System, device and method for compensating for temperature effects on sensors

Abstract

This document discusses, among other things, systems and methods for compensating for temperature effects on sensors, such as analyte sensors. An example method may include determining a temperature-compensated glucose concentration level by receiving a temperature signal indicative of a temperature parameter of an external component, receiving a glucose signal indicative of an in vivo glucose concentration level, and determining a compensated glucose concentration level based on the glucose signal, the temperature signal, and a delay parameter.

Description

[0001] Incorporation by Reference into Related Applications

[0002] Any and all priority claims identified in the Application Data Sheet, or any corrections thereto, are hereby incorporated by reference pursuant to 37 CFR 1.57. This application claims the benefit of U.S. Provisional Application No. 62 / 620,775, filed January 23, 2018. Each of the aforementioned applications is incorporated herein by reference in its entirety and each is hereby expressly made a part of this specification. The aforementioned applications are incorporated herein by reference in their entirety and are hereby expressly made a part of this specification. Technical Field

[0003] The present development relates generally to medical devices such as analyte sensors, and more particularly, but not by way of limitation, to systems, devices, and methods for compensating for the effects of temperature on analyte sensors. Background Art

[0004] Diabetes is a metabolic condition related to the body's production or use of insulin, a hormone that allows the body to use glucose for energy or store it as fat.

[0005] When a person consumes a meal containing carbohydrates, the food is processed by the digestive system, producing glucose in the blood. Blood sugar can be used for energy or stored as fat. The body normally maintains blood sugar levels within a range that provides enough energy to support bodily functions and avoids problems that can occur when blood sugar levels are too high or too low. Regulation of blood sugar levels depends on the production and use of insulin, which regulates the movement of blood sugar into cells.

[0006] When the body does not produce enough insulin, or when the body cannot effectively use the existing insulin, blood sugar levels can rise beyond the normal range. The state of having higher-than-normal blood sugar levels is called "hyperglycemia." Chronic hyperglycemia can lead to many health problems, such as cardiovascular disease, cataracts and other eye problems, nerve damage (neuropathy), and kidney damage. Hyperglycemia can also lead to acute problems, such as diabetic ketoacidosis - a state in which the body becomes overly acidic due to the presence of blood sugar and blood ketones produced when the body cannot use glucose. The state of having lower-than-normal blood sugar levels is called "hypoglycemia." Severe hypoglycemia can lead to an acute crisis that can cause seizures or death.

[0007] People with diabetes may take insulin to manage their blood sugar levels. Insulin can be administered manually, for example, via needle injection. Wearable insulin pumps are also available. Diet and exercise can also affect blood sugar levels. Glucose sensors can provide an estimated glucose concentration level, which can be used as a guide by the patient or caregiver.

[0008] Diabetes conditions are sometimes referred to as "type 1" and "type 2." People with type 1 diabetes are generally able to use insulin when it's available, but their bodies don't produce enough of it due to problems with the insulin-producing beta cells in the pancreas. People with type 2 diabetes can produce some insulin, but because of decreased sensitivity to insulin, they become "insulin-resistant." As a result, even when insulin is available, the body can't use it enough to effectively regulate blood sugar levels.

[0009] This background information is provided to provide a brief context for the following summary and detailed description. This background information is not intended to help determine the scope of the claimed subject matter, nor is it to be considered to limit the claimed subject matter to implementations that solve any or all of the above-mentioned disadvantages or problems. Summary of the Invention

[0010] This document discusses, among other things, systems, devices, and methods for determining subcutaneous temperature or compensating for the effects of temperature on an analyte sensor, such as a glucose sensor.

[0011] An example of the subject matter (e.g., "Example 1") can include determining a temperature-compensated glucose concentration level by receiving a temperature signal indicative of a temperature parameter of an external component, receiving a glucose signal indicative of a glucose concentration level in the body, and determining a compensated glucose concentration level based on the glucose signal, the temperature signal, and a delay parameter.

[0012] In Example 2, the subject matter of Example 1 can optionally be configured such that the temperature parameter is temperature, temperature change, or temperature offset.

[0013] In Example 3, the subject matter of any one or more of Examples 1-2 can be optionally configured such that the temperature parameter is detected at a first time and the glucose concentration level is detected at a second time after the first time, and can be configured such that the delay parameter includes a delay time period between the first time and the second time, the delay time period being due to a delay between a first temperature change of the external component and a second temperature change of an adjacent glucose sensor.

[0014] In Example 4, the subject matter of any one or more of Examples 1-3 can optionally include adjusting the delay period based on a rate of temperature change.

[0015] In Example 5, the subject matter of any one or more of Examples 1-4 can optionally include adjusting the delay period based on the detected condition.

[0016] In Example 6, the subject matter of any one or more of Examples 1-5 can optionally be configured such that the detected condition includes a sudden change in temperature.

[0017] In Example 7, the subject matter of any one or more of Examples 5-6 can optionally be configured such that the detected condition includes motion.

[0018] In Example 8, the subject matter of any one or more of Examples 1-7 can optionally be configured such that detecting the glucose signal includes receiving the glucose signal from a wearable glucose sensor.

[0019] In Example 9, the subject matter of any one or more of Example 8 can optionally be configured such that detecting a temperature signal includes measuring a temperature parameter of a component of the wearable glucose sensor.

[0020] In Example 10, the subject matter of any one or more of Examples 8 or 9 can be optionally configured such that determining the compensated glucose concentration level includes executing instructions on a processor to receive the glucose signal and the temperature signal, and using the glucose signal, the temperature signal, and the delay parameter to determine the compensated glucose concentration level.

