Neural Audio Analysis for Automatic Breathing Cycle Counting
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Solution Overview
Problem
Existing solutions for counting breathing cycles during exercises are cumbersome and distract users, reducing exercise effectiveness, and lack flexibility for personal adjustment.
Innovation Solution
A neural network-based system that automatically counts breathing cycles by analyzing audio streams, allowing users to focus on their exercises and adjust parameters without manual counting, using devices like laptops, smartphones, or remote controls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually count breathing cycles during exercises, then they can track their progress, but it deflects their attention from the breathing process and reduces exercise effectiveness
Solution Approach 1:
The system uses the user's own audio input (breathing sounds) to automatically count breathing cycles, eliminating the need for manual counting. The audio processing system serves itself by extracting breathing cycle information directly from the recorded audio stream, freeing the user to focus entirely on the breathing exercise without distraction.
Solution Approach 2:
The patent replaces the mechanical/manual counting process with an automated audio analysis system. Instead of relying on human cognitive resources to track and count breathing cycles, the system uses audio recording and processing to automatically detect and count breathing events, substituting human mental effort with technological automation.
2Reliability
If users follow pre-recorded videos or animations for breathing exercises, then they can synchronize their pace, but they must constantly adapt to a fixed rhythm that may not suit them
Solution Approach 1:
The system dynamically adjusts the breathing exercise rhythm based on real-time detection of the user's actual breathing rate. Instead of using a fixed pre-programmed rhythm, the system continuously monitors the user's breathing through audio analysis and adapts the exercise pace to match the user's natural breathing pattern, providing both synchronization and adaptability.
Solution Approach 2:
The system implements real-time feedback by continuously monitoring the user's breathing sounds and using this information to adjust the exercise rhythm. The audio input provides continuous feedback about the user's breathing state, which the system uses to dynamically modify the exercise parameters, creating a closed-loop control system that adapts to the user's needs.
3Measurement precision
If medical technology solutions are used for breathing analysis, then diagnostic accuracy is improved, but they are difficult to use and commercially inaccessible for average users
Solution Approach 1:
The patent uses inexpensive, widely available consumer devices (smartphone microphones, standard audio recording equipment) instead of expensive medical-grade sensors. The system leverages the audio capabilities of everyday devices to achieve sufficient breathing analysis accuracy, making the technology accessible to average users without requiring specialized medical equipment.
Solution Approach 2:
The system is designed as a self-service consumer application that users can independently operate without requiring medical professionals or specialized training. The automated audio analysis and breathing cycle detection enable users to perform and monitor their own breathing exercises, eliminating the need for medical practitioner involvement while maintaining measurement accuracy.
Data Source
AI summary
An embodiment of a device for automatically counting a user's breathing cycles when the user performs breathing exercises includes at least: input/output devices as a microphone and a display, central and graphic processors, a memory with machine-readable instructions and an installed user application for automatically counting breathing cycles, as well as a database, a training sample, a machine learning algorithm, and a neural network.