Pattern Recognition Based Myoelectric Control for Emerging Human-Machine Interfaces

基于模式识别的新兴人机界面肌电控制

• 批准号: RGPIN-2020-04776
• 负责人: Scheme, Erik
• 金额: $ 4.01万
• 依托单位: University of New Brunswick
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=740173
• 关键词: Pattern; Recognition; Based; Myoelectric; Control

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.

技术的可得性和实用性的迅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和普遍使用电脑和移动终端的危险，许多人都在争先恐后地寻找替代品。随着新兴的头戴式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不断增长。几十年来，肌电控制，即使用肌肉收缩期间产生的电信号来解码用户意图，已被用于控制截肢者的假肢。最近，技术、机器学习和用户培训的进步彻底改变了实现肌电控制的方式，使多个自由度的控制更加直观。然而，相比之下，肌电控制作为与其他消费产品一起使用的潜在人机界面相对未被探索。那些追求这一方向的人大多直接应用了假肢研究机构的知识，限制了其有效性。因此，对于消费者使用的肌电控制的潜力、鲁棒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，肌电控制领域的世界知名领导者和影响者，我们将利用在开发最先进的假肢肌电控制方面所吸取的经验教训，同时为这种非常不同的用例进行设计。我们将确定适当的手势集，平衡应用程序的直观性与控制方案的信息内容和鲁棒性，并通过将用户视为控制系统的一部分来改进训练和学习过程。最后，我们将探讨替代传感器和传感器融合在开发多功能和强大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。该计划的结果将产生基础研究，这将扩大肌电控制的应用，并为新技术的设计提供信息。开发直观的免提人机界面将减少与当前工作环境相关的限制和风险。