Real-Time Processing of Cardiovascular Signals for Autonomous Care and Smart Helmet Applications
Abstract
The central aim of this effort will be to transition the current algorithms that have been developed in the Inan Lab for seismocardi,ogram (SCG) signal processing and machine learning to real-time implementation. The definition of real-time in this case will be <1,sec latency, with which the extracted data could be used for multiple applications of relevance to the Navy: for example, (1) interf,acing to autonomous care systems to provide blood volume status or cardiac output values that could be used as set points for fluid,infusion control loops; (2) detecting sudden decreases in cardiac output for pilots during high-G acceleration profiles such that fl,ight parameters could be accordingly adapted to prevent syncope from hypoxia and / or thigh cuff systems could be actuated to increa,se venous return; and (3) for providing decision support capabilities to Warfighters using the Smart Helmet with sensors measuring p,arameters such as cerebral blood flow or cardiac output and delivering feedback to mitigate environmental stressors or optimize perf,ormance. Specifically, this proposed effort builds on our existing technologies to address four research objectives: Base Objective,1: Develop real-time algorithms for extracting relevant timing features (aortic valve opening and closing) and blood volume decompen,sation status from datasets collected previously by the Inan Lab including SCG and photoplethysmogram (PPG) signals; Base Objective,2: Develop real-time algorithms for reducing external vibrations from the measured SCG and PPG signals; Optional Objective 3: Implem,ent real-time algorithms for blood volume decompensation status estimation and external vibration cancellation on embedded hardware,, incorporating mode-switching for minimizing power consumption; Optional Objective 4: Demonstrate wireless transfer of signals and b,lood volume status estimation to Google Glass using the mannikin and synthetic SCG / ECG generator previously developed in the prese,nce of external vibrations. This research program will result in optimized real-time algorithms for SCG and PPG signal processing, i,ncluding for extracting salient features for providing actionable information, reducing the impact of vibrations, and implementation, of these algorithms on hardware. While the initial impact will be on Warfighter health and protection, the technologies can have br,oad use in civilian applications as well. Approved for Public Release.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Apr 01, 2022
- Source ID
- N000142212325
Entities
People
- Ömer İnan
Organizations
- Georgia Tech Research Corporation
- Office of Naval Research
- United States Navy