I-Corps: Assistive Context Aware Interface

Northeastern University, Boston MA

Investigators

Abstract

The broader impact/commercial potential of this I-Corps project is to help millions of individuals with chronic or acute disabilities leading to loss of communication and computer control abilities. The proposed assistive context aware interface (ACAI) will allow these individuals to regain the ability communicate with caregivers and families, and to control their environments, which will lead to increase in quality of life and in some cases improvement in received healthcare. Potential customers include close to 4 million people worldwide, with conditions such as spinal cord injuries, strokes, multiple sclerosis, amyotrophic lateral sclerosis, and traumatic brain injury. This project will pursue the commercialization of ACAI as a stand-alone computer interface with which individuals can use existing assistive technology, including augmentative and alternative communication solutions that targets individuals as customers, and its commercialization as part of a complete intensive care unit delirium assessment system that will be offered to hospitals for improved patient care. This I-Corps project will offer an infrastructure that supports rich contextual information exchange between physiological and behavioral sensors that capture human intent for the control of computer applications. The assistive context aware interface (ACAI) framework is based on Bayesian inference and information theoretic coding principles that ensure mathematical rigor in design in offering almost optimal speed-accuracy performance to the user. The framework is based on the human-in-the-loop cyber-physical systems design principles, which ensures a user-centric, modular and scalable design for assistive computer access using all physiological and behavioral signals that can be exploited by the users in their clinical conditions. ACAI unifies body and brain physiological signal processing in human intent inference. Convenient user customization will allow users to exploit multiple input modalities to control assistive computer applications, and promises an adaptive solution that can cater to their changing needs during treatment or disease progression.