SBIR Phase I: Using electromyography (EMG) signals for advanced human-machine interface of amputees and bionic upper limb prosthetics

这个小型企业创新研究(SBIR)第一阶段项目的更广泛的影响/商业潜力是开发一种用于上肢假肢控制的新型人机界面和机器学习系统。更广泛地说，该项目旨在为人类-机器人控制开发全面的、非侵入性的神经接口。这一研究和发展加深了对如何破译来自周围神经及其支配的肌肉的运动控制信号的科学理解。该团队还致力于对高度可用的人类附着式机器人的技术和外形因素的理解。这个小型企业创新研究(SBIR)第一阶段项目旨在为那些遭受上肢肢体丧失痛苦的人提供解决方案，以解决当今假肢不发达的问题。主要的研究目标是发现一套能够实现实时人类-机器人控制的硬件、固件和软件。这项研究的范围从机器学习培训平台，到肌电图仪和声波仪输入系统，再到减少假肢相关污名的工业设计。该项目的技术成果包括深度学习培训方法的基础、成熟的信号传输方法以及确保实时信号处理的ARM频段设计。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to develop a novel human-machine interface and machine learning system for upper limb prosthetic control. More broadly, the project aims to develop comprehensive, non-invasive, neural interfaces for human-robotics control. This resesarch and development deepens scientific understanding of how to decipher motor control signals from peripheral nerves and their innervated muscles. The team also pursues technological and form factor understanding of highly-usable human-attached robotics. This Small Business Innovation Research (SBIR) Phase I project aims to deliver solutions to the problem of today’s underdeveloped prosthetics for those who suffer from upper extremity limb loss. The primary research objective is to discover a set of hardware, firmware, and software that enable real-time human-robotic controls. This research ranges from machine learning training platforms, to electromyography and sonomyography input systems, to industrial design that reduces the stigma associated with prostheses. Technical results of this project include the foundation for deep learning training approaches, mature signal transfer methodology, and arm band design that ensures real-time signal processing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.