Signal Processing of Electromyography with Bayesian Framework for Next Generation Motion Integration Human Machine Interface

人机界面（HMI）将很快嵌入到我们社会的各个方面。一种类型的人机界面用于捕获、监控、辅助和增强人体运动。它现在被发现在大量不同的领域，包括但不限于，制造业，医疗保健，健身跟踪，办公室人体工程学和娱乐。在这里，这种类型的HMI被称为运动集成HMI（miHMI）。下一代miHMI需要比以往更快、更可靠地感知人类运动意图。然而，为了实现这一目标，必须结合目前唯一使用的机械传感器之外的替代传感器模态。最近，肌电图（EMG），肌肉活动的电表现，已经为此目的进行了研究。从理论上讲，表面肌电信号（sEMG）是比机械传感器更好的快速可靠的人体运动意图估计来源，因为1）它总是比相应的机械信号更早出现; 2）它总是可测量的，而在某些情况下，机械信号很难或不可能测量。

Human machine interface (HMI) will soon be embedded in every aspect of our society. One type of HMIs is used to capture, monitor, assist, and augment human motion. It is are now found in a vast array of different fields including, but not limited to, manufacturing, healthcare, fitness tracking, office ergonomics, and entertainment. Here, this type of HMI is called motion integration HMI (miHMI). Next generation miHMIs need to sense human motion intentions faster and more reliably than ever before. To achieve this goal, however, alternative sensor modalities beyond mechanical sensors, which are exclusively used currently, must be incorporated. Recently, electromyogram (EMG), the electric manifestation of muscle activities, has been investigated for this purpose. In theory, surface EMG (sEMG) is a better source for fast and reliable human motion intention estimation than mechanical sensors because 1) it always appears earlier than the corresponding mechanical signals; 2) it is always measureable, while in some cases mechanical signals are difficult or impossible to measure.