Warfighter Task Termination Due to Breathing Mechanics and Compensation Paradigms
Abstract
Warfighters rely on their internal sensors to determine respiratory distress. Primarily, these sensations are the result of sensory,activation from subcortical and cortical neuro-pathways. Respiratory environmental changes can affect the way this well-balanced, co,ordinated system drives respiratory control. Extreme changes in oxygen levels, humidity indexes, and added load of breathing can ind,uce dyspnea. These sensations can range in severity and manifest in various forms.These negative side effects can prevent individual,s from performing their required tasks. As such, warfighters have been advised to wear specialized equipment to ameliorate these ave,rsive sensations.Unfortunately, symptoms of ?air hunger? may persist and prolonged exposure can induce a continual sense of fear or,anxiety. These states prevent individuals from performing their job duties and can eventually result in catastrophic human error. Cr,ucially, the chemical, mechanical and neurological mechanisms contributing to dyspnea are not fully understood. Our fundamental goal, is to develop and apply advanced mechanical breathing models that will experimentally link the onset of the neuro and chemical syst,ems for breathing to improv,xtend their mission time.Respiratory-related sensory receptors are located in the upper airways, lower airways, and parts of the eso,phagus. Mechanical and chemical stimuli generate afferent signals that produce changes in respiratory function. The function of thes,e afferent signals and their role in respiratory control highlight the necessity for understanding compensatory breathing and its bi,omarkers. Thus, making the necessity for new respiratory diagnostics, monitoring tools, and models for respiration pivotal to improv,e our understanding of the work of breathing (WoB) and quantifying compensatory breathing patterns. However, no solution has ever be,en generated to decompose instantaneous airflow into a meaningful way to generate a model that enables interpretation and prediction, of task termination. The fundamental hypothesis guiding this proposal is that instantaneous respiratory airflow velocities reflect,respiratory compensation paradigms and WoB that lead to dyspnea and task termination. Our primary goal is to develop quantitative st,rategies for discriminative breathing biomarkers and link them to a range of dyspneic physiological episodes that leads to task term,ination via a machine learning sensor fusion approach (NAPS Fusion). We propose three specific aims. Aim 1: Characterize instantaneo,us flow breathing patterns to quantify WoB. We have built a preliminary, non-sinusoidal WoB model where we have decomposed instantan,eous airflow. Further analysis would investigate wavelet decomposition and compare methods in deterministic force models. Aim 2: Cha,racterize breathing patterns to predict task termination and performance using resistive loads and chest wall restriction as an anal,og to diving. Analysis has demonstrated different modalities of breathing complexity. Further investigation would link how we can pr,thing under respiratory loads. Preliminary data suggests differences between normal and resistive load breathing. Further inquiry co,uld determine unique bio markers and compensatory strategies for varied breathing patterns. This proposed research would change the,way we quantify and examine breathing and develop new life support systems. We will also predict a warfighter?s trajectory and provi,de real-time feedback for task failure prediction, changes to their compensation paradigm, and the need for escalating respiratory s,upport..
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Oct 07, 2022
- Source ID
- N000142212653
Entities
People
- Nicholas Napoli
Organizations
- Office of Naval Research
- United States Navy
- University of Florida