Novel Machine Learning Methods for Robust Myoelectric Control

我的研究小组在利用肌肉收缩产生的电信号（肌电信号）开发基于模式识别的动力假肢控制方面进行了开创性的工作。我们仍然是算法开发和性能评估的世界领导者。主要贡献来自新的发展和机器学习，包括特征和分类器设计，以及训练，运动学习和反馈对实时控制的影响的表征。这项研究已经形成了嵌入式肌电控制系统成功商业化的基础，我以前的两个研究生成立了一家公司。虽然工作仍然是为了提高假肢控制的可靠性和灵活性，但肌电信号作为现有和新兴消费者应用的人机界面（HMI）的创新使用具有巨大的潜力。肌电信号的吸引人的方面在于，它是作为自然肌肉收缩的结果而生成的，并且如果可靠地提取运动意图，则它可以用于自主地控制设备。通过将传感器放置在手臂上，其作为通用HMI的使用允许使用抬头控制场景，并且对于身体健全的用户，可以腾出双手来执行其他任务。这在虚拟和增强现实系统以及导航（骑自行车或驾驶车辆）期间的辅助控制中具有许多潜在应用。 虽然相当多的知识可以从假肢控制的巨大成功中转化而来，但消费者应用的最佳设计将需要机器学习算法、传感器设计和训练方法的新开发。 这项研究计划将集中在优化的灵巧性和鲁棒性的肌电控制作为一个广义的人机界面。这些目标将通过开发新的信息提取方法和利用运动控制和学习来实现。 我的长期目标是开发足够灵活和可扩展的信号处理和训练策略，用于未来的传感技术，包括外周神经和皮层植入物。 我的短期目标是：1.开发分析方法，明确纳入和利用时间信息，以实现新的控制模式并提高可靠性。 2.使用深度学习方法来提高对失真的恢复能力。 3.为位于肌肉内部的电极开发优化的特征和分类器设计。 4.开发强大的视觉反馈方法来诱导运动学习，以在训练过程中诱导运动学习，从而获得更好的现实表现。 这项工作将为学生提供参与多学科培训的机会，并在解决问题，批判性思维，信号处理，机器学习和人机交互方面开发备受追捧的技能。

My research group has conducted seminal work in developing pattern recognition-based control of powered artificial limbs using the electrical signals produced by contracting muscles (the myoelectric signal). We remain world leaders in algorithm development and assessment of performance. The primary contributions have come through novel developments and machine learning including feature and classifier design, and the characterization of the impact of training, motor learning and feedback on real-time control. This research has formed the basis of successful commercialization of embedded myoelectric control systems, with the formation of a company by two of my former graduate students. While work remains to improve the reliability and dexterity of control for artificial limbs, there is tremendous potential for innovative use of the myoelectric signal as a human-machine interface (HMI) for existing and emerging consumer applications. An appealing aspect of the myoelectric signal is that it is generated as a consequence of natural muscle contraction and, if movement intent is reliably extracted, it may be used to autonomously control devices. With sensors placed on the arms, its use as a generalized HMI allows use heads-up control scenarios and, with able-bodied users, leaves the hands free for other tasks. This has many potential applications in virtual and augmented reality systems, and auxiliary control during navigation (cycling or driving a vehicle). Although considerable knowledge may be translated from the great success in prosthetics control, optimal design for consumer applications will require novel development of machine learning algorithms, sensor design, and training methodologies. This research program will focus on optimizing the dexterity and robustness of myoelectric control as a generalized human computer interface. These goals will be accomplished by developing novel methods of information extraction and leveraging motor control and learning. My long-term objective envisions developing signal processing and training strategies that will be sufficiently flexible and scalable to be used in future sensing technologies, including peripheral nerve and cortical implants. My short-term objectives are:  1.Develop analytic methods that explicitly incorporate and leverage temporal information to enable new control modalities and improve reliability.  2.Use deep learning methods to improve resilience to distortion.  3.Develop optimized feature and classifier design for electrodes that reside inside the muscles.  4.Develop powerful visual feedback methods to induce motor learning to induce motor learning during training that will result in better real-world performance. This work will provide engaging multidisciplinary training opportunities for students and develop highly sought-after skillsets in problem solving, critical thinking, signal processing, machine learning, and human machine interaction.