IoT Health Feedback Using Personalized Causal Models
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Solution Overview
Problem
Current health management systems rely heavily on population-level correlations and lack personalization in establishing relationships between behavioral factors and health metrics, failing to account for intra-individual variations and causal relationships.
Innovation Solution
A method and system using IoT devices and machine learning to establish causal intra-individual relationships between behavioral factors and health metrics, employing techniques like linear regression, logistic regression, and reinforcement learning to provide personalized behavioral recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If population-level correlation modeling is used to establish relationships between behavioral factors and health metrics, then the system can provide general health guidance based on aggregate data, but it fails to account for intra-individual variations and causal relationships, reducing personalization and prediction accuracy
Solution Approach 1:
The patent segments the population into individual units by establishing separate intra-individual models for each person. Instead of treating all individuals as a homogeneous group, the system creates personalized causal models that capture unique behavioral patterns and health responses for each individual, thereby resolving the contradiction between generalizability and personalization.
Solution Approach 2:
The patent implements dynamic modeling that adapts to changing individual behaviors and health states over time. The causal models are continuously updated as new data becomes available, allowing the system to capture temporal variations in individual responses to behavioral interventions and improve prediction accuracy for personalized health management.
2Device complexity
If inter-individual modeling practices are used to treat all individuals as having the same physiology, then the system can simplify analysis and reduce computational complexity, but it fails to capture unique physiological parameters and behavioral impacts for each individual
Solution Approach 1:
The patent divides the modeling approach into inter-individual and intra-individual components. While inter-individual models provide population-level context, the system prioritizes intra-individual causal models that capture unique physiological responses and behavioral patterns for each person, enabling personalized health recommendations without requiring excessive complexity in every aspect of the system.
Solution Approach 2:
The patent applies different levels of modeling complexity to different aspects of health analysis. Rather than using complex models uniformly across all individuals and all parameters, the system applies detailed intra-individual causal modeling specifically where personalization is most critical, while using simpler aggregation methods for population-level trends, thereby balancing customization needs with computational feasibility.
3Ease of manufacture
If self-reported data is used for behavioral factors, then the system can reduce measurement costs and simplify data collection, but it introduces reporting biases and reduces measurement precision of actual behavior
Solution Approach 1:
The patent introduces wearable sensors and objective measurement devices as intermediaries between the individual's actual behavior and the data captured by the system. These intermediaries automatically collect behavioral data (such as physical activity, sleep patterns, and dietary intake) without requiring self-reporting, thereby eliminating reporting biases while maintaining ease of data collection through automated sensor integration.
Data Source
AI summary
A method, and, system for modeling causal intra-individual relationships between behavioral patterns and health metrics and recommending changes in behavior that will optimize health as measured by IOT technology. The method and system may at times implement a learning mode (exploration) to identify true causal relationships between behaviors and health metrics and may at other times implement an optimization or exploitation mode for improving on the existing causal relationships between behaviors and health metrics. The method and system may give a user feedback to improve health metrics by changing behaviors and may further comprise measuring and storing behavioral and health data, establishing models taking as input the behavioral data and forecasting an expectation of the health data for an individual as a result.