Personalized Breath Training with Sleep-Triggered Session Control
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Solution Overview
Problem
Existing breath training regimens for sleep disordered breathing (SDB) require long-term continuous compliance, which many individuals find challenging, leading to early termination and loss of benefits, despite significant short-term improvements.
Innovation Solution
A portable smart device with sensors and processors breaks down breath training into multiple shorter phases, monitoring sleep patterns to adjust training schedules and intensity based on individual compliance and neuroplasticity principles, ensuring long-term retention of breathing control modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If breath training requires long-term continuous compliance, then breathing control modifications are achieved, but trainee compliance deteriorates leading to early termination
Solution Approach 1:
The breath training program is divided into multiple phases (initial phase, intermediate phase, maintenance phase) with varying intensity and duration requirements. This segmentation allows trainees to progress through manageable stages rather than facing a single demanding continuous regimen, thereby maintaining compliance while achieving long-term breathing control modifications.
Solution Approach 2:
The system implements periodic training sessions with scheduled intervals between phases. Trainees perform breath training during specific periods (e.g., 2-3 times daily during initial phase, then transitioning to less frequent sessions) with break periods in between. This periodic structure sustains neuroplastic changes while accommodating trainee compliance limitations.
2Productivity
If breath training intensity is increased to achieve faster results, then short-term improvements are achieved, but trainee ability to maintain compliance deteriorates
Solution Approach 1:
The training intensity and duration are dynamically adjusted based on trainee progress and phase progression. The system automatically modifies parameters such as breath hold duration, number of repetitions, and session frequency. This dynamic adaptation allows for intensive training when trainees can comply, while automatically reducing demands when compliance becomes difficult, thereby maintaining both productivity and compliance.
Solution Approach 2:
The system changes multiple parameters simultaneously including training frequency (from 2-3 times daily to once daily to weekly), session duration, and breath hold intensity across different phases. These parameter changes enable the program to deliver high productivity during intensive phases while ensuring long-term compliance through progressive de-intensification in maintenance phases.
3Reliability
If breath training sessions are extended to ensure neuroplastic changes, then breathing control modifications are achieved, but training duration increases leading to termination
Solution Approach 1:
The system employs periodic intensive training bursts followed by maintenance periods. During initial and intermediate phases, trainees perform multiple short sessions (2-3 minutes each, 2-3 times daily) that accumulate sufficient neuroplastic stimulus. During maintenance phases, training frequency reduces to once daily or weekly while maintaining session quality. This periodic structure achieves necessary neuroplastic changes without requiring indefinitely extended training duration.
Solution Approach 2:
The system maintains continuous neuroplastic stimulus through repeated short sessions rather than single long sessions. By distributing training across multiple brief periods throughout the day during intensive phases, the system ensures cumulative neuroplastic effect equivalent to or greater than fewer longer sessions, while significantly improving trainee compliance and reducing overall program burden.
Data Source
AI summary
A portable smart device including a coupled sensor sensing a physiological parameter of a user, and a processor. The processor is configured to perform a breath training session by instructing the user to breath in a specified manner, receiving physiological data from the sensor, the physiological data representing the sensed physiological parameter of a user, determining a breathing quality based on the physiological data from the sensor, and stopping the breath training when the breathing quality reaches a breath training session stopping threshold. The processor is further configured to evaluate initiation of a further breath training session by receiving the physiological data from the sensor while the user is sleeping, and repeating the breath training session in response to the physiological data from the sensor indicating that the breathing quality reaches a breath training session starting threshold.


