Wearable Temperature Profiling for Menstrual Cycle Onset Prediction
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Solution Overview
Problem
Conventional wearable devices lack the ability to accurately predict menstrual cycle onset due to insufficient context from single temperature data points and the inability to collect comprehensive physiological, behavioral, or contextual inputs, leading to inaccurate cycle tracking and a lack of robust prediction capabilities.
Innovation Solution
Wearable devices, such as rings, continuously collect physiological data using multiple LEDs to measure temperature and other parameters, fitting the data to trigonometric or polynomial functions to estimate menstrual cycle onset, and provide early warnings through graphical user interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional wearable devices use single temperature data points for cycle tracking, then device simplicity is maintained, but prediction accuracy deteriorates
Solution Approach 1:
The patent segments the temperature measurement process into multiple discrete temperature points taken throughout the day (e.g., morning, afternoon, evening). Each temperature point is independently measured and recorded, allowing the system to capture temperature variations across different times without requiring a complex continuous measurement system. This segmentation enables accurate prediction while maintaining relatively simple device architecture.
Solution Approach 2:
The patent adds the time dimension to temperature measurements by collecting multiple temperature readings at different times of day. Instead of a single temperature point, the system measures temperature across multiple time points, creating a temporal profile that significantly improves prediction accuracy. This dimensional expansion transforms the data from a single scalar value to a time-series dataset.
2Reliability
If wearable devices collect comprehensive physiological, behavioral, and contextual inputs, then prediction robustness improves, but device complexity increases
Solution Approach 1:
The patent implements a multi-functional wearable device that integrates multiple sensing capabilities into a single platform. The device simultaneously measures temperature, heart rate, activity levels, and sleep patterns using integrated sensors. This universal approach allows comprehensive data collection for robust prediction while avoiding the complexity of multiple separate devices through unified hardware and processing architecture.
Solution Approach 2:
The patent merges multiple data collection functions into a single wearable device platform. Temperature sensing, physiological monitoring, activity tracking, and contextual data collection are combined in one device with unified processing. This consolidation improves prediction robustness by integrating diverse inputs while managing complexity through integrated system architecture rather than separate components.
3Measurement precision
If multiple temperature data points are collected throughout the day, then prediction accuracy improves, but energy consumption increases
Solution Approach 1:
The patent implements periodic temperature measurement at specific intervals throughout the day (e.g., three times daily at morning, noon, and evening). Instead of continuous monitoring, the device takes discrete periodic measurements that capture sufficient temperature variation for accurate prediction while minimizing energy consumption. This periodic sampling strategy balances accuracy requirements with power efficiency.
Solution Approach 2:
The patent collects a partial set of temperature measurements focused on key times of day that provide sufficient information for accurate prediction without measuring continuously. By selecting specific critical measurement points rather than exhaustive continuous monitoring, the system achieves adequate prediction accuracy while significantly reducing energy consumption compared to full-time monitoring.
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
The system provides accurate prediction of menstrual cycle onset, allowing users to prepare for symptoms and modify their activities, thereby reducing symptom severity and improving overall health awareness.
Implementation Method 1
Wearable devices, such as rings, continuously collect physiological data using multiple LEDs to measure temperature and other parameters
Data Source
AI summary
Methods, systems, and devices for menstrual cycle onset prediction are described. The method may include receiving physiological data associated with a user from a wearable device, the physiological data including at least temperature data. The method may include fitting the received temperature data to a trigonometric or polynomial function including a plurality of features and calculating a duration between the plurality of features and a corresponding plurality of menstrual cycle onset days. The method may include estimating a future menstrual cycle onset day that the user experiences a first day of a menstrual cycle based on applying the duration to a most recent feature of the trigonometric or polynomial function. In some cases, the method may include causing a graphical user interface of a user device to display an indication of the estimated future menstrual cycle onset day.