Wearable Menstrual Cycle Phase Detection From Temperature Patterns
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Solution Overview
Problem
Conventional menstrual cycle tracking devices lack the ability to provide robust prediction and insight due to reliance on single temperature data points, sensitivity to user movement, and lack of integration with other physiological, behavioral, or contextual inputs.
Innovation Solution
A wearable device that continuously collects temperature data and other physiological parameters, identifies morphological features in time series data to detect menstrual cycle phases, and uses machine learning to predict future cycles, providing personalized insights and recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single temperature data points are used for cycle tracking, then device simplicity is maintained, but measurement precision and reliability deteriorate
Solution Approach 1:
The patent segments the temperature measurement process into multiple discrete data points taken throughout the day, rather than relying on a single measurement. This segmentation allows the system to capture temperature variations and trends, improving cycle detection accuracy while maintaining relative device simplicity through the use of multiple independent measurements.
Solution Approach 2:
The system performs preliminary temperature measurements and morphological feature identification before making cycle phase determinations. By pre-processing the temperature data to identify patterns and features, the system improves measurement precision while keeping the overall device design relatively simple.
2Device complexity
If conventional temperature detection methods are used, then device complexity is low, but reliability of cycle detection deteriorates due to sensitivity to user movement
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors temperature data, compares it against established patterns and morphological features, and adjusts cycle phase predictions accordingly. This feedback loop improves reliability by accounting for variations caused by user movement and other confounding factors.
Solution Approach 2:
The system changes the parameters used for detection by incorporating multiple temperature data points and analyzing morphological features rather than relying on a single temperature threshold. This parameter change approach improves reliability while maintaining relatively simple device architecture.
3Ease of operation
If only temperature data is collected, then ease of operation is maintained, but loss of information increases due to lack of physiological and contextual inputs
Solution Approach 1:
The patent makes the wearable device multi-functional by integrating not only temperature sensing but also other physiological sensors and contextual data collection capabilities. This universality allows the device to gather comprehensive information without significantly complicating user operation, as all data collection occurs automatically in the background.
Data Source
AI summary
Methods, systems, and devices for menstrual cycle phase identifier are described. A system may be configured to receive physiological data 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 taken over the plurality of days where the time series includes a plurality of menstrual cycles for the user. The system may then identify morphological features in the time series and identify menstrual cycle phases in the time series based on the morphological features. The system may cause the graphical user interface to display the identified menstrual cycle phases.