Mobile Device Health Monitoring via Machine Learning Anomaly Detection
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Solution Overview
Problem
Current health monitoring systems face challenges in effectively detecting abnormalities in continuous health indicator data due to high volumes of data, complexity of interactions between health indicators and other factors, and limited clinical guidance, particularly in diagnosing asymptomatic arrhythmias like atrial fibrillation, which require continuous monitoring but are often missed with bulky and invasive devices.
Innovation Solution
The development of software platforms and systems that utilize predictive machine learning models to analyze sequences of health indicator data in conjunction with other factor data, enabling continuous monitoring and notification of potential health issues, such as atrial fibrillation burden, through trained neural networks that can detect anomalies unsupervisedly and prompt users for high-fidelity measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous monitoring of health indicators is implemented, then detection capability for arrhythmias is improved, but device complexity and invasiveness increase
Solution Approach 1:
The patent replaces bulky mechanical monitoring devices with a mobile device-based system that uses software algorithms and machine learning models to analyze health indicator data. The mechanical/invasive monitoring equipment is substituted with computational analysis performed on standard mobile devices, reducing device complexity while maintaining continuous monitoring capability.
Solution Approach 2:
The patent introduces an intermediary processing layer between raw health indicator data and diagnostic conclusions. Machine learning models and predictive algorithms serve as intermediaries that process continuous health indicator streams, identifying arrhythmias and generating notifications without requiring complex direct monitoring hardware.
2Measurement precision
If high-volume continuous health indicator data is collected, then monitoring accuracy is improved, but difficulty in detecting abnormalities increases due to data complexity
Solution Approach 1:
The patent implements feedback mechanisms where machine learning models continuously learn from health indicator data patterns, improving their ability to detect abnormalities. The system provides feedback through notifications when anomalies are detected, allowing users to understand the context of abnormal readings while the system refines its detection algorithms based on accumulated data.
Solution Approach 2:
The patent transforms the analysis approach by changing parameters from simple threshold-based detection to multi-dimensional pattern recognition. The system analyzes combinations of health indicators, temporal patterns, and contextual factors using machine learning, converting the complexity challenge into an enhanced detection capability through sophisticated parameter analysis.
3Ease of operation
If simple mobile devices are used for monitoring, then ease of operation is improved, but capability to interpret complex health data deteriorates
Solution Approach 1:
The patent enables the system to serve itself by using machine learning models to automatically interpret complex health data patterns and generate actionable notifications. The mobile device leverages its own computational resources and integrated sensors to perform sophisticated analysis without requiring external medical equipment or complex user intervention, maintaining ease of operation while enhancing data interpretation capability.
Data Source
AI summary
Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's low-fidelity health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health-indicator (“other-factors”) to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.