Breathing Therapy Audio Feedback Using ML Interruption Scoring
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Solution Overview
Problem
Existing breathing therapy technologies are inefficient and ineffective in optimizing breathing exercises, particularly for patients with conditions like atelectasis, leading to complications such as pneumonia due to improper breathing exercises or lack of incentive spirometry, which can result in serious illness or death.
Innovation Solution
The use of an interruption score generation machine learning model to analyze observed inspiration-expiration patterns, comparing them to expected patterns to determine splinting activity, generating predicted interruption scores, and mapping these patterns to inferred musical notes for audio feedback, thereby optimizing breathing therapy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional breathing therapy methods are used, then simplicity and ease of operation are maintained, but therapeutic effectiveness and patient outcomes deteriorate due to inability to optimize breathing exercises
Solution Approach 1:
The patent replaces traditional mechanical breathing therapy devices with an audio-based system that uses musical patterns and machine learning models to guide breathing exercises. The system substitutes complex mechanical monitoring and feedback mechanisms with audio signal processing and computational intelligence, achieving optimized therapy while reducing physical device complexity
Solution Approach 2:
The system employs machine learning models that automatically analyze breathing patterns and generate optimized audio feedback without requiring manual intervention or extensive training operations. The interruption score generation model self-adjusts to provide personalized breathing guidance, eliminating the need for complex external calibration and setup procedures
2Measurement precision
If extensive training operations are performed to optimize breathing therapy, then measurement precision and therapeutic accuracy are improved, but computational efficiency and processing time deteriorate
Solution Approach 1:
The system performs preliminary analysis by pre-processing breathing data into structured formats and pre-training the interruption score generation model on representative datasets. This preliminary preparation enables the model to quickly process real-time breathing patterns during therapy sessions without requiring extensive computational resources during actual operation
Solution Approach 2:
The patent divides the breathing analysis into discrete segments corresponding to individual breath cycles, each associated with specific musical notes. This segmentation allows the machine learning model to process breathing patterns in manageable units, improving computational efficiency while maintaining measurement precision through focused analysis of each breath segment
3Object-affected harmful factors
If traditional incentive spirometry is used, then device simplicity is maintained, but patient safety and complication prevention deteriorate due to improper breathing exercises
Solution Approach 1:
The system implements continuous feedback by analyzing breathing patterns in real-time and providing immediate audio guidance through musical patterns. The machine learning model monitors each breath segment and adjusts the audio feedback to ensure proper breathing technique, preventing improper exercises and reducing complication risks through proactive correction
Solution Approach 2:
The patent introduces audio signals and musical patterns as an intermediary between the patient and the breathing therapy process. This intermediary provides gentle guidance and correction without requiring direct mechanical intervention or complex monitoring hardware, reducing complication risks through non-invasive real-time feedback
Data Source
AI summary
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing optimized breathing therapy. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform optimized breathing therapy using at least one of interruption score generation machine learning models, observed inspiration-expiration pattern, expected inspiration-expiration patterns, expected musical patterns, and inferred musical patterns.