Ear-worn Sensing for Healthcare Enabled by Cloud-based Machine Learning
Wearable medical devices are becoming a key part of modern smart healthcare systems. The concept of ear-based sensing is based on the fact that human ears are relatively close to the sources of many important physiological signals such as brain, eyes, and facial muscles. The objective of this research is to create a test bed for building and testing robust, efficient, and effective ear-worn sensing and actuation systems, called ‘earable systems’, for long-term and unobtrusive healthcare and brain-computer interactions. Tools such as complex algorithms, analytical models, and software libraries will be developed to sense individual head-based physiological signals and infer the wearer’s mental, physiological, and physical states from the ears. Applications include monitoring sleep, focus and performance in work and health-related settings. This test bed benefits significantly from the advancement of the AWS Internet of Things platform and other AWS services.
PI: Tam Vu