Metabolic Rate Calculation Using Wearable Sensors
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Solution Overview
Problem
Current wearable technology inaccurately calculates metabolic rate due to reliance on single variables like heart rate, inadequate scientific models, and failure to account for individual variations, resulting in over 30% error in calorie expenditure estimates.
Innovation Solution
A system using non-intrusive wearable sensors trained with a gas exchange analyzer to capture specific body signatures, employing a respiratory-system mathematical model optimized by machine learning, allowing for accurate calculation of metabolic rate without obstructing the user and accounting for individual variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If heart rate measurement is used to calculate metabolic rate, then the calculation process is simple, but the accuracy of metabolic rate calculation deteriorates with over 30% error
Solution Approach 1:
The patent segments the single heart rate variable into multiple measurement dimensions including heart rate, breathing rate, step count, and demographic parameters. This segmentation allows the system to capture more aspects of metabolic activity while maintaining a relatively simple wearable device structure.
Solution Approach 2:
The patent changes from using a single parameter (heart rate) to using multiple parameters (heart rate, breathing rate, step count, age, gender, weight). This parameter expansion fundamentally improves measurement accuracy by capturing the complexity of metabolic processes that cannot be represented by a single variable.
2Device complexity
If a single variable model is used for metabolic rate calculation, then the model is simple, but it cannot account for individual variations and activity types
Solution Approach 1:
The patent collects and stores demographic information (age, gender, weight) and baseline physiological data before actual metabolic rate calculation. This preliminary data collection creates a personalized baseline for each user, enabling the model to adapt to individual variations without increasing the complexity of the calculation model itself.
Solution Approach 2:
The patent adds demographic dimensions (age, gender, weight) and activity dimensions (step count, breathing rate) to the traditional heart rate model. This dimensional expansion allows the model to account for individual variations and different activity types while maintaining a relatively simple regression-based structure.
3Ease of manufacture
If empirical regression from lab studies is used, then the model can be generated, but it fails when conditions deviate from nominal test conditions
Solution Approach 1:
The patent transitions from a static regression model based on fixed lab conditions to a dynamic model that continuously adapts to varying conditions. By incorporating real-time measurements of multiple physiological parameters and using them in a multi-variable regression equation, the model can adjust to different activity types, intensities, and environmental conditions while maintaining reliability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables precise, user-specific metabolic rate calculations during various activities, reducing errors and providing real-time data on energy expenditure, body composition, and optimal training strategies.
Implementation Method 1
a gas exchange analyzer to measure oxygen consumption by the body
Implementation Method 2
one or more primary sensors configured to measure concentration of oxygen in blood
Implementation Method 3
one or more secondary sensors configured to measure concentration of oxygen in lung
Data Source
AI summary
The present disclosure is directed to a method and system of detecting and calculating a lung map or lung properties of an individual user from nonintrusive wearable sensors to generate a metabolic rate of the user as well as several other key body and performance metrics.