Body Data Variability Ranges for Activity-Aware Pathology Detection
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Solution Overview
Problem
Existing medical devices struggle to accurately distinguish between pathological and non-pathological body states due to variations in activity levels and environmental conditions, leading to false positive or negative diagnoses.
Innovation Solution
A medical device system that dynamically determines non-pathological body data variability (BDV) ranges based on activity type and level, using sensors to collect data and adjust ranges in real-time to account for individual and environmental factors, allowing for precise classification of pathological or non-pathological states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed BDV thresholds are used for pathological state detection, then the detection method is simple, but false positive and false negative diagnoses increase due to activity level variations
Solution Approach 1:
The patent implements dynamic adjustment of BDV thresholds based on real-time activity level detection. The system continuously monitors activity signals and adapts the pathological state detection thresholds accordingly, transforming fixed thresholds into dynamic, context-aware boundaries that reduce false positives and negatives while maintaining detection simplicity
Solution Approach 2:
The system changes the parameter of BDV thresholds from fixed values to activity-dependent variable values. By linking threshold parameters to detected activity levels, the system automatically adjusts detection criteria to match current physiological states, improving diagnostic accuracy without requiring complex manual calibration
2Reliability
If BDV ranges are adjusted to account for activity levels, then false diagnoses are reduced, but the system complexity increases
Solution Approach 1:
The patent segments the detection system into distinct functional modules: activity level detection module, BDV calculation module, and pathological state determination module. Each module performs a specific function, and their integration through standardized interfaces improves reliability while managing complexity through modular design
Solution Approach 2:
The system implements feedback loops where activity level detection results feed into BDV range adjustment, which in turn influences pathological state detection. This closed-loop feedback mechanism ensures that detection parameters continuously adapt to current conditions, enhancing reliability through self-regulation
Data Source
AI summary
We report a method of detecting a pathological body state of a patient, comprising receiving a body signal of the patient; determining a BDV from said body signal; determining an activity level of said patient; determining a value range for said BDV for said patient, based at least in part on said activity level; comparing said BDV to said value range; and detecting a pathological state when said BDV is outside said value range. We also report a medical device system configured to implement the method. We also report a non-transitory computer readable program storage unit encoded with instructions that, when executed by a computer, perform the method.