Novel Machine Learning Methods for Robust Myoelectric Control

我的研究小组利用肌肉收缩产生的电信号(肌电信号)，在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。我们在算法开发和性能评估方面仍处于世界领先地位。主要贡献来自于新的发展和机器学习，包括特征和分类器设计，以及对训练、运动学习和反馈对实时控制的影响的表征。这项研究为嵌入式肌电控制系统的成功商业化奠定了基础，我以前的两名研究生成立了一家公司。虽然假肢控制的可靠性和灵活性仍有待提高，但在现有和新兴的消费应用中，肌电信号作为人机界面(HMI)的创新使用潜力巨大。肌电信号的一个吸引人的方面是，它是作为自然肌肉收缩的结果而产生的，如果可靠地提取了运动意图，它可以用于自主控制设备。由于在手臂上放置了传感器，它作为一个通用的HMI使用，允许使用平视控制场景，对于健全的用户，可以腾出手来执行其他任务。这在虚拟和增强现实系统以及导航过程中的辅助控制(骑自行车或驾驶车辆)中有许多潜在的应用。虽然在假肢控制方面的巨大成功可能会转化成大量的知识，但针对消费者应用的优化设计将需要机器学习算法、传感器设计和训练方法的新发展。这项研究计划将专注于优化肌电控制的灵活性和健壮性，作为一种通用的人机界面。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，以用于未来的传感技术，包括周围神经和皮质植入。我的短期目标是：1.开发分析方法，明确结合和利用时间信息，以实现新的控制模式并提高可靠性。2.使用深度学习方法提高对失真的适应能力。为驻留在肌肉内的电极开发优化的特征和分类器设计。开发强大的视觉反馈方法来诱导运动学习，从而在训练中诱导运动学习，从而产生更好的真实世界表现。这项工作将为学生提供引人入胜的多学科培训机会，并开发在解决问题、批判性思维、信号处理、机器学习和人机交互方面备受欢迎的技能。

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.