CELL Ultracomputing Platform for Metabolic and Cardiovascular Health Monitoring using Wearables
| Autor: | Bill Van Antwerp, Dan Mandutianu, Timothy L. Ruchti, Vijay Daggumati, Sandeep Gulati, John Hopple |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
business.industry
Information processing Wearable computer Cloud computing 02 engineering and technology computer.software_genre 03 medical and health sciences 0302 clinical medicine Human–computer interaction Asynchronous communication Virtual machine Analytics 020204 information systems Scalability 0202 electrical engineering electronic engineering information engineering Cellular network business computer 030217 neurology & neurosurgery |
| Zdroj: | 2021 IEEE Aerospace Conference (50100). |
| DOI: | 10.1109/aero50100.2021.9438200 |
| Popis: | Current processing systems are limited in their ability to provide holistic view of personalized health from wrist wearables and smart clothing, embedded with a mix of low and high fidelity - optical, MEMS, RF and other electronic biosensors. The mathematics of combining data from disparate sensors with asynchronous arrival events, episodic and continuous sampling, and varying performance due to confounders and physiological variability injects unique challenges in building predictive models and providing actionable, engaging information over long duration to the users. As wearables become more sophisticated, with new analytics added on a regular basis, the information processing challenge becomes harder. Our ultra-scalable CELL analytical platform is inspired by the scale of cellular networks found in nature - distributed information pathways and triggers in living organisms and creatures. CELL is a virtual machine implementing the data-flow architecture where “the network is the computer”. It integrates the communication and processing resources into a coherent model where computing is generally distributed between the wearable, smart phone and cloud. The paper will describe our CELL architecture in detail and discuss departures from traditional and emerging processing containers (such as Confluent, Kubernetes etc.) applied to analyzing health data from wearables. Our implementation and results using the CELL will be presented from multi-sensor devices with metabolic, cardiovascular and impedance biosensor examples for wellness and clinically relevant metrics; including implications for scalability and real-time fused analytics that combine data from multiple sensors. |
| Databáze: | OpenAIRE |
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