Identification of Hypercapnia Through Voice Analysis and Associated Neurological and Performance Effects
Abstract
As the number of unidentified physiologic episodes (UPEs) in tactical aviation continue to increase, a need exists for early detection of these occurrences in flight to ensure operator safety. Hypercapnia is believed to be a significant contributor to UPEs as operators encounter breathing resistance from oxygen mask valves, breathing hoses, and regulators, which can lead to hypoventilation and carbon dioxide (CO2) retention. While current methods to detect hypercapnia exist, the ability to use this technology in the cockpit remains difficult due to environmental issues and interference from and with flight equipment. This investigation seeks to develop a noninvasive method to identify hypercapnia using a machine learning algorithm to detect changes in speech and breath features specific to an individual with excess arterial CO2.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 29, 2021
- Accession Number
- AD1170833
Entities
People
- Adrien Moucheboeuf
- Allison Bew
- Andrew Dorsey
- Anil Raj
- Arash Mahyari
- Ian Perera
- Jeffrey B. Phillips
- Madison C. Mcinnis
Organizations
- Florida Institute for Human and Machine Cognition