Pattern Recognition Based Myoelectric Control for Emerging Human-Machine Interfaces

技术的可获得性和实用性的快速变化使社会处于不断适应的状态。现在意识到久坐不动的生活方式以及普遍使用电脑和移动设备的危险，许多人正在争先恐后地寻找替代方案。随着新兴的平视虚拟和增强现实设备的出现，人们对能够实现移动交互的免提交互方法的需求越来越大。几十年来，肌电控制一直被用来控制截肢者的假肢。肌电控制是利用肌肉收缩过程中产生的电信号来解码用户的意图。最近，技术、机器学习和用户培训的进步彻底改变了肌电控制的实现方式，使得能够更直观地控制多个自由度。然而，相比之下，肌电控制作为一种潜在的人机界面与其他消费产品一起使用还相对没有被探索过。那些追求这一方向的人大多直接应用假肢研究机构的知识，限制了其有效性。因此，关于消费者使用的肌电控制的潜力、健壮性和直观性存在许多突出的问题。 在这项工作中，我们将开发新的人机交互模型和设备，专门设计用于新兴的消费者免提技术。作为生物医学工程研究所的创新研究主席，作为肌电控制领域的世界知名领导者和有影响力的人，我们将利用在开发用于假肢的最先进的肌电控制方面所学到的经验教训，同时针对这一截然不同的用例进行设计。我们将确定适当的手势集，以平衡应用程序的直观性与控制方案的信息内容和健壮性，并通过将用户视为控制系统的一部分来改进培训和学习过程。最后，我们将探索替代传感器和传感器融合在开发多功能和健壮的免提人机界面中的作用。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。该计划的结果将产生基础性研究，将扩大肌电控制的应用，并为新技术的设计提供信息。开发直观、免提的人机界面将减少与当前工作环境相关的限制和风险。

Rapid changes in the availability and utility of technology have left society in a state of constant adaptation. Now realizing the dangers of sedentary lifestyles and pervasive computer and mobile device use, many are scrambling to find alternatives. With the advent of emerging heads-up virtual and augmented reality devices, there is growing demand for hands-free interaction approaches that could enable interaction on the go. For decades, myoelectric control, the use of electrical signals generated during muscle contractions to decode user intent, has been used to control prostheses for amputees. Recently, advances in technology, machine learning, and user training have revolutionized the way myoelectric control is achieved, enabling more intuitive control of multiple degrees of freedom. In comparison, however, myoelectric control has been relatively unexplored as a potential human-machine interface for use with other consumer products. Those who have pursued this direction have mostly directly applied the knowledge from the body of prosthetics research, limiting its effectiveness. Consequently, there are many outstanding questions about the potential, robustness and intuitiveness of myoelectric control for consumer use. In this work, we will develop new human-machine interaction models and devices specifically designed for use with emerging consumer hands-free technologies. As the Innovation Research Chair at the Institute of Biomedical Engineering, a world-renowned leader and influencer in the field of myoelectric control, we will leverage lessons learned in developing the state-of-the-art in myoelectric control for prosthetics, while designing for this very different use-case. We will determine appropriate gesture sets that balance intuitiveness for the application with the information content and robustness of the control scheme and improve the training and learning process by considering the user as part of the control system. Finally, we will explore alternative sensors and the role of sensor fusion in developing versatile and robust hands-free human-machine interfaces. This work will present 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. The results of this program will produce foundational research that will broaden the applications for myoelectric control and inform the design of novel technologies. The development of intuitive, hands-free human machine interfaces will reduce the restrictions and risks associated with current work environments.