Bio-sensor Tissue Liveness Detection via Signal Analysis
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Solution Overview
Problem
Wearable devices and mobile health monitoring systems face challenges in determining whether bio-sensors are sensing live tissues, leading to inaccurate data collection and power consumption issues due to continued sensing even when not properly attached or when erroneous data is gathered.
Innovation Solution
A multi-stage data analysis process involving co-variance calculation, pulsatile amplitude comparison, and mutual information analysis is employed to determine if bio-sensors are sensing live tissues, allowing for the powering off of sensors or discarding of data when conditions are not met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If bio-sensors continuously collect data without verification, then data collection coverage is maximized, but data accuracy deteriorates due to erroneous readings from non-live tissue contact
Solution Approach 1:
The system performs preliminary verification of tissue liveness using co-variance analysis and pulsatile amplitude comparison before accepting health data. This preliminary action filters out erroneous readings from non-live tissue contact, ensuring only valid data is collected while maintaining continuous monitoring coverage.
Solution Approach 2:
The system continuously monitors signal characteristics and provides feedback to validate sensor-tissue contact quality. By analyzing co-variance between multiple sensors and pulsatile amplitude patterns, the system adjusts data acceptance in real-time, maintaining both accuracy and continuous coverage.
2Duration of action of moving object
If bio-sensors remain powered on continuously, then monitoring duration is extended, but power consumption increases reducing device usage time
Solution Approach 1:
Instead of continuous high-power operation, the system uses periodic verification of tissue contact validity through signal analysis. Sensors operate continuously at low power, with periodic validation checks that enable the system to extend monitoring duration while managing power consumption through intelligent data validation rather than continuous high-power transmission.
3Use of energy by moving object
If sensors are powered off when not detecting live tissue, then power consumption is reduced, but data collection continuity is interrupted
Solution Approach 1:
The system dynamically adjusts sensor operation based on real-time signal validation. When invalid contact is detected through co-variance and pulsatile analysis, the system temporarily suspends data collection from affected sensors while maintaining monitoring of other sensors, ensuring continuous overall monitoring with optimized power usage.
4Reliability
If all collected data is used for health assessment, then assessment completeness is improved, but reliability deteriorates due to inclusion of erroneous data
Solution Approach 1:
The system extracts and removes erroneous data from the collected dataset through co-variance analysis and pulsatile amplitude validation. By identifying and excluding readings from invalid sensor-tissue contact, the system maintains comprehensive health assessment coverage while ensuring reliability through selective data extraction and validation.
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
This approach improves the accuracy of health monitoring data and conserves power by ensuring that only valid data is collected, reducing the risk of erroneous treatment decisions and extending device usage without daily charging.
Implementation Method 1
a photodetector to detect electromagnetic radiation in the optical spectrum and generate time-series data representing a periodic biological signal
Data Source
AI summary
In one embodiment, a method includes accessing first time-series data based on electromagnetic radiation in a first spectrum and second time-series data based on electromagnetic radiation in a second spectrum. The method also includes comparing the first time-series data with the second time-series data and determining, based on the comparison, (1) whether a stopping condition associated with a device has occurred or (2) whether a discarding condition associated with the first time-series data or the second time-series data has occurred.