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. However, current sEMG processing framework can only achieve an extremely low signal-to-noise ratio (SNR), orders of magnitude lower than that of a standard mechanical sensor. This limitation prevents sEMG from reaching its full potential as a fast and accurate human motion intention estimator for next generation miHMIs. Although there are ways to enhance sEMG's SNR under the current processing framework, the improvement is marginal, and the result is nowhere near the SNR of standard mechanical sensors. As such, there is an imperative need for drastic improvements to the SNR of sEMG processing algorithms. To address this problem, I propose to take a radically different paradigm for processing sEMG with the Bayesian framework. The proposed research program will undertake a systematic investigation in the theory and application of the Bayesian framework in sEMG processing. The program will start from a well-defined generation model for sEMG, which the applicant had developed and successfully applied to the control of dexterous upper limb prosthesis (a specific type of miHMI). The model will be extended to incorporate more complex motor tasks. Then, probabilistic properties of various parameters of this model will be explicitly incorporated within Bayesian-based algorithms, resulting in model and tasks specific Bayesian algorithms for estimating motion intentions from sEMG. These algorithms will be examined, tested, and evaluated, in the context of estimating motor intentions from multi-channel sEMG. The performance of the algorithms will be gauged by estimation accuracy, online computation speed, and robustness against nonstationary factors. To ensure the practical applicability of the algorithm, special focus will be on the long term adaptive characteristic, both of the Bayesian algorithms and from the user.