Wearable Biosensor Insomnia Profile Monitoring
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Solution Overview
Problem
Current biosensor technologies lack effective methods for characterizing and monitoring sleep disturbances and insomnia symptoms, failing to provide real-time feedback and personalized treatment pathways based on individual health indicators.
Innovation Solution
A method utilizing wearable biosensors to collect and analyze physiological data, deriving user-specific insomnia profiles, and suggesting interventions by correlating biosignals with health indicators, enabling real-time monitoring and adjustment of treatment pathways.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If wearable biosensors are used to collect physiological data, then real-time monitoring capability is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex monitoring task into distinct components: biosignal acquisition by wearable sensors, preprocessing of raw signals, extraction of health indicators, and generation of insomnia profiles. This segmentation allows each component to be optimized independently, managing complexity while maintaining real-time monitoring capability.
Solution Approach 2:
The patent introduces intermediate processing layers including signal preprocessing modules and health indicator extraction algorithms that act as intermediaries between raw biosensor data and final insomnia assessment. These intermediaries simplify the data processing pipeline by transforming complex physiological signals into meaningful health metrics.
2Measurement precision
If user-specific insomnia profiles are derived from biosignal data, then measurement precision of sleep disturbance detection is improved, but loss of time for model calibration increases
Solution Approach 1:
The system performs preliminary calibration by collecting biosignal data during an initial period when the user wears the device, automatically deriving their baseline insomnia profile without requiring manual intervention. This preliminary action establishes user-specific parameters in advance, enabling precise detection from the outset while minimizing calibration time through automated procedures.
Solution Approach 2:
The insomnia profile derivation process is fully automated, with the system self-calibrating by analyzing the user's own biosignal patterns without requiring external expert input or manual configuration. The algorithm automatically adapts to individual physiological characteristics, reducing calibration time while maintaining high measurement precision.
3Adaptability or versatility
If treatment pathways are personalized based on insomnia profiles, then adaptability of treatment to individual needs is improved, but device complexity for tracking and adjusting pathways increases
Solution Approach 1:
The treatment pathway system is designed to be dynamic rather than static, automatically adjusting treatment recommendations based on continuous monitoring of biosignal data and changes in the user's insomnia profile. The system adapts treatment intensity, duration, and type in real-time according to measured physiological responses, providing personalized care while managing complexity through automated feedback loops.
Solution Approach 2:
The system implements continuous feedback by monitoring biosignal data during treatment, comparing actual physiological responses against expected outcomes, and automatically adjusting the treatment pathway accordingly. This feedback mechanism enables personalized treatment adaptation without requiring complex manual intervention, as the system self-regulates based on measured effectiveness.
4Productivity
If biosignal data is continuously collected and analyzed, then productivity of sleep health monitoring is improved, but use of energy by the wearable device increases
Solution Approach 1:
The system employs periodic sampling of biosignal data rather than continuous recording, collecting measurements at strategically selected intervals sufficient to capture sleep disturbance patterns while allowing the device to enter low-power states between measurements. This periodic approach maintains monitoring productivity by capturing essential physiological changes while significantly reducing average energy consumption compared to continuous operation.
Solution Approach 2:
The system applies partial monitoring by focusing computational resources on analyzing only the most relevant biosignal parameters and time periods (such as sleep episodes) rather than processing all available data continuously. This selective analysis maintains high monitoring efficiency for critical sleep health metrics while reducing overall computational load and energy consumption during non-critical periods.
Data Source
AI summary
One variation of a method includes: accessing a first timeseries of biosignal data collected by a wearable device worn by a user during a first time period; deriving a first insomnia profile, representative of a set of health indicators exhibited by the user during the first time period, based on the first timeseries of biosignal data; and selecting a treatment pathway for implementation by the user based on the first insomnia profile. The method further includes: accessing a second timeseries of biosignal data collected for the user by the wearable device during a second time period; deriving a second insomnia profile, representative of the set of health indicators exhibited by the user during the second time period, based on the second timeseries of biosignal data; and characterizing effectiveness of the treatment pathway based on a difference between the first insomnia profile and the second insomnia profile.