[0021] In Example 11, the subject matter of any one or more of Examples 8-10 can optionally include storing a value corresponding to the temperature parameter in a memory circuit, and retrieving the stored value from the memory circuit for use in determining the compensated glucose concentration level.

[0022] In Example 12, the subject matter of any one or more of Examples 1-11 can optionally include delivering therapy based at least in part on the compensated glucose concentration level.

[0023] An instance (e.g., “Example 13”) of a subject matter (e.g., a system) may include: a glucose sensor circuit configured to generate a glucose signal representing a glucose concentration level, a temperature sensor circuit configured to generate a temperature signal indicative of a temperature parameter, and a processor configured to determine a compensated glucose concentration level based on the glucose signal, the temperature signal, and a delay parameter.

[0024] In Example 14, the subject matter of Example 13 can be configured such that the temperature parameter is temperature, temperature change, or temperature offset.

[0025] In Example 15, the subject matter of Example 13 or 14 can be configured such that the delay parameter includes a delay period that accounts for a delay between a first temperature change of the temperature sensor circuit and a second temperature change of the glucose sensor circuit.

[0026] In Example 16, the subject matter of Example 15 can be configured such that the processor adjusts the delay period based on a rate of temperature change determined using the temperature parameter.

[0027] In Example 17, the subject matter of Example 15 or 16 can be configured such that the processor adjusts the delay period based on a detected condition or measured state.

[0028] In Example 18, the subject matter of any one or any combination of Examples 13-17 can be configured such that the processor executes instructions to receive the glucose signal and the temperature signal, and apply the delay parameter to determine the compensated glucose concentration level.

[0029] In Example 19, the subject matter of any one or any combination of Examples 13-19 may further include a storage circuit, which may be configured such that the system stores a value corresponding to the temperature parameter in the storage circuit, and the processor subsequently retrieves the stored value from the memory for determining the compensated glucose concentration level.

[0030] In Example 20, the subject matter of any one or any combination of Examples 13-19 can be configured such that the glucose sensor circuit includes an electrode operably coupled to an electronic circuit configured to generate the glucose signal and a membrane over at least a portion of the electrode, the membrane including an enzyme configured to catalyze a reaction of glucose and oxygen from a biological fluid in vivo in contact with the membrane.

[0031] An instance (Example 21) of a subject matter (e.g., a system, device, or method) for determining a temperature-compensated glucose concentration level may include: receiving a glucose sensor signal, receiving a temperature parameter signal, receiving a third sensor signal, evaluating the temperature parameter signal using the third sensor signal to generate an estimated temperature parameter signal, and determining a temperature-compensated glucose concentration level based on the evaluated temperature parameter signal and the glucose sensor signal.

[0032] In Example 22, the subject matter of Example 21 can be configured such that receiving a third sensor signal includes receiving a heart rate signal.

[0033] In Example 23, the subject matter of Example 21 or 22 may be configured such that receiving the third signal includes receiving a heart rate signal.

[0034] In Example 24, the subject matter of any one or any combination of Examples 21-23 may be configured such that receiving the third signal includes receiving an activity signal.

[0035] In Example 25, the subject matter of any one or any combination of Examples 21-24 can be configured such that receiving a third sensor signal includes receiving a position signal.

[0036] In Example 26, the subject matter of any one or any combination of Examples 21-25 can be configured such that evaluating the temperature parameter signal includes determining the presence at a location having a known temperature signature.

[0037] In Example 27, the subject matter of any one or any combination of Examples 21-26 can be configured such that the method includes determining presence at a location having a known ambient temperature characteristic.

[0038] In Example 28, the subject matter of any one or any combination of Examples 21-27 can be configured such that the method includes determining the presence at a location having an immersive water environment.

[0039] In Example 29, the subject matter of Example 28 may be configured such that the immersive water environment is a swimming pool or a beach.

[0040] In Example 30, the subject matter of any one or any combination of Examples 21-29 can be configured such that receiving the third sensor signal includes receiving temperature information from an ambient temperature sensor.

[0041] In Example 31, the subject matter of any one or any combination of Examples 21-30 can be configured such that receiving the third sensor signal includes receiving information from a wearable device.

[0042] In Example 32, the subject matter of Example 31 can be configured such that receiving the third sensor signal includes receiving information from a watch.

[0043] In Example 33, the subject matter of any one or any combination of Examples 21-32 can be configured such that receiving the third sensor signal includes receiving temperature information from a physiological temperature sensor. In some instances, the subject matter can include a watch or other wearable device including the temperature sensor.

[0044] In Example 34, the subject matter of any one or any combination of Examples 21-33 can be configured such that receiving the temperature parameter signal includes receiving a signal indicative of a temperature, a temperature change, or a temperature offset.

[0045] In Example 35, the subject matter of any one or any combination of Examples 21-34 can be configured such that receiving a third signal includes receiving an accelerometer signal.

[0046] In Example 36, the subject matter of any one or any combination of Examples 21-35 may further include detecting motion using a third signal.

[0047] In Example 37, the subject matter of any one or any combination of Examples 21-36 can be configured such that evaluating the temperature parameter signal includes determining that a change in the temperature parameter signal is consistent with a period of exercise.

[0048] In Example 38, the subject matter of any one or any combination of Examples 21-37 can be configured such that evaluating the temperature parameter signal includes determining that the temperature parameter signal is consistent with the occurrence of an increase in body temperature due to exercise.

