Hydration Monitoring via Heart Rate Signal Classification
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Solution Overview
Problem
Current methods for non-invasively monitoring hydration status are inconvenient, invasive, costly, or unreliable, especially in detecting low fluid losses, which can lead to heat-related injuries in athletes and vulnerable populations.
Innovation Solution
A computer-implemented method using a system comprising a heart rate sensor, an orientation sensor, and a data management device that extracts features from heart rate signals and classifies hydration status through machine learning, allowing for quantification of hydration changes in response to posture changes, with features including frequency domain representations and statistical descriptors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional hydration monitoring methods (body weight, urine specific gravity, blood plasma levels, bioelectrical impedance) are used, then hydration status can be measured, but the methods are inconvenient, invasive, costly, or location impeded
Solution Approach 1:
The patent replaces mechanical/invasive measurement systems (scales, urine tests, blood draws, bioelectrical impedance devices) with an optical/electrical sensing system using a heart rate sensor to detect hydration status through physiological signal analysis
Solution Approach 2:
The system uses the subject's own physiological signals (heart rate) to self-diagnose hydration status without requiring external intervention, invasive procedures, or specialized equipment access
2Measurement precision
If steady-state orthostatic heart rate measurements are used to detect fluid loss, then some hydration information can be obtained, but the method does not allow unobtrusive monitoring and fails to capture heart rate transients during posture changes
Solution Approach 1:
The patent transitions from static steady-state measurements to dynamic continuous monitoring that captures heart rate transients during posture changes, allowing the system to adapt to various activity levels and environmental conditions
Solution Approach 2:
The system continuously monitors heart rate signals without interruption, capturing both steady-state and transient phases, thereby providing uninterrupted hydration status information throughout the monitoring period
3Measurement precision
If steady-state orthostatic heart rate measurements are used, then moderate to large fluid losses can be detected, but the method is unreliable for detecting low fluid losses
Solution Approach 1:
The patent segments the heart rate signal into multiple frequency components using Fast Fourier Transform, allowing detection of subtle changes in different frequency bands that correspond to different levels of fluid loss, thereby improving sensitivity for low fluid losses
4Measurement precision
If machine learning classification with frequency domain features is used, then hydration status can be quantified with high accuracy, but the system complexity increases
Solution Approach 1:
The system pre-processes the heart rate signal by extracting frequency domain features and constructing feature vectors before classification, preparing the data in advance to simplify the machine learning classification process and improve computational efficiency
Data Source
AI summary
A computer-implemented method is presented for quantifying hydration in a subject. The method includes: receiving a heart rate signal indicative of heart rate of the subject; extracting features from the heart rate signal, where one or more of the extracted features include a frequency domain representation of the heart rate signal; constructing a feature vector from the extracted features; and quantifying hydration status of the subject as a percent of body weight of the subject by classifying the feature vector using machine learning, where percentages of body weight are expressed in increments on the order of one percent or less.