Smart Watch Activity Classification for Menstrual Cycle Tracking
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Solution Overview
Problem
Current health and wellness trackers do not account for the specific needs of women throughout their menstrual cycle, failing to provide personalized wellness and exercise routines based on biometric and lifestyle data.
Innovation Solution
A smart watch equipped with a 3-axis accelerometer, processor, and machine learning classifiers that categorize physical activities into rhythmic and non-rhythmic, performing spectral or time-domain analysis to accurately track and store data, enabling personalized health and wellness tracking and management across multiple devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If health and wellness trackers monitor general body indicators, then basic health tracking is achieved, but they fail to account for women's specific needs throughout menstrual cycle phases
Solution Approach 1:
The system segments the menstrual cycle into distinct phases (menstrual, follicular, fertile, luteal) and provides customized wellness recommendations for each phase. The processor analyzes accelerometer data in conjunction with menstrual cycle phase identification to deliver phase-specific exercise and wellness guidance, ensuring personalized tracking throughout the cycle.
Solution Approach 2:
The system performs preliminary classification of physical activities into rhythmic and non-rhythmic categories using trained machine learning models. This pre-processing enables the system to apply appropriate analysis methods (spectral analysis for rhythmic, time-domain analysis for non-rhythmic) and store data with contextual information about menstrual cycle phase, preparing personalized recommendations in advance.
2Measurement precision
If the smart watch performs detailed spectral analysis and machine learning classification, then physical activity recognition accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary classification of physical activities into rhythmic and non-rhythmic categories using trained machine learning models. This pre-processing enables the system to apply appropriate analysis methods (spectral analysis for rhythmic, time-domain analysis for non-rhythmic) and store data with contextual information about menstrual cycle phase, preparing personalized recommendations in advance.
Solution Approach 2:
The system dynamically adjusts its analysis approach based on the classified activity type. For rhythmic activities, spectral analysis is applied to identify movement frequency; for non-rhythmic activities, time-domain analysis is used where each oscillation is considered independently. This dynamic adaptation optimizes processing efficiency while maintaining high recognition accuracy.
3Loss of information
If multiple products and trackers are integrated to collect comprehensive data, then data completeness is improved, but system integration complexity increases
Solution Approach 1:
The system is designed as a universal platform that can integrate multiple health and wellness trackers and products. The processor receives and processes data from various sources including accelerometer data, heart rate monitors, and other biometric devices, consolidating them into a unified view of the user's health status across different menstrual cycle phases.
Solution Approach 2:
The system acts as an intermediary layer between multiple health tracking devices and the user. It collects data from various products and trackers, processes the information through machine learning models, and delivers synthesized personalized recommendations, simplifying the integration complexity for end users while maintaining comprehensive data collection.
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 solution provides tailored exercise and wellness plans specific to women's needs during different menstrual cycle phases, enhancing health management and data synchronization across devices for continuous monitoring.
Implementation Method 1
a 3-axis accelerometer configured to measure and store position and acceleration information for the smart watch
Implementation Method 2
in a case that a physical activity is classified as rhythmic, perform a spectral analysis on the data captured by the accelerometer to identify the frequency of movement
Implementation Method 3
in a case that a physical activity is classified as non-rhythmic, perform a time-domain analysis, in which each oscillation in the data collected by the accelerometer is considered independently
Implementation Method 4
a transceiver configured to connect the smart watch to an external computer device for data communication
Data Source
AI summary
A smart watch is described. The smart watch includes a 3-axis accelerometer that measures and stores position and acceleration information for the smart watch. The smart watch also includes a memory that stores instructions and a processor that executes the instructions to receive the stored position and acceleration information from the 3-axis accelerometer, determine one or more physical activities corresponding to the stored position and acceleration information, and store the determined one or more physical activities.