Sensor Data Presentation with Performance Metrics
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Solution Overview
Problem
Current monitoring systems lack a mechanism to assess the accuracy of physiological data from different biometric sensors, leading to uncertainty in medical assessments and potential misdiagnosis due to varying sensor precision and statistical characteristics.
Innovation Solution
Displaying sensor performance information, such as accuracy and probability distribution curves, alongside physiological data on electronic devices, allowing users to evaluate the reliability of sensor readings and making informed medical decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor data from multiple different biometric sensors is displayed without performance information, then the quantity of data presented is sufficient, but the reliability and accuracy of medical assessments deteriorate due to varying sensor precision
Solution Approach 1:
The patent segments the information display into distinct components: sensor metric data (e.g., blood pressure readings) and sensor performance information (e.g., accuracy metrics, probability distribution curves). This segmentation allows users to separately evaluate both the measurement values and the confidence levels associated with different sensors, thereby improving reliability without overwhelming the user with undifferentiated data.
Solution Approach 2:
The patent introduces sensor performance information as an intermediary element that mediates between raw sensor data and medical assessment decisions. This intermediary provides context about sensor accuracy and precision, enabling users to weight different sensor readings appropriately and make more reliable medical assessments.
2Reliability
If sensor performance information is displayed alongside sensor data, then the reliability of medical assessments is improved, but the device complexity increases due to additional information processing and display requirements
Solution Approach 1:
The patent implements a universal data processing framework that handles multiple sensor types and multiple performance metrics through a single integrated system. The mobile application is designed to process various sensor metrics (blood pressure, heart rate, etc.) and their associated performance information (accuracy, precision, probability distributions) using common algorithms and display templates, thereby reducing overall system complexity through multi-functionality.
Solution Approach 2:
The patent uses probability distribution curves as visual copies or representations of complex statistical data. Instead of displaying raw statistical parameters that would require complex interpretation, the system generates visual curve representations that intuitively convey sensor accuracy and precision, simplifying the display complexity while maintaining information fidelity.
3Measurement precision
If all sensor data is treated with equal statistical characteristics, then the ease of operation is maintained, but the measurement precision deteriorates due to ignoring sensor-specific accuracy variations
Solution Approach 1:
The patent applies local quality by providing customized performance information for each specific sensor and sensor type. Instead of applying a uniform accuracy assumption to all sensors, the system displays sensor-specific accuracy metrics, precision values, and probability distribution characteristics. This allows users to evaluate each reading with its appropriate precision context while maintaining ease of operation through automated presentation.
Solution Approach 2:
The patent changes the parameter representation from simple scalar values to include statistical parameters such as standard deviation, confidence intervals, and probability distribution characteristics. By presenting these additional parameters in an accessible format (visual curves, color-coded indicators), the system improves measurement precision evaluation without significantly increasing operational complexity for users.
Data Source
AI summary
A method of qualifying a measurement from a subject includes collecting PPG data and meta data from the subject, determining metric features from the meta data, determining whether the meta data metric features are associated with a desired metric statistic, and in response to the determining whether the meta data metric features are associated with a desired metric statistic, categorizing the subject-specific metric statistic as qualified for processing a PPG-based metric for the subject based on the collected PPG data from the subject.