Integrated biosignal data analysis for performance assessment

生物信号为了解人体的基本功能提供了一个窗口。人体运动涉及从大脑传输控制信号以激活骨骼肌，骨骼肌反过来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，已经产生了大量关于从记录的EMG预测力的工作。我们已经开发了基于单站点记录的EMG力模型，最近在多站点高密度EMG记录上，其中在所研究的肌肉的大表面积上检测到肌肉激活。然而，这些模型仍然受到肌肉激活的不完整图片的限制。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，其中力矩由跨关节作用的几块肌肉控制）。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。这部分是因为肌肉激活随着姿势的变化而变化，这改变了系统的生物力学。

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.