Pilot Respiration Pattern Detection for In-Flight Hypoxia Alerts
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Solution Overview
Problem
Existing systems fail to effectively detect the onset of physiological episodes in pilots during flight due to physiological issues such as hypoxia, hypocapnia, and hypothermia, which can lead to degraded performance and safety risks.
Innovation Solution
A system that processes raw physiological data from a biosensing garment, including respiration data, and aligns it with aircraft environment data to classify breath patterns and determine potential physiological episodes, issuing alerts when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physiological monitoring systems are implemented to detect episodes like hypoxia and hypocapnia, then pilot safety is improved, but system complexity and cost increase
Solution Approach 1:
The system segments the monitoring task into distinct functional modules: a biosensing garment for data acquisition, a processor for breath extraction and classification, and an alerting system for notifications. This modular segmentation reduces overall system complexity by making each component specialized and independently manageable while maintaining comprehensive monitoring capability for pilot safety.
Solution Approach 2:
The patent introduces an intermediary processing layer that extracts breath-level information from raw physiological data and classifies breath types before generating alerts. This intermediary layer acts as a mediator between the complex sensor array and the simple alert output, reducing the complexity burden on both ends while improving detection reliability through systematic breath pattern analysis.
2Measurement precision
If continuous physiological monitoring is performed during flight, then detection accuracy is improved, but data processing requirements and computational load increase
Solution Approach 1:
The system performs preliminary action by extracting individual breaths and classifying them into breath types before conducting the actual physiological episode detection. This preliminary processing organizes the continuous data stream into discrete, meaningful units (breath types), which reduces the computational load during the actual detection phase while maintaining high detection accuracy through pre-processed breath pattern recognition.
Solution Approach 2:
The system applies partial action by focusing computational resources on analyzing only the breath types that are relevant to detecting physiological episodes, rather than processing every raw data point equally. By classifying breaths into specific types and only flagging those that match episode patterns, the system achieves high detection accuracy while minimizing unnecessary computational expenditure.
Data Source
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AI summary
Systems and methods are provided for detecting physiological episodes (PE). Raw physiological data including raw respiration data is received from at least one sensor of a biosensing garment of a pilot. Individual breaths are extracted from the raw physiological data. Each individual breath has an associated breath pattern. Aircraft environment data associated with an aircraft is received. The individual breaths are aligned with the aircraft environment data in accordance with a breath timeline associated with the individual breaths is and an aircraft environment timeline associated with the aircraft environment data. Each individual breath is classified as one of a plurality of different breath types based on the associated breath pattern. A determination is made regarding whether the breath types associated with the individual breaths are associated with a PE profile based at least in part on the aircraft environment data. A PE alert is issued based on the determination.