Multisensory Device Data Fusion for Analyte Accuracy
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Solution Overview
Problem
Multisensory devices face challenges in accurately measuring analytes due to interactions between different analytes in a sample, leading to noisy or inaccurate readings.
Innovation Solution
The implementation of data fusion methods in multisensory devices to improve accuracy by analyzing traces of signals from one sensor to estimate levels of other analytes and confirm measurements through comparison with dedicated sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple electrochemical sensors are used to detect different analytes, then the functionality and versatility of the device is improved, but measurement precision deteriorates due to interference between analytes
Solution Approach 1:
The patent uses one sensor's output signal as an intermediary to estimate and compensate for the interference effect on another sensor. Specifically, the output trace from a first sensor is used to estimate the level of a second analyte, which then serves as a basis for determining whether the first analyte measurement is limited. This intermediary approach allows the system to maintain multi-analyte detection capability while improving measurement precision through cross-sensor validation.
2Measurement precision
If data fusion methods are implemented to improve measurement accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where the output signal from one sensor is fed back into the processing system to estimate analyte levels and determine measurement limitations. The system continuously monitors the output trace of the first sensor, uses it to estimate the second analyte level, and feeds this information back to assess whether the first analyte measurement is limited. This feedback loop improves measurement precision by utilizing available sensor data without requiring additional complex hardware.
Data Source
AI summary
A multisensory device includes two or more sensors in contact with a channel, each sensor configured to output a corresponding level of an analyte in a sample in the channel. The multisensory device includes an analyzer having a memory and one or more processors. The analyzer is configured to receive a first input signal from a first sensor indicating a level of a first analyte and receive a second input signal from a second sensor indicating a level of a second analyte. The analyzer estimates the level of the second analyte based on a trace of the first input signal. The analyzer determines whether the level of the second analyte measured by the second sensor is comparable to the estimated level of the second analyte. The analyzer outputs an indication of the level of each of the first analyte and the second analyte.