Wearable Temperature Monitoring for Early Labor Onset Prediction
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Solution Overview
Problem
Conventional cycle detection techniques in wearable devices are deficient in providing robust prediction and insight into women's health cycles and pregnancy patterns due to reliance on single temperature data points, lack of continuous temperature measurement, and insufficient integration of other physiological and contextual inputs.
Innovation Solution
A wearable device that continuously measures temperature and other physiological parameters, such as heart rate and arterial blood flow, analyzes time series data to identify morphological features, and uses machine learning to predict labor onset and birth by detecting deviations from a pregnancy baseline.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional cycle detection techniques use single temperature data points, then device complexity is reduced, but measurement precision and prediction reliability deteriorate
Solution Approach 1:
The patent implements continuous temperature measurement throughout the day and night using wearable devices, replacing single data point collection. This continuous monitoring captures temperature trends, diurnal variations, and subtle changes that indicate labor onset, significantly improving measurement precision and prediction reliability without requiring complex manual intervention.
Solution Approach 2:
The system automatically collects, stores, and analyzes temperature data without requiring manual user input. The wearable device continuously monitors physiological parameters and the system autonomously identifies morphological features and predicts labor onset, reducing the burden on users while maintaining high measurement precision.
2Reliability
If continuous temperature measurement is implemented, then prediction reliability is improved, but use of energy and device complexity increase
Solution Approach 1:
The system performs temperature measurements at multiple time points throughout the day and night, capturing diurnal variations and identifying patterns. This periodic sampling strategy improves prediction reliability by analyzing temperature trends over time while managing energy consumption through structured measurement intervals rather than continuous high-power monitoring.
Solution Approach 2:
The system analyzes temperature data in real-time, identifies morphological features indicative of labor onset, and provides feedback to users through notifications. This feedback mechanism enables early prediction and preparation while optimizing energy usage by processing data only when significant changes are detected, rather than continuously analyzing all measurements.
3Reliability
If multiple physiological parameters are integrated, then prediction reliability is improved, but device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The wearable device is designed to simultaneously measure multiple physiological parameters including temperature, heart rate, and other indicators through integrated sensors. This multi-functional approach improves prediction reliability by capturing comprehensive physiological data while avoiding the need for separate specialized devices for each measurement.
Solution Approach 2:
The system combines multiple physiological measurements and contextual information into a unified analysis framework. By merging temperature data with heart rate and other parameters, the system identifies complex patterns and morphological features that indicate labor onset, improving prediction reliability through holistic data integration.
Data Source
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AI summary
Methods, systems, and devices for labor onset and birth identification and prediction are described. A system may be configured to receive physiological data associated with a user that is pregnant and collected over a plurality of days, where the physiological data includes at least temperature data. Additionally, the system may be configured to determine a time series of temperature values. The system may then calculate a pregnancy baseline temperature slope for the user and identify that the temperature slope deviates from the pregnancy baseline temperature slope for the user. The system may detect an indication of a labor onset of the user and generate a message for display on a graphical user interface on a user device that indicates the indication of the labor onset.