[0049] In Example 39, the subject matter of any one or any combination of Examples 21-38 can be configured such that determining the temperature-compensated glucose concentration level comprises applying the temperature parameter signal to a motion model.

[0050] In Example 40, the subject matter of any one or any combination of Examples 21-39 can be configured such that the method includes applying the motion model when motion is detected and a change in the temperature parameter signal indicates a decrease in temperature (e.g., which may indicate motion in a cool temperature environment or a convection cooled environment).

[0051] In Example 41, the subject matter of any one or any combination of Examples 21-40 can be configured so that the third signal includes a heart rate signal, a breathing signal, a pressure signal, or an activity signal, and motion is detected by a rise in the heart rate signal, breathing signal, pressure signal, or activity signal.

[0052] An example ("Example 42") of a subject matter (e.g., a system, device, or method) includes: a glucose sensor configured to generate a first signal representative of glucose concentration in a host, wherein the sensor includes a temperature sensor configured to generate a second signal representative of temperature, and a processor that evaluates the second signal based on a third signal and generates a temperature-compensated glucose concentration level based at least in part on the first signal and the evaluation of the second signal.

[0053] In Example 43, the subject matter of Example 42 can be configured such that the processor evaluates the second signal by corroborating the detected temperature or temperature change using the third signal.

[0054] In Example 44, the subject matter of Example 42 or 43 can be configured such that the processor determines a condition based on the third signal, and validates the detected temperature or temperature change based on the condition.

[0055] In Example 45, the subject matter of any one or any combination of Examples 42-44 can be configured such that the condition is a location, surrounding environment, activity state, or physiological condition.

[0056] In Example 46, the subject matter of any one or any combination of Examples 42-45 can be configured such that the processor discontinues temperature compensation based at least in part on the third signal.

[0057] In Example 47, the subject matter of any one or any combination of Examples 42-46 can be configured such that the processor detects motion based at least in part on the third signal.

[0058] In Example 48, the subject matter of Example 47 can be configured such that in response to detecting motion, the processor suspends temperature compensation even though the second signal indicates a drop in temperature, and can be configured such that the processor avoids incorrect temperature compensation when the host is exercising in a cool (e.g., cold outdoors or convection-cooled) environment.

[0059] In Example 49, the subject matter of any one or any combination of Examples 42-48 can be configured such that the processor specifies the temperature based at least in part on the third signal.

[0060] In Example 50, the subject matter of any one or any combination of Examples 42-49 may further include a third sensor that generates a third signal.

[0061] In Example 51, the subject matter of any one or any combination of Examples 42-50 may be configured such that the third signal includes location information, and the processor evaluates the second signal based at least in part on the location information.

[0062] In Example 52, the subject matter of any one or any combination of Examples 42-51 can be configured such that the third signal includes activity information, and the processor evaluates the second signal based at least in part on the activity information.

[0063] In Example 53, the subject matter of any one or any combination of Examples 42-52 can be configured such that the temperature compensated glucose sensor system comprises a wearable continuous glucose monitor comprising the glucose sensor and the temperature sensor.

[0064] In Example 54, the subject matter of any one or any combination of Examples 42-53 can be configured such that the temperature compensated glucose sensor system includes an activity sensor, and the third signal includes activity information from the activity sensor.

[0065] In Example 55, the subject matter of any one or any combination of Examples 42-54 can be configured such that the third signal comprises the host's heart rate, respiratory rate, or stress.

[0066] In Example 56, the subject matter of any one or any combination of Examples 42-55 may be configured such that the processor detects motion based on changes in the heart rate, breathing rate, or pressure.

[0067] In Example 57, the subject matter of any one or any combination of Examples 42-56 can be configured such that the processor confirms the elevated body temperature indicated by the second signal based at least in part on the detection of motion.

[0068] In Example 58, the subject matter of any one or any combination of Examples 42-56 can be configured such that the processor reduces, tapers, limits, or suspends temperature compensation in response to detection of motion.

[0069] In Example 59, the subject matter of any one or any combination of Examples 42-58 can be configured such that the third signal comprises a signal from an optical sensor configured to detect a blood parameter of the host.

[0070] In Example 60, the subject matter of Example 59 may further include an optical sensor comprising a light source and a light detector configured to detect a blood flow velocity or a red blood cell count of the host in an area below the optical sensor.

[0071] An instance ("Example 61") of a subject matter (e.g., a system, device, or method) can include temperature compensating a continuous glucose sensor by determining a pattern from temperature data, receiving a glucose signal from the continuous glucose sensor, the glucose signal indicating a glucose concentration level, and determining a temperature-compensated glucose concentration level based at least in part on the sensor glucose signal and the pattern.

[0072] In Example 62, the subject matter of Example 61 can be configured such that determining a pattern includes determining a pattern of temperature changes, and the method includes compensating the glucose concentration level based on the pattern.

[0073] In Example 63, the subject matter of Example 61 or 62 may further include receiving a temperature parameter, comparing the temperature parameter to the pattern, and determining the temperature-compensated glucose concentration level based at least in part on the comparison.

[0074] In Example 64, the subject matter of Example 63 can be configured such that the pattern includes a temperature pattern associated with a physiological cycle.

[0075] In Example 65, the subject matter of Example 63 or 64 can be configured such that the method includes: determining whether the temperature parameter is reliable based on the comparison with the pattern; and when the temperature parameter is determined to be reliable, using the temperature parameter to temperature compensate the glucose concentration level.

