Portable Wellness Monitoring With Predictive Early Disease Detection
Find Innovative SolutionsGenerate Solutions
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 physiological, cognitive, psychosocial, and behavioral data, processed by a predictive model that analyzes wavelet coefficients and user data to predict wellness and disease risk, employing machine learning algorithms like SVM, ANN, and rule-based systems for early detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple portable monitoring devices are used to track various physiological parameters, then the comprehensiveness of health monitoring is improved, but the device complexity and data processing burden increase
Solution Approach 1:
The patent combines multiple monitoring functions (heart rate, activity tracking, sleep monitoring, calorie counting) into a single integrated portable device. This merging approach allows comprehensive health monitoring without proportionally increasing device complexity, as shared components and unified processing reduce overall system complexity compared to using separate devices for each function.
Solution Approach 2:
The portable monitoring device is designed with multi-functionality, serving as both an activity tracker and a medical monitoring device. It can monitor various physiological parameters and adapt to different user needs (athletic vs. medical applications), providing comprehensive health monitoring through a single universal platform rather than requiring multiple specialized devices.
2Reliability
If continuous monitoring of physiological parameters is implemented, then early detection of health issues is improved, but the energy consumption increases
Solution Approach 1:
The system implements periodic monitoring with variable sampling rates that adapt to user activity levels and detected physiological states. During normal conditions, monitoring occurs at lower intervals to conserve energy, while during detected anomalies or high-activity periods, the sampling rate increases to improve detection reliability, thus balancing energy consumption with early detection capability.
Solution Approach 2:
The monitoring system dynamically changes operational parameters such as sampling frequency, monitoring intensity, and processing depth based on detected physiological states and user profiles. This allows the device to maintain high reliability for early detection when needed while reducing energy consumption during stable periods, effectively adapting resource usage to actual monitoring requirements.
3Productivity
If real-time feedback and alerts are provided to users, then user engagement and health management are improved, but the false alarm rate and user anxiety increase
Solution Approach 1:
The system implements intelligent feedback mechanisms that analyze trends and patterns across multiple physiological parameters before generating alerts. Rather than reacting to single isolated readings, the feedback system evaluates contextual information and historical data to distinguish between normal variations and genuine health concerns, thereby improving user engagement through relevant alerts while reducing false alarms through sophisticated analysis.
Solution Approach 2:
The system performs preliminary analysis and trend detection before issuing alerts to users. By continuously evaluating physiological data and identifying developing patterns, the system can provide early warnings for genuine issues while filtering out normal variations, thus maintaining high user engagement through meaningful notifications while minimizing false alarms that would cause anxiety.
4Measurement precision
If user-specific profiles and customization options are implemented, then measurement precision is improved, but the ease of operation decreases
Solution Approach 1:
The system implements self-service functionality where the monitoring device automatically creates and updates user profiles based on collected physiological data and usage patterns. This automation eliminates the need for manual profile configuration, maintaining measurement precision through personalized parameters while preserving ease of operation by removing complex setup requirements from the user experience.
Data Source
Figure 1
Figure 2
Figure 3~4
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.