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

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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.