Wearable Temperature Monitoring for Postpartum Depression Prediction
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Solution Overview
Problem
Current wearable devices lack the ability to provide robust prediction and insight into women's health cycles, pregnancy patterns, and postpartum periods due to reliance on single temperature data points and the absence of comprehensive physiological and contextual inputs.
Innovation Solution
A system that utilizes continuous temperature measurement from wearable devices to identify morphological features in temperature data, combining it with other physiological parameters like heart rate and sleep patterns, to detect and predict mental or emotional distress during prenatal, perinatal, or postnatal periods, using machine learning models and real-time analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single temperature data points are used for health tracking, then device simplicity is maintained, but prediction accuracy and robustness deteriorate
Solution Approach 1:
The patent implements continuous temperature monitoring throughout the day and night, collecting multiple temperature data points over extended periods rather than relying on single discrete measurements. This continuous data collection enables robust prediction of women's health events by capturing temperature trends and patterns, thereby improving prediction accuracy while maintaining device simplicity through automated continuous measurement.
Solution Approach 2:
The patent combines temperature data with multiple other physiological parameters including heart rate, respiratory rate, sleep patterns, and activity levels. By merging these diverse data sources into a comprehensive health monitoring system, the patent achieves robust multi-parameter prediction accuracy while the integrated approach maintains overall system simplicity through unified data processing.
2Reliability
If comprehensive physiological and contextual inputs are collected, then prediction robustness improves, but device complexity and data processing requirements worsen
Solution Approach 1:
The patent implements a multi-functional wearable device that simultaneously monitors temperature, heart rate, respiratory rate, sleep patterns, and activity levels using integrated sensors. This universal health monitoring platform collects comprehensive physiological data through a single device, improving prediction robustness while avoiding the complexity of multiple separate devices through unified multi-functional design.
Solution Approach 2:
The system automatically processes and analyzes collected physiological data using machine learning algorithms to predict health events without requiring manual user intervention. The automated data processing and analysis reduce the operational complexity burden on users, enabling comprehensive data collection while maintaining ease of use through self-service automated prediction and notification features.
3Loss of time
If continuous monitoring is implemented, then early detection capability improves, but energy consumption and measurement complexity worsen
Solution Approach 1:
The patent implements periodic sampling of physiological parameters at optimized intervals rather than truly continuous monitoring. Temperature is monitored continuously during sleep periods when health events are most likely to occur, while other parameters are sampled at periodic intervals during active periods. This periodic monitoring strategy enables early detection of health events while significantly reducing energy consumption compared to constant high-rate sampling.
Solution Approach 2:
The system establishes baseline physiological profiles for each user through initial continuous monitoring periods, then uses these pre-established baselines to detect deviations indicating potential health events. This preliminary action of baseline establishment enables later detection with reduced monitoring intensity, achieving early detection capability while conserving energy during ongoing monitoring through comparison against pre-stored reference patterns.
Data Source
AI summary
Methods, systems, and devices for prenatal, perinatal, or postnatal mental or emotional distress identification and prediction are described. A system may be configured to receive physiological data associated with a user that is experiencing a prenatal, perinatal, or postnatal period of pregnancy 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 identify that the temperature values deviate from a prenatal, perinatal, or postnatal baseline of temperature values for the user and detect an indication of one or more conditions of mental or emotional distress experienced during the prenatal, perinatal, or postnatal period. The system may generate a message for display on a graphical user interface on a user device that indicates the indication of the one or more conditions of mental or emotional distress.