FMCW Wireless Inflammation Detection via ML Signal Analysis
Solution Overview
Problem
Current methods for detecting inflammation in diseases like Crohn's and Lupus are inefficient and often detect inflammation only after levels have significantly increased, lacking early detection capabilities.
Innovation Solution
A wireless method using frequency-modulated continuous-wave (FMCW) signals and machine-learning models to predict inflammation states by continuously transmitting and receiving signals, processing reflected data into three-dimensional maps, and inputting this data into trained models to differentiate between inflamed and non-inflamed states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If scheduled doctor visits with blood tests and physical examinations are used to detect inflammation, then diagnosis can be performed with medical expertise, but detection occurs only after inflammation levels have significantly increased and treatment timing is delayed
Solution Approach 1:
The system performs preliminary detection of inflammation through continuous wireless signal monitoring of physiological parameters (breathing rate, heart rate, movement patterns) before clinical symptoms manifest. By establishing baseline measurements and detecting subtle deviations early, the system enables intervention during pre-inflammatory stages, preventing full-blown inflammation episodes.
Solution Approach 2:
The system implements continuous monitoring of physiological parameters through wireless signals transmitted at regular intervals, replacing periodic doctor visits with uninterrupted data collection. This continuous action enables real-time detection of inflammation markers as they develop, maintaining constant surveillance without requiring patient presence in clinical settings.
2Loss of time
If continuous wireless signal monitoring is implemented to enable early inflammation detection, then detection timing is improved and intervention can occur earlier, but system complexity and implementation costs increase
Solution Approach 1:
The system uses wireless signals as an intermediary medium to indirectly measure physiological parameters related to inflammation. Rather than directly measuring inflammatory markers, the system monitors breathing patterns, heart rate variations, and movement characteristics that correlate with inflammatory states, simplifying the measurement approach while maintaining detection effectiveness.
Solution Approach 2:
The system replaces complex medical diagnostic procedures (blood tests, physical examinations) with wireless signal-based monitoring of physiological parameters. This substitution uses electromagnetic field interactions to detect changes in breathing, movement, and other vital signs, eliminating the need for invasive procedures and complex laboratory equipment while enabling continuous home-based monitoring.
3Measurement precision
If frequent doctor visits are scheduled to monitor inflammation levels, then early detection is possible, but patient convenience and quality of life during remission periods deteriorate
Solution Approach 1:
The system enables patients to self-monitor their inflammation levels continuously through passive wireless signal collection during normal daily activities. Patients wear simple tracking devices that automatically collect physiological data without requiring active participation in monitoring procedures, allowing them to maintain health surveillance while pursuing normal lifestyles and activities during remission periods.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables early detection of inflammation, allowing for timely intervention and potentially quicker return to remission by continuously monitoring and analyzing health indicators through wireless signal tracking.
Implementation Method 1
receiving reflected FMCW wireless signals with one or more receiving antennas, at least some of the reflected FMCW wireless signals being reflected from the person partially or fully
Data Source
AI summary
A wireless method for predicting an inflammation state of a person under observation, comprising: (a) transmitting frequency-modulated continuous-wave (FMCW) wireless signals from one or more transmitting antennas; (b) receiving reflected FMCW wireless signals with one or more receiving antennas, at least some of the reflected FMCW wireless signals being reflected from the person; (c) repeating steps (a) and (b) continuously while the person is under observation; (d) producing reflected FMCW wireless data based on the reflected FMCW wireless signals; (e) providing the reflected FMCW wireless data as an input to a trained machine-learning (ML) model, the trained ML model having been trained with ground-truth data that represents ground-truth inflammation states and ground-truth reflected FMCW wireless data of one or more subjects with respect to time; and (f) predicting, with the trained ML model, whether the person under observation is in an inflamed state or a non-inflamed state.