This Small Business Innovation Research Phase II project will provide a novel assistive technology to the neuromuscular market. Nationwide, more than 20,000 Americans suffer from ALS, a progressive neurodegenerative disease. While augmentative and alternative communication (AAC) tools exist to aid these patients to control computers and other devices, current solutions all have significant shortcomings for users with extremely limited movement or speaking ability. We have developed non-invasive neuromuscular sensing technology which detects small, intentional movements of muscles, and allows micro-movements to be transmitted wirelessly to devices such as computers. While developed for ALS patients, this technology can be repurposed for other markets (e.g. enterprise augmented-reality, consumer electronics, computer gaming) with minimal alterations. The interest of large technology companies in human-computer interaction (HCI) demonstrates demand for nonfatiguing, silent, hands-free input methods. The company plans for sales to the ALS market through channel partners, and for miniaturizing the technology and licensing circuit design and software for other markets, to achieve adoption by hundreds of millions of users. This pathway enables worldwide distribution to ALS users through partner companies.
The intellectual merit of this project results from an innovative approach to improve augmentative and alternative communication (AAC) usability for individuals, using a proprietary hardware/software neuromuscular human-computer interface (HCI) system. The device to-be-developed detects and transmits skin-surface electromyography/electroneurography (EMG/ENG) voltage signals. Phase II research objectives are to: 1) develop a software/firmware Machine Learning (ML) platform to enhance the existing device, to allow robust detection/classification of EMG/ENG signals (neuromuscular activation data) as computer commands, and 2) conduct user testing of a commercial AAC integration of the device, in a task-based (e.g. web-browsing) protocol for ALS patients. Testing in Objective 2 will validate accuracy of the ML models developed as part of Objective 1 (with data collected in human subjects). Objective 2 testing will include measurement of accuracy/error rates and performance-time, for participants with ALS interacting with AAC tools including integration of the device. It is anticipated that software/firmware developments and other enhancements in Phase II will improve accuracy and latency of the device, in comparison to models produced in Phase I, and show usability effect in real-world user testing as part of a full AAC solution to improve communication capabilities for ALS patients.
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.