[0076] In Example 66, the subject matter of any one or any combination of Examples 63-65 can be configured such that the method includes determining a degree of compensation based at least in part on a comparison of the temperature parameter with the pattern. For example, the degree of compensation can be based on a defined range or confidence interval.

[0077] In Example 67, the subject matter of any one or any combination of Examples 61-66 can be configured such that the determining mode includes determining a state, and determining the temperature-compensated glucose concentration level based at least in part on the determined state.

[0078] In Example 68, the subject matter of Example 67 can be configured such that determining the state includes applying a temperature parameter to the state model.

[0079] In Example 69, the subject matter of Example 67 or 68 can be configured such that determining the state includes applying one or more of glucose concentration level, carbohydrate sensitivity, time, activity, heart rate, breathing rate, posture, insulin delivery, meal timing, or meal size to the state model.

[0080] In Example 70, the subject matter of any one or any combination of Examples 67-69 can be configured such that determining a state includes determining a motion state, and the method includes adjusting temperature compensation based on a model of the motion state.

[0081] An instance (“Example 71”) of a subject matter (e.g., a system, device, or method) may include: a glucose sensor circuit configured to generate a glucose signal representing a glucose concentration level, a temperature sensor circuit configured to generate a temperature signal indicative of a temperature parameter, and a processor for receiving the glucose signal and the temperature signal and determining a temperature-compensated glucose concentration level based at least in part on the glucose signal and a pattern determined by the temperature signal.

[0082] In Example 72, the subject matter of Example 71 can be configured such that the processor determines a temperature parameter based on the temperature signal, compares the temperature parameter to the pattern, and determines a temperature-compensated glucose concentration level based at least in part on the comparison.

[0083] In Example 73, the subject matter of Example 71 or 72 can be configured such that the processor determines whether the temperature parameter is reliable based on the comparison with the pattern, and when the temperature parameter is determined to be reliable, uses the temperature parameter to temperature compensate the glucose concentration level.

[0084] In Example 74, the subject matter of Example 72 or 73 can be configured such that the processor determines the degree of compensation based at least in part on a comparison of the temperature parameter to the pattern.

[0085] In Example 75, the subject matter of any one or any combination of Examples 71-74 can be configured such that the schema includes a state model, and the processor determines the temperature-compensated glucose concentration level at least in part by applying a temperature parameter to the state model.

[0086] In Example 76, the subject matter of Example 75 can be configured such that the processor determines the temperature compensated glucose concentration level by additionally applying one or more of glucose concentration level, carbohydrate sensitivity, time, activity, heart rate, breathing rate, posture, insulin delivery, meal timing, or meal size to a state model.

[0087] In Example 77, the subject matter of Example 75 or 76 can be configured such that the processor determines a motion state and adjusts the temperature compensation model based at least in part on the motion state.

[0088] In Example 78, the subject matter of any one or any combination of Examples 71-77 may further include a storage circuit comprising executable instructions for determining a pattern from the temperature signal and determining a temperature-compensated glucose concentration level based on the pattern, the processor being configured to retrieve the instructions from the memory and execute the instructions.

[0089] In Example 79, the subject matter of any one or any combination of Examples 71-78 can be configured such that the processor receives information about the mode from the remote system via the communication circuit.

[0090] In Example 80, the subject matter of Example 79 may be configured such that the remote system receives temperature parameter information based on the temperature signal and determines the mode from the temperature parameter information.

[0091] An instance ("Example 81") of a subject matter (e.g., a method, system, or apparatus) can include determining a first value from a first signal indicative of a temperature parameter of a component of a continuous glucose sensor system, receiving a glucose sensor signal indicative of a glucose concentration level, comparing the first value to a reference value, and determining a temperature-compensated glucose level based on the glucose sensor signal and the comparison of the first signal to the reference value.

[0092] In Example 82, the subject matter of Example 81 can be configured such that the method includes determining a temperature difference from a reference state based on a change in the first value relative to the reference value without calibrating the temperature to the reference value.

[0093] In Example 83, the subject matter of Example 81 or 82 may further include determining the reference value from the first signal.

[0094] In Example 84, the subject matter of Example 83 can be configured such that the continuous glucose sensor system includes a glucose sensor insertable into a host, and the reference value is determined during a specified time period after the glucose sensor is inserted into the host.

[0095] In Example 85, the subject matter of Example 83 or 84 can be configured such that the continuous glucose sensor system includes a glucose sensor insertable into a host, and the reference value is determined during a specified period of time after activating the glucose sensor.

[0096] In Example 86, the subject matter of any one or any combination of Examples 83-85 can be configured such that the reference value is determined during the manufacturing process.

[0097] In Example 87, the subject matter of any one or any combination of Examples 83-86 can be configured such that the method includes determining the reference value during a first time period and determining the first value during a second time period, the second time period occurring after the first time period. The reference value can be, for example, a long-term average value, and the first value can be a short-term average value.

[0098] In Example 88, the subject matter of Example 87 may further include updating the reference value based on one or more temperature signal values ​​obtained in a third time period after the second time period.

[0099] In Example 89, the subject matter of any one or any combination of Examples 83-88 can be configured such that determining the reference value includes determining an average of a plurality of sample values ​​obtained from the first signal.

