Application US20190076067

METHODS, SYSTEMS, AND DEVICES FOR CALIBRATION AND OPTIMIZATION OF GLUCOSE SENSORS AND SENSOR OUTPUT
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US20190076067

1. A method for using factory calibration to correct for manufacturing batch variations in one or more sensor parameters of a glucose sensor used for measuring the level of glucose in the body of a user, said sensor including physical sensor electronics, a microcontroller, and a working electrode, the method comprising: (9) (9)

periodically measuring, by the physical sensor electronics, electrode current (Isig) signal values for the working electrode;

performing, by the microcontroller, an Electrochemical Impedance Spectroscopy (EIS) procedure to generate values of one or more EIS-related parameters for the working electrode;

calculating, by the microcontroller, values of counter voltage (Vcntr) for the sensor;

applying, by said microcontroller, a factory calibration factor to said Isig, EIS parameter, and Vcntr values to generate respective modified Isig, EIS parameter, and Vcntr values;

based on the modified Isig, EIS parameter, and Vcntr values and a plurality of calibration-free sensor glucose (SG)-predictive models, calculating, by the microcontroller, a respective SG value for each of the SG-predictive models;

fusing, by the microcontroller, the respective SG values to calculate a single, fused SG value;

performing, by the microcontroller, error detection diagnostics on said calibrated, fused SG value to determine whether a correctable error exists in the calibrated, fused, SG value;

correcting, by the microcontroller, said correctable error; and

displaying a corrected, calibrated, fused SG value to the user.

2. The method of claim 1, wherein, when it is determined that an error in the calibrated, fused SG value is not correctable, the calibrated, fused SG value is blanked to the user. (0)

3. The method of claim 1, wherein said plurality of calibration-free SG-predictive models include at least two of a genetic programming model, an analytical model, a bag of trees model, and a decision tree model. (0)

4. The method of claim 1, wherein said plurality of calibration-free SG-predictive models include a genetic programming model, an analytical model, a bag of trees model, and a decision tree model. (0)

5. The method of claim 1, wherein said factory calibration factor is a correction parameter that mitigates deviations in sensor performance characteristics between sensors of a prior batch and sensors of a subsequent batch. (1) (0)

6. The method of claim 5, wherein said correction parameter is a weighting factor that is multiplied by said Isig, EIS parameter, and Vcntr values. (1) (0)

7. The method of claim 6, wherein said weighting factor has a first value that is applied to the Isig values, a second value that is applied to the EIS parameter values, and a third value that is applied to the Vcntr values. (1) (0)

11. The method of claim 1, wherein said factory calibration factor is determined for each of said Isig, EIS parameter, and Vcntr values based on respective reference values for a previously-manufactured sensor batch. (0)

12. The method of claim 1, wherein said one or more EIS-related parameters includes 1 kHz real impedance. (0)

13. The method of claim 1, wherein said one or more EIS-related parameters includes 1 kHz imaginary impedance. (0)

14. The method of claim 1, further comprising applying, by the microcontroller, a filter to the single, fused SG value. (0)

15. The method of claim 1, wherein the glucose sensor is used in a hybrid closed-loop (HCL) glucose monitoring system. (0)

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Application US20190076067

METHODS, SYSTEMS, AND DEVICES FOR CALIBRATION AND OPTIMIZATION OF GLUCOSE SENSORS AND SENSOR OUTPUT

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