Physiological Parameter Estimation Using Time-Dependent Functions
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Solution Overview
Problem
Conventional methods and apparatuses fail to accurately estimate resting state parameters of a being based on known relationships between resting and current states, and lack the capability to reliably measure dynamically changing relationships in everyday life activities.
Innovation Solution
A method and apparatus that utilize a time-dependent relationship function, derived from circadian rhythms and environmental factors, to estimate physiological parameters by measuring and processing data from physiological and environmental sensors, adjusting for heightened activity and individual variations, to determine resting or other selected state parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to measure physiological parameters, then measurement simplicity is maintained, but measurement precision deteriorates due to inability to account for time-dependent and environmental influences
Solution Approach 1:
The system performs preliminary measurements of physiological parameters at multiple time points throughout the day to establish baseline relationships between current state and resting state parameters. These preliminary actions create a historical database that enables accurate resting state estimation without requiring complex real-time measurements during rest periods.
Solution Approach 2:
The system continuously monitors physiological parameters and uses feedback loops to adjust the time-dependent relationship functions. The measured current state parameters are fed back into the model to refine the estimation of resting state parameters, improving measurement precision through iterative optimization.
2Reliability
If simple time-based models are used, then device complexity is reduced, but reliability deteriorates due to inability to account for individual variations and environmental factors
Solution Approach 1:
The system transitions from static time-based models to dynamic relationship functions that adapt to individual variations. The time-dependent relationship functions are continuously updated based on measured data from each subject, allowing the model to evolve and improve reliability while accounting for personal physiological patterns and environmental influences.
Solution Approach 2:
The system changes the parameters of the relationship functions based on environmental conditions and individual subject characteristics. By adjusting the relationship parameters dynamically rather than using fixed models, the system achieves higher reliability in estimating resting state parameters across different conditions and individuals.
3Measurement precision
If measurements are taken at multiple time points to establish relationships, then measurement precision improves, but loss of time increases due to extended measurement periods
Solution Approach 1:
The system performs measurements at multiple time points (excessive action) to establish comprehensive time-dependent relationships, but uses these relationships to eliminate the need for continuous monitoring. The initial extensive measurement period enables subsequent accurate estimations without requiring constant measurements, ultimately reducing total time investment.
Solution Approach 2:
The system implements periodic measurements at strategically selected time points throughout the day to capture circadian rhythm patterns. This periodic sampling approach establishes accurate time-dependent relationships more efficiently than continuous monitoring, reducing the total measurement time while maintaining precision.
4Measurement precision
If activity level adjustments are incorporated into the model, then measurement precision improves for resting state estimation, but device complexity increases due to activity detection requirements
Solution Approach 1:
The system uses multi-functional sensors that can detect both physiological parameters and activity conditions with the same hardware components. By designing sensors that serve multiple purposes, the system achieves activity-level adjusted measurements without proportionally increasing device complexity.
Solution Approach 2:
The system merges the detection of physiological parameters and activity conditions into a unified measurement framework. By combining these functions in the data processing layer rather than requiring separate hardware systems, the achievement of improved measurement precision comes with minimal additional complexity.
Data Source
AI summary
A method of determining a value of a physiological parameter for a subject at a selected state includes obtaining, via a device located a distance from the subject, a value of the physiological parameter of the subject at a particular time-of-day, and applying a time-dependent relationship function to the obtained physiological parameter value via a processor to determine a value of the physiological parameter at the selected state.