Non-Invasive Wellness Prediction via Wearable Sensor Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for measuring wellness metrics are invasive, stressful, and limited by reliance on pre-determined biological models, leading to inefficiencies and potential false positives.
Innovation Solution
A system and method that use non-invasive biological parameters to predict wellness metrics through a model that processes these parameters to generate quantified biological indicators, eliminating the need for invasive tests and laboratory analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive sample collection methods (blood, tissue, saliva, urine) are used to determine wellness markers, then measurement precision is improved, but user stress increases and may lead to false positive results
Solution Approach 1:
The patent replaces the mechanical/invasive system of blood draws and laboratory processing with an optical/electronic system using wearable sensors that continuously monitor physiological parameters (heart rate, temperature, galvanic skin response) to predict wellness markers, thereby eliminating user stress while maintaining measurement capability
Solution Approach 2:
The patent introduces intermediate physiological parameters (heart rate variability, skin conductance, temperature) as mediators that correlate with wellness markers but can be measured non-invasively, serving as proxies that eliminate direct invasive contact while preserving diagnostic information
2Reliability
If traditional methods use pre-determined biological models and physiological parameters to measure wellness, then measurement framework is established, but adaptability to new parameters is restricted
Solution Approach 1:
The patent transforms the static, pre-determined biological model into a dynamic machine learning framework that continuously learns from data, allowing the system to adapt to new parameters and individual user patterns while maintaining a reliable measurement structure through standardized processing pipelines
Solution Approach 2:
The patent enables parameter flexibility by allowing the machine learning model to process diverse input parameters (physiological, behavioral, environmental) and dynamically adjust which parameters are most predictive for each user and condition, moving beyond fixed biological models to adaptive parameter selection
3Measurement precision
If machine learning techniques are applied to diagnostic imaging (CT, MRI), then diagnostic accuracy is improved, but application to wellness measurement has not been explored
Solution Approach 1:
The patent applies the universal machine learning framework across multiple domains including diagnostic imaging (CT, MRI) and wellness measurement, demonstrating that the same computational approach can serve both specialized diagnostic functions and broader wellness monitoring applications through appropriate data input and model configuration
Data Source
AI summary
A method (200) for predicting a wellness metric (208, 314, 612) is presented. The method (200) includes maintaining (202) a model which receives as input a set of parameters and provides as output a wellness metric (208, 314, 612). Furthermore, the method (200) includes receiving (204) a set of non-invasive biological parameters (106, 404, 602) of a user (102). In 5 addition, the method (200) includes providing (206) the set of non-invasive biological parameters (106, 404, 602) as the set of parameters to the model to cause the model to generate a wellness metric (208, 314, 612) for the user (102).