Portable Wellness Monitoring for Early Health Disorder Prediction
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Solution Overview
Problem
Existing methods fail to effectively detect health and wellness disorders in their early stages, particularly infectious diseases, which can be contagious without noticeable symptoms, necessitating timely intervention.
Innovation Solution
A system utilizing portable monitoring devices to collect wellness-relevant parameters, cognitive assessment data, and psychosocial assessment data, processed by a predictive model to generate indices for user wellness, incorporating wavelet transforms and machine learning algorithms for early detection and prediction of health conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used, then device simplicity is maintained, but early detection precision deteriorates
Solution Approach 1:
The system segments monitoring into multiple dimensions: physiological parameters (heart rate, temperature, oxygen saturation), behavioral parameters (activity levels, sleep patterns), and environmental parameters (exposure risk). This segmentation allows comprehensive early detection while keeping individual sensor requirements manageable and modular.
Solution Approach 2:
The patent combines multiple data sources including wearable sensor data, electronic health record data, environmental exposure data, and social determinants of health into a unified predictive model. This merging enables sophisticated early detection capabilities while distributing computational complexity across multiple data layers rather than requiring all sensors in a single device.
2Reliability
If continuous monitoring is implemented, then detection reliability is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic monitoring with variable intervals based on risk assessment. During low-risk periods, monitoring occurs at extended intervals to conserve battery life. When risk indicators increase or anomalies are detected, the system automatically increases monitoring frequency, maintaining detection reliability while optimizing energy consumption across different operational states.
Solution Approach 2:
The predictive model continuously processes data in the background without requiring active user engagement or frequent device charging. Once the model is trained and deployed, it operates autonomously with minimal computational overhead, maintaining continuous detection capability while consuming minimal energy compared to traditional continuous sampling approaches.
3Measurement precision
If comprehensive data collection is performed, then prediction accuracy is improved, but information processing complexity increases
Solution Approach 1:
The system performs preliminary data processing and feature extraction at the edge device before transmission to centralized servers. Relevant features such as heart rate variability metrics, activity pattern summaries, and temperature trends are pre-computed locally, reducing the dimensionality of data requiring complex processing while preserving the information necessary for accurate predictions.
Solution Approach 2:
The patent introduces intermediate processing layers including feature extraction modules, data normalization pipelines, and risk scoring algorithms that bridge raw data collection and final prediction. These intermediaries transform complex multi-source data into standardized formats suitable for the predictive model, reducing processing complexity while maintaining prediction accuracy.
Data Source
AI summary
Systems and methods are provided for monitoring a wellness of a user. A wellness-relevant parameter representing the user is monitored at a portable device over a defined period to produce a time series for the wellness-relevant parameter. A first set and a second set of either cognitive assessment data or psychosocial assessment data are obtained for the user at respective first and second times in the defined period. A value is assigned to the user via a predictive model according to the time series for the wellness-relevant parameter, the first set of either cognitive assessment data or psychosocial assessment data, and the second set of either cognitive assessment data or psychosocial assessment data.