Sensor Fusion in Automated Insulin Delivery (AID)

Sensor fusion means combining several imperfect measurements into one estimate that is more trustworthy than any single input. As of October 2026, the automated insulin delivery (AID) systems sold in the US fuse one glucose sensor with things the pump already knows: the insulin it has delivered and what the user has entered, such as meals [1][2]. Fusing several body sensors, such as two CGMs or a CGM plus a heart rate monitor, has stayed in research [3][4]. For people using AID now, the question that matters most runs the other way: what does the system do when its only sensor goes quiet or looks wrong?

Why one CGM reading isn't used on its own

A continuous glucose monitor (CGM) reads glucose in interstitial fluid, the fluid between cells, not in blood. Readings can go missing. They can read falsely low when pressure on the sensor site cuts blood flow, a "compression low," and they can differ from a blood value "with no explainable cause," according to an EASD/ADA consensus report [5]. An algorithm that reacted to every raw value would chase that noise.

So the algorithms blend each new reading with a forecast. FDA's De Novo summary for Tandem's original Control-IQ says it "predicts glucose levels 30 minutes in the future based on prior iCGM readings, insulin delivery history, and user input (e.g., carbohydrate intake, exercise, and sleep schedule)" [6]. One classic tool for this blending is the Kalman filter, which weighs a model's prediction against each new measurement by how much each can be trusted; a 2005 paper applied it to real-time glucose estimation from CGM data [7]. FDA summaries describe inputs and outputs, not internal math, so we couldn't confirm which method any commercial system uses.

What each US system takes in

The table lists the inputs named in FDA summaries (and, for twiist, the maker's site), plus what happens when CGM data stops, where the record says.

Here's what surprised us: the iLet asks for the least. Its FDA summary says it needs only body weight to start and "does not require any information about the user's total daily dose of insulin, basal or long-acting insulin requirements, or insulin correction factors" [2]. Three adaptive controllers run in parallel, one each for basal, correction and meal doses, and keep adjusting to the user [2]. MiniMed leans on the CGM differently. Its Meal Detection feature "detects rapid rises in sensor glucose" and delivers a correction dose for a missed meal bolus [11].

How FDA's interoperability rules shape the inputs

Most US AID systems are built from three FDA device types: an integrated CGM (iCGM), an alternate controller enabled (ACE) pump, and an interoperable automated glycemic controller (iAGC), which is the algorithm [14][15][16].

The controller rule narrows fusion on purpose. An iAGC "must only receive glucose input" from iCGMs unless FDA decides another type of glucose device is designed appropriately [16]. MiniMed's SmartGuard summary, for example, lists compatible Medtronic CGMs alongside iCGMs [10]. In exchange, the iCGM rule makes that one input dependable. Accuracy targets apply across the glucose range, and the trend has to be right: no more than 1% of readings may show glucose rising while it is actually falling fast, or the reverse [14]. Makers must also show the sensor "does not allow clinically significant gaps in sensor data availability," and labeling must report how often gaps happen and how long they last [14].

Then the controller rule plans for failure. Makers must show that "safe therapy is maintained when communication with digitally connected devices is interrupted, lost, or re-established," and the controller must log events such as a "glucose sensor shut down" [16]. ACE pumps must detect hazards such as occlusions (blockages) within a clinically relevant time [15].

When the sensor goes quiet

The fallbacks follow one pattern: stop trusting the forecast and drop back to fixed settings. Omnipod 5 and iLet do it as the table shows [8][2]. The MiniMed 770G, which MiniMed has since followed with the 780G and the MiniMed Flex, used a "Safe Basal" rate for no longer than 90 minutes when it judged that "the sensor data is not adequate for Auto Mode," then switched to manual mode [17][11].

The consensus report names the cost. Falling back to preprogrammed manual pump settings "could be incorrect for the individual in a specific situation" [5]. A single-sensor system also can't tell by itself whether a sudden low is real or a compression low. How to check or respond to a reading you doubt is a decision to make with your care team.

Research: catching a bad reading before acting on it

Researchers have also used fusion to judge the sensor itself. A University of Padova team combined CGM and pump data with a personalized model and a Kalman filter to predict glucose; when the CGM disagreed with the prediction, the method raised an alert [18]. In 100 virtual patients it caught simulated sensor spikes, lost sensor sensitivity and pump delivery failures, with false alerts averaging under 10% [18]. A 2014 review lists CGM "signal dropout and pressure-induced sensor attenuations" among the faults and borrows detection methods from chemical plants and aircraft [19].

Research: two glucose sensors instead of one

Redundancy is the classic form of fusion. In a 2012 study, 19 adults with type 1 diabetes wore four Dexcom SEVEN PLUS sensors at once. Combining all four cut very large errors (readings 50% or more off the reference) from 2.6% to 0.4%. It also improved mean absolute relative difference (MARD), the average percentage error, from 14.8% to 11.6% [3]. Two sensors helped too, even placed 7 mm apart [3]. The same group notes that with more than two sensors, "voting algorithms" can temporarily exclude a sensor whose signal is outlying [20].

Orthogonal redundancy goes further, pairing sensors that measure glucose in different ways so they're less likely to fail together. A 2016 feasibility study had 21 adults wear a Medtronic prototype combining optical fluorescence with two electrochemical elements in one insertion. Week-long MARD was 10.5% against 11.0% for an electrochemical-only redundant sensor, not a significant difference, but first-day MARD was 10.7% against 16.5% [21]. The authors said optical sensing had to improve before clinical use [21].

Medtronic MiniMed patented a fusion method for this kind of sensor. US9649059B2, granted May 16, 2017, describes integrity checks on each sensor for sensitivity loss, noise and drift, then weighting each value by a reliability index to get "a single, fused SG value" [22]. A patent shows what was claimed, not what any product uses. Our article on sensor calibration algorithms and data fusion covers the calibration side.

Research: adding motion and other body signals

The other direction is fusing different kinds of signals. In a 2015 inpatient study, 13 adults with type 1 diabetes wore an accelerometer and heart rate monitor during 45 minutes of treadmill exercise. A model using both signals detected exercise with 97.2% sensitivity and 99.5% specificity [4]. An earlier Illinois Institute of Technology system fed energy expenditure and galvanic skin response (skin conductance, which changes with sweat) into its glucose predictions, and ran without meal or activity announcements in clinical tests with three patients [23]. Our article on closed-loop algorithms with multi-analyte data covers the ketone and heart-rate studies.

Every added signal is one more thing that can fail. Fusion helps only if the system can tell which input to distrust at a given moment, which is what the redundancy and fault-detection studies were testing [20][18]. As of October 2026, none of the FDA summaries we read for US systems names a second glucose sensor or a wearable signal as an algorithm input [1][9][10][2].