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

使用贝叶斯框架进行肌电图信号处理，用于下一代运动集成人机界面

• 批准号: RGPIN-2016-04137
• 负责人: Jiang, Ning
• 金额: $ 3.35万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=689362
• 关键词: Signal; Processing; Electromyography; Bayesian; Framework

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.* **

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HMI用于捕获、监视、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。然而，目前的表面肌电信号处理框架只能实现极低的信噪比（SNR），比标准机械传感器低几个数量级。这种限制阻止了sEMG充分发挥其作为下一代miHMI的快速准确的人体运动意图估计器的潜力。虽然在当前的处理框架下有方法提高sEMG的SNR，但这种改进是微不足道的，并且结果与标准机械传感器的SNR相差甚远。因此，迫切需要大幅改善sEMG处理算法的SNR。为了解决这个问题，我建议采取一个完全不同的范式与贝叶斯框架处理表面肌电信号。* 拟议的研究计划将在表面肌电信号处理的贝叶斯框架的理论和应用进行系统的调查。该计划将从一个定义良好的sEMG生成模型开始，申请人已经开发并成功应用于灵巧上肢假肢（一种特定类型的miHMI）的控制。该模型将被扩展到包括更复杂的运动任务。然后，该模型的各种参数的概率属性将被明确纳入基于贝叶斯的算法，从而产生用于从sEMG估计运动意图的模型和任务特定的贝叶斯算法。这些算法将被检查，测试和评估，在从多通道表面肌电信号估计运动意图的背景下。算法的性能将通过估计精度、在线计算速度和对非平稳因素的鲁棒性来衡量。为了确保算法的实用性，将特别关注贝叶斯算法和用户的长期自适应特性。**