。