The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to bring to market a platform for monitoring and management of sleep disorders that is cost effective, easy to use for multiple nights in the home environment and delivers clinically valid and actionable results. The many consumer devices that claim to provide sleep quality data are not clinically relevant or accurate and are not accepted by healthcare providers. On the other hand, the clinical state-of-the-art is a single night, expensive, highly obtrusive and uncomfortable procedure at a sleep laboratory that only works for detection of moderate/severe sleep apnea. Awarables is dedicated to providing devices and technologies that can be validated by institutions specializing in sleep medicine and approved by the FDA. Starting with the pediatric population, including children with ADHD and Autism, we are bringing the ability to monitor and improve sleep, an integral part of every person's overall health and well-being, in populations suffering from insomnia (30M+), apnea (18M+), and other sleep disorders that require longer term home monitoring. As we progress through a landscape at the intersection of consumer applications and mobile healthcare, Awarables will challenge the existence of archaic sleep laboratories.
The proposed project addresses the research and development challenge of monitoring sleep quantity and quality i.e. sleep stages and other clinical metrics, and detecting critical sleep events such as apnea events per hour, snoring, and posture for multiple nights in the home environment. This entails miniature hardware of the size of a quarter instead of machinery-sized single-night sleep laboratory equipment, while providing the clinically validated analytics provided in the sleep laboratory. A comprehensive system comprising hardware, analytics, mobile user app, and clinician report will be developed leveraging a multi-disciplinary team of hardware and software engineering, and medical researchers and sleep clinicians. The detection of apnea events using a multi-sensor, multi-method approach is also proposed on re-engineered Awarables' hardware housing EKG/heart-rate and acoustic signals. The methods to be used include sensor fusion and intelligent classification algorithms, such as neural networks, that leverage both time and frequency domain features. The absence of such a product in the industry indicates the inability of existing competing IP to solve the problem at robust product level. The apnea detection and improved patent-pending sleep staging enhancements in this project will also be statistically validated on data from public clinical trial databases.