[0100] In Example 90, the subject matter of any one or any combination of Examples 81-89 can be configured such that the temperature-compensated glucose level is determined based at least in part on a temperature-dependent sensitivity value that varies based on a deviation of the first value from the reference value.

[0101] An instance ("Example 91") of a subject matter (e.g., a system, device, or method) may include: a glucose sensor circuit configured to generate a glucose signal representing a glucose concentration level, a temperature sensor circuit configured to generate a first signal indicative of a temperature parameter, and a processor for determining a temperature-compensated glucose level based on the glucose signal and a deviation of the first signal from a reference value.

[0102] In Example 92, the subject matter of Example 91 can be configured such that the processor determines a deviation of the first signal from the reference value without determining a temperature corresponding to the reference value.

[0103] In Example 93, the subject matter of Example 91 or 92 can be configured such that the processor determines the reference value based on the first signal.

[0104] In Example 94, the subject matter of Example 93 may be configured such that the processor determines the reference value based on a plurality of sample values ​​obtained from the first signal during a first time period.

[0105] In Example 95, the subject matter of Example 93 or 94 may be configured such that the processor determines the reference value based on a plurality of sample values ​​obtained from the first signal within a specified period of time after activation or insertion of the glucose sensor.

[0106] In Example 96, the subject matter of any one or any combination of Examples 93-95 can be configured such that the processor cyclically updates the reference value.

[0107] In Example 97, the subject matter of any one or any combination of Examples 91-96 may be configured such that the processor determines the reference value as an average of a plurality of sample values ​​obtained from the first signal within a specified time period.

[0108] In Example 98, the subject matter of any one or any combination of Examples 91-97 can be configured such that the processor determines the temperature compensated glucose level based on the glucose signal and a temperature-dependent sensitivity value that varies based on a deviation from the reference value.

[0109] In Example 99, the subject matter of any one or any combination of Examples 91-98 can be configured such that the processor determines the temperature compensated glucose concentration level based on a model, wherein the model can be configured such that a glucose sensor value determined from the glucose signal and a sample value based on the first signal are applied to the model.

[0110] In Example 100, the subject matter of any one or any combination of Examples 91-100 may also include a storage circuit and executable instructions stored on the storage circuit to determine the temperature-compensated glucose concentration level based on the glucose signal and the deviation of the first signal from the reference value.

[0111] An example ("Example 101") of a subject matter (e.g., a method, system, or apparatus) can include receiving a glucose signal indicative of a glucose concentration level, receiving a temperature signal indicative of a temperature parameter, detecting a condition, and determining a temperature-compensated glucose concentration level based at least in part on the glucose signal, the temperature signal, and the detected condition.

[0112] In Example 102, the subject matter of Example 101 can be configured such that the condition comprises a high rate of change of the glucose signal, wherein temperature compensation is reduced or suspended during a time period when the glucose signal experiences a high rate of change.

[0113] In Example 103, the subject matter of Example 101 or 102 may be configured such that the condition comprises a sudden change in the temperature signal.

[0114] In Example 104, the subject matter of Example 103 may be configured such that temperature compensation is reduced or suspended in response to detecting a sudden change in temperature.

[0115] In Example 105, the subject matter of Example 103 or 104 can be configured such that determining the temperature-compensated glucose concentration level includes replacing the temperature signal value associated with the sudden change in temperature with a previous temperature signal value.

[0116] In Example 106, the subject matter of any one or any combination of Examples 103-105 can be configured such that determining the temperature-compensated glucose concentration level includes determining an inferred temperature signal value based on previous temperature signal values ​​and using the inferred temperature signal value to replace the temperature signal value associated with the sudden change in temperature.

[0117] In Example 107, the subject matter of Example 106 can be configured such that, in response to detecting a sudden change in temperature, a delay model is invoked, the delay model specifying a delay period for determining the temperature-compensated glucose level.

[0118] In Example 108, the subject matter of any one or any combination of Examples 101-107 can be configured such that the condition is the presence of radiant heat on the continuous glucose monitoring system.

[0119] In Example 109, the subject matter of any one or any combination of Examples 101-108 can be configured such that the condition is fever, wherein temperature compensation is reduced or suspended in response to detecting fever.

[0120] In Example 110, the subject matter of Example 109 can be configured such that the condition includes motion.

[0121] In Example 111, the subject matter of Example 110 can be configured such that the method includes reducing, tapering, limiting, or suspending temperature compensation when motion is detected.

[0122] In Example 112, the subject matter of any one or any combination of Examples 101-111 can be configured such that the method includes determining the temperature-compensated glucose concentration level using a linear model.

[0123] In Example 113, the subject matter of Example 112 may further include receiving a blood glucose calibration value, wherein upon receiving the blood glucose calibration value, the temperature compensation gain and offset are updated.

[0124] In Example 114, the subject matter of any one or any combination of Examples 101-113 can be configured such that the method includes determining the temperature-compensated glucose concentration level using a time series model.

[0125] In Example 115, the subject matter of any one or any combination of Examples 101-114 can be configured such that the method includes determining the temperature-compensated glucose concentration level using a partial differential equation.

[0126] In Example 116, the subject matter of any one or any combination of Examples 101-115 can be configured such that the method includes determining the temperature-compensated glucose concentration level using a probabilistic model.

[0127] In Example 117, the subject matter of any one or any combination of Examples 101-116 can be configured such that the method includes determining the temperature-compensated glucose concentration level using a state model.

[0128] In Example 118, the subject matter of any one or any combination of Examples 101-117 can be configured such that the condition includes a body mass index (BMI) value.

