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 todays 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.
