ABSTRACT

Real-time remote monitoring of physiological indicators and early intervention can save lives. These critical services require wearable technologies with strong predictive abilities, fast networks, and fast servers to extract insights from the collected data. Unfortunately, these technology components are inaccessible to hundreds of millions of people, specifically those living in areas with limited broadband connectivity and limited means to invest in local computing and communication infrastructure. We develop hardware and software for sustainable and efficient wearable edge intelligence in this project. We address fundamental accessibility and sustainability challenges of both wearable health monitoring devices and artificial intelligence services for under-served communities.

Our research, education, and outreach plans are anchored on a sustainability- and accessibility-focused view of computer systems research. Health services based on machine learning lean heavily on vast data stores, fast networks, and farms of Cloud servers, which are inaccessible to large parts of the world?s population. This effort's intellectual challenges lie in how to change hardware and software design to bring advanced machine learning services to unprivileged users who cannot depend on wireless or Cloud service providers for their well-being. Underlying this challenge are specific intellectual challenges in (i) lengthening the lifetime of wearable devices that perform biomedical signal acquisition and processing while trying to expand their computational and processing capabilities; (ii) performing more efficient, robust, and trustworthy machine learning in personal and edge computing devices outside the Cloud; and (iii) finding scalable and sustainable development and deployment models for distributed machine learning services, without the robustness and availability guarantees of Cloud datacenters. The project brings together four research teams with demonstrated and complementary expertise in wearable sensors and hardware, software, systems, and algorithms. Our recent research on reducing power consumption of edge sensors, transprecise computing, serverless computing, and network systems optimization lays the foundation and serves as a starting point for this research.

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.

Please report errors in award information by writing to: awardsearch@nsf.gov.