Calibration framework 1

New sensor?

Y

Assume default gradient of [x].

Base intercept on [mean] of initial dual

calibration.Use standard error

[how?].

NSeek calibration based

on [certain conditions].

High enough calibration validity

weight for inclusion

Y

Add calibration to chart with weight

determined by weighting algorithm; and also use this to

re-weight prior points

N Discard calibration

Sensor calibration – conceptual framework Dan Evans, 17/12/16

Calibration validity weightDetermine this weight based on higher rate when:• Calibration not when reading rising / falling [by

more than x mmol/min]• Calibration in ‘normal’ range of [x-x]

• Calibration not an outlier from other points [x% off existing line, when x points already]

• Calibration not at a time of high noise [when latest reading off trend of last x points by x%]

Metrics for the above, and significance of the conditions to be tested by use of large-scale user

data

Seek calibration conditionsThe first of:

• When no calibration at all• When no calibration for [x] hours• ? When few [less than x in ‘normal

range’] data points at all / in normal range

Metrics for the above, and significance of the conditions to be tested by use of

large-scale user data

Weighting algorithmHigher weighting (by x%) for calibrations which are:

• [x] hours newer• Not in ‘bed in’ [first day] time

• Have higher calibration validity weight

Where calibration validity weight is low, allow this if few points (which suggests that algorithm should be about relative weights).

Metrics for the above, and significance of the conditions to be tested by use of large-scale user data

Calibration chart

Linear / curvilinear choice to be tested by use of large-scale user data