[0129] In Example 119, the subject matter of any one or any combination of Examples 101-118 can be configured such that the method includes determining a long-term average using the temperature signal, wherein the temperature-compensated glucose concentration level is determined using the long-term average.

[0130] In Example 120, the subject matter of any one or any combination of Examples 101-119 can be configured such that the glucose signal indicative of a condition is received from a continuous glucose sensor, and the condition is stress on the continuous glucose sensor.

[0131] In Example 121, the subject matter of Example 120 can be configured such that the compression is detected based at least in part on a rapid drop in the glucose signal.

[0132] In Example 122, the subject matter of Example 120 or 121 can be configured such that the condition is stress during sleep.

[0133] In Example 123, the subject matter of any one or any combination of Examples 101-122 can be configured such that the condition is sleep.

[0134] In Example 124, the subject matter of Example 123, wherein sleep is detected using one or more of temperature, posture, activity, and heart rate, and the method includes applying a specified glucose alarm trigger based on the detected sleep.

[0135] In Example 125, the subject matter of any one or any combination of Examples 101-124 can further comprise delivering an insulin therapy, wherein the therapy is determined at least in part based on the temperature compensated glucose level.

[0136] An instance (“Embodiment 126”) of a subject matter (e.g., a system, device, or method) may include: a glucose sensor circuit configured to generate a glucose signal representative of a glucose concentration level, a temperature sensor circuit configured to generate a temperature signal indicative of a temperature parameter, and a processor configured to determine a compensated glucose concentration level based on the glucose signal, the temperature signal, and a detected condition.

[0137] In Example 127, the subject matter of Example 126 can be configured such that the condition includes a high rate of change of the glucose signal, and the processor reduces, suspends, tapers, or limits temperature compensation during the high rate of change of the glucose signal.

[0138] In Example 128, the subject matter of Example 126 or 127 can be configured such that the condition includes a sudden change in the temperature signal, and can be configured such that the processor reduces, suspends, tapers off, or limits temperature compensation in response to detecting the sudden change in temperature.

[0139] In Example 129, the subject matter of any one or any combination of Examples 126-128 can be configured such that the condition includes motion, and can be configured such that when motion is detected, the processor reduces, tapers, limits, or suspends temperature compensation.

[0140] In Example 130, the subject matter of any one or any combination of Examples 126-129 may also include a second temperature sensor circuit configured to detect radiant heat on the continuous glucose monitoring system, and wherein the detected condition includes radiant heat detected by the second temperature sensor circuit.

[0141] An example ("Embodiment 131") of a subject matter (e.g., a device, system, or method) may include: an elongated portion having a distal end configured for intracorporeal insertion into a host and a proximal end configured to be operably coupled to a circuit; and a temperature sensor at the proximal end of the elongated portion.

[0142] In Example 132, the subject matter of Example 131 can be configured such that the temperature sensor includes a thermistor.

[0143] In Example 133, the subject matter of Example 131 or 132 can be configured such that the temperature sensor includes a temperature variable resistance coating.

[0144] In Example 134, the subject matter of any one or any combination of Examples 131-133 can be configured such that the temperature sensor comprises a thermocouple.

[0145] In Example 135, the subject matter of Example 134 can be configured such that the elongated portion includes a first wire extending from the proximal end to the distal end, and the thermocouple includes the first wire and a second wire connected to the first wire to form the thermocouple.

[0146] In Example 136, the subject matter of Example 135 can be configured such that the first metal wire is tantalum or a tantalum alloy, and the second metal wire is platinum or a platinum alloy.

[0147] In Example 137, the subject matter of Example 135 or 136 may further include a transmitter coupled to the glucose sensor, a first electrical contact on the transmitter coupled to the first metal line, and a second electrical contact on the transmitter coupled to the second metal line.

[0148] An instance ("Example 138") of a subject matter (e.g., a method, system, or apparatus) may include receiving a calibration value for a temperature signal, receiving a temperature signal indicative of a temperature parameter from a temperature sensor, receiving a glucose signal indicative of a glucose concentration level from a continuous glucose sensor, and determining a temperature-compensated glucose concentration level based at least in part on the glucose signal, the temperature signal, and the calibration value.

[0149] In Example 139, the subject matter of Example 138 can be configured such that receiving a calibration value for the temperature signal includes obtaining the calibration value during a manufacturing step having a known temperature.

[0150] In Example 140, the subject matter of Example 138 or 139 can be configured such that receiving the calibration value of the temperature signal includes obtaining a temperature within a specified time period after inserting the continuous glucose sensor into a host.

[0151] An instance ("Example 141") of a subject matter (e.g., a method, system, or device) can include receiving a temperature signal indicative of a temperature of a component of a continuous glucose sensor on a host, and determining an anatomical location of the continuous glucose sensor on the host based at least in part on the received temperature signal.

[0152] In Example 142, the subject matter of Example 141 can be configured such that the anatomical location is determined based at least in part on the sensed temperature.

[0153] In Example 143, the subject matter of Example 141 or 142 may be configured such that the anatomical location is determined based at least in part on the variability of the temperature signal.

[0154] An example ("Embodiment 144") of a subject matter (eg, a method, system, or apparatus) can include receiving a temperature signal indicative of a temperature parameter from a temperature sensor on a continuous glucose monitor, and determining a restart of the continuous glucose monitor based on the temperature signal.

