A considerable amount of the population in the US and around the world suffers from a chronic sleep disorder. However, the majority of these are not diagnosed or treated. There is a vital need for new wearable technologies to increase the capacity of sleep researchers to make further advances in investigating sleep, understanding sleep pathologies, and to improve the ability of clinicians to reliably detect and treat sleep disorders. This award investigates the use of an artificial intelligence driven, reconfigurable sleep monitoring system to transform sleep research in the clinic and at-home. A sensor fusion strategy backed by artificial intelligence to ultra-miniaturize the sleep assessment instruments and explore novel sleep-related biomarker features have the transformative potential to invigorate sleep research for more efficient and accurate diagnosis and treatment of sleep disorders. Combining lower cost with better ergonomic comfort, and more efficient data analysis will pave the way for rapid translation, adoption, and effective deployment of these technologies for home-use in real-world settings. The research results from this award have the potential to positively influence the continuous monitoring instrumentation required for other chronic conditions such as heart diseases. This project enables several motivating opportunities for outreach and education including the use of technologies to interface sleep, learning new artificial intelligence and data analytics skills, analyzing sleep performance to unlock its mysteries, and the impact of sleep in the wellness and efficiency of society. Several such activities are planned with the products of this research project to reach out to younger generations, educators, other researchers in the field and public-at-large.
This award integrates two parallel efforts combining innovations in hardware and data analytics: 1) enabling an adaptable and reconfigurable embedded system platform in the form factors of an adhesive patch, and 2) developing state-of-the-art machine learning techniques incorporating the data-driven models necessary for improving sleep monitoring system resilience. The hardware system fuses multimodal wearable sensors, combining Near Infrared Spectroscopy (NIRS) with other traditional sleep related signal sensors, in skin conformable substrates, to collect data on multiple body locations. The efficacy of the system will be assessed in terms of improving conformability and flexibility, and reducing the system real estate and cost. The data analytics platform includes 1) signal processing to enable data-driven metrics for signal quality assessment for a given inference task, 2) inference models based on transfer learning techniques and diverse datasets for detection of sleep events and disorders using new sensing modalities, and 3) Bayesian Neural Network supported sensor selection for improving the resilience and adaptability of sleep sensor systems. The `adaptation' will take place by selecting the most resilient sensing configurations over design iterations and in real-time during operation. In addition to allowing a novel, artificial intelligence-driven and reconfigurable tool design for sleep research, this effort will also shed light into novel multimodal biomarkers assessed noninvasively in wearable form factors for detection of sleep stages and disorders.
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.