[0155] In Example 145, the subject matter of Example 144 can be configured such that determining the restart of the continuous glucose monitor by the temperature signal includes: comparing a first temperature signal value before the sensor is turned on with a second temperature signal value after the sensor is turned on, and declaring the restart of the continuous glucose monitor when the comparison meets a similarity condition.

[0156] In Example 146, the subject matter of Example 144 or 145 can be configured such that the similarity condition is a temperature range.

[0157] An instance (“Embodiment 147”) of a subject matter (e.g., a system, device, or method) may include: a glucose sensor circuit configured to generate a glucose signal representative of a glucose concentration level, a temperature sensor circuit configured to generate a temperature signal indicative of a temperature parameter, a heat deflector configured to deflect heat from the temperature sensor circuit, and a processor configured to determine a compensated glucose concentration level based on the glucose signal, the temperature signal, and a detected condition.

[0158] An example ("Embodiment 148") of a subject matter (e.g., a system, device, or method) may include: a glucose sensor circuit configured to generate a glucose signal representative of a glucose concentration level of a host, a first temperature sensor circuit configured to generate a first temperature signal indicative of a first temperature parameter proximate to the host, a second temperature sensor circuit configured to generate a second temperature signal indicative of a second temperature parameter, and a processor configured to determine a compensated glucose concentration level based on the glucose signal, the first temperature signal, and the second temperature signal.

[0159] In Example 149, the subject matter of Example 148 can be configured such that the processor determines the compensated glucose concentration level based in part on a temperature gradient between the first temperature sensor circuit and the second temperature sensor circuit.

[0160] In Example 150, the subject matter of Example 148 or 149 can be configured such that the processor determines the compensated glucose concentration level based in part on an estimate of heat flux between the first temperature sensor circuit and the second temperature sensor circuit.

[0161] In Example 151, the subject matter of any one or any combination of Examples 148-150 can be configured such that the second temperature circuit is configured to generate a temperature signal indicative of an ambient temperature.

[0162] In Example 152, the subject matter of any one or any combination of Examples 148-151 can be configured such that the processor is configured to generate a temperature signal indicative of a temperature of a transmitter coupled to the glucose sensor circuit.

[0163] An example ("Example 153") of a subject matter (e.g., a method, apparatus, or system) can include receiving a glucose signal from a glucose sensor representing a glucose concentration level of a host, receiving a first temperature signal indicative of a first temperature parameter proximate the host or the glucose sensor, receiving a second temperature signal indicative of a second temperature parameter, and determining a compensated glucose concentration level based at least in part on the glucose signal, the first temperature signal, and the second temperature signal.

[0164] In Example 154, the subject matter of Example 153 can be configured such that the first temperature signal is received from a first temperature sensor coupled to the glucose sensor, and the second temperature signal is received from a second temperature sensor coupled to the glucose sensor.

[0165] In Example 155, the subject matter of Example 154 can be configured such that the compensated glucose concentration level is determined based at least in part on a temperature gradient between the first temperature sensor and the second temperature sensor.

[0166] In Example 156, the subject matter of Example 154 or 155 can be configured such that the compensated glucose concentration level is determined based at least in part on a heat flux between the first temperature sensor and the second temperature sensor.

[0167] In Example 157, the subject matter of any one or any combination of Examples 154-156 may further include detecting a rise in the first temperature signal and a fall in the second temperature signal, and adjusting a temperature compensation model based on the detected rise and fall.

[0168] In Example 158, the subject matter of Example 157 can be configured such that the method includes detecting motion (e.g., outdoor motion or convection cooling motion) based at least in part on the detected rises and falls, and adjusting or applying a temperature compensation model based on the motion detection.

[0169] In Example 159, the subject matter of any one or any combination of Examples 154-158 may further include determining, based at least in part on the second temperature signal, that the temperature change is due to radiant heat or ambient heat, and adjusting or applying a temperature compensation model based on the determination.

[0170] An example ("Example 160") of a subject matter (e.g., a method, system, or apparatus) can determine a glucose concentration level by receiving a temperature sensor signal, receiving a glucose sensor signal, applying the temperature sensor signal and the glucose sensor signal to a model, and receiving an output from the model related to the glucose concentration level, wherein the model compensates for multiple temperature-dependent effects on the glucose sensor signal.

[0171] In Example 161, the subject matter of Example 161 can be configured such that the output is a compensated glucose concentration level.

[0172] In Example 162, the subject matter of Example 161 can further include delivering therapy based on the compensated glucose concentration value.

[0173] In Example 163, the subject matter of Example 161 can be configured such that the model compensates for two or more of sensor sensitivity, local glucose level, compartment bias, and non-enzymatic bias. In some instances, the model can compensate for three or more of sensor sensitivity, local glucose level, compartment bias, and non-enzymatic bias. In some instances, the model can account for additional temperature-dependent factors in addition to sensor sensitivity, local glucose level, compartment bias, and non-enzymatic bias.

[0174] An example ("Example 164") of a subject matter (e.g., a method, system, or device) can include determining an analyte concentration level by determining a first value indicative of conductivity of a sensor component, determining a second value indicative of conductivity of the sensor component, receiving a signal representative of an analyte concentration of a host, and determining a compensated analyte concentration level based at least in part on a comparison of the second value and the first value. The first value and the second value can be, for example, conductivity, resistance, or electrical impedance.

[0175] In Example 165, the subject matter of Example 164 can be configured such that determining the first value includes determining an average conductivity.

[0176] In Example 166, the subject matter of Example 164 or Example 165 may optionally include determining a first estimated subcutaneous temperature that is time-correlated with the first value, and determining a second estimated subcutaneous temperature that is time-correlated with the second value, wherein the second estimated subcutaneous temperature is determined at least in part based on a comparison of the second value with the first value.

[0177] In Example 167, the subject matter of Example 166 may optionally include determining a third estimated subcutaneous temperature that is time-correlated with the second value, determining whether a condition is satisfied based on a comparison of the third estimated subcutaneous temperature and the second estimated subcutaneous temperature, and declaring an error or triggering a reset in response to satisfaction of the condition.

[0178] In Example 168, the subject matter of Example 167 can optionally include triggering a reset, wherein triggering a reset includes determining a subsequent estimated subcutaneous temperature based on the third estimated temperature and the second value, or based on a third conductance value and a fourth estimated subcutaneous temperature that is time-correlated with the third conductance value.

[0179] In Example 169, the subject matter of any one or any combination of Examples 164-168 can optionally include compensating for drift in conductance values.

[0180] In Example 170, the subject matter of any one or any combination of Examples 164-169 can be configured such that compensating for drift includes applying a filter.

[0181] An instance (“Example 171”) of a subject matter (e.g., a method, system, or device) can include determining a first value indicative of conductivity of a sensor component at a first time, determining a second value indicative of conductivity of the sensor component at a later time, and determining an estimated subcutaneous temperature based at least in part on a comparison of the second value and the first value.

[0182] An instance ("Example 172") of a subject matter (e.g., a method, system, or apparatus) can include accessing, by the analyte sensor system, first data from a system temperature sensor of the analyte sensor system; applying the first data to a trained temperature compensation model, the trained temperature compensation model being used to generate a compensated temperature value; and determining an analyte concentration value based at least in part on the compensated temperature value.

[0183] In Example 173, the subject matter of Example 172 can be configured such that the first data includes at least one of an uncompensated temperature value or raw temperature sensor data from the system temperature sensor.

[0184] In Example 174, the subject matter of any one or more of Examples 172-173 can be configured such that the trained temperature compensation model returns a first temperature sensor parameter in response to the first data, and can also include generating the compensated temperature value based at least in part on the first temperature sensor parameter.

[0185] In Example 175, the subject matter of any one or more of Examples 172-174 can be configured such that the trained temperature compensation model returns a system temperature sensor offset and a system temperature sensor slope, and also includes receiving raw sensor data from the system temperature sensor; and generating the compensated temperature value based at least in part on the raw sensor data, the system temperature sensor offset, and the system temperature sensor slope.

[0186] An example (Embodiment 176) of a subject matter (e.g., a method, system, or apparatus) can include an analyte sensor; a system temperature sensor; and control circuitry. The control circuitry can be configured to perform operations including: accessing first data from a system temperature sensor of the analyte sensor system; applying the first data to a trained temperature compensation model, the trained temperature compensation model being used to generate a compensated temperature value; and determining an analyte concentration value based at least in part on the compensated temperature value.

[0187] In Example 177, the subject matter of Example 176 can be configured such that the first data includes at least one of an uncompensated temperature value or raw temperature sensor data from the system temperature sensor.

[0188] In Example 178, the subject matter of any one or more of Examples 176-177 can be configured such that the trained temperature compensation model returns a first temperature sensor parameter in response to the first data, and may also include generating the compensated temperature value based at least in part on the first temperature sensor parameter.

[0189] In Example 179, the subject matter of any one or more of Examples 176-178 can be configured such that the trained temperature compensation model returns a system temperature sensor offset and a system temperature sensor slope, and can also include receiving raw sensor data from the system temperature sensor; and generating the compensated temperature value based at least in part on the raw sensor data, the system temperature sensor offset, and the system temperature sensor slope.

[0190] In Example 180, the subject matter of any one or more of Examples 176-179 may further include an application specific integrated circuit (ASIC) including a system temperature sensor.

[0191] An example (Embodiment 181) of a subject matter (e.g., a method, system, or apparatus) can include determining a temperature-compensated glucose concentration level. The determining can include receiving a glucose sensor signal; receiving a temperature parameter signal; detecting a motion state based at least in part on the glucose sensor signal or the temperature parameter signal; and modifying a temperature compensation applied to the glucose sensor signal.

[0192] In Example 182, the subject matter of Example 181 can include determining that a noise floor of the glucose sensor signal is greater than a first threshold.

[0193] In Example 183, the subject matter of any one or more of Examples 181-182 may include determining that a noise floor of the temperature parameter signal is greater than a second threshold.

[0194] In Example 184, the subject matter of any one or more of Examples 181-183 may include determining that a noise floor of the glucose sensor signal is greater than a first threshold; and determining that a noise floor of the temperature parameter signal is greater than a second threshold.

[0195] In Example 185, the subject matter of any one or more of Examples 181-184 may be configured such that modifying the temperature compensation comprises: applying a motion model to the temperature parameter signal to generate an estimated temperature parameter signal; and generating a temperature-compensated glucose concentration value using the estimated temperature parameter.

[0196] In Example 186, the subject matter of any one or more of Examples 181-185 may be configured such that detecting the motion state comprises determining that a distribution of a rate of change of the temperature parameter signal satisfies a classifier.

[0197] In Example 187, the subject matter of any one or more of Examples 181-186 may be configured such that detecting the motion state includes determining that a distribution of a rate of change of the temperature parameter signal is less than a threshold value.

[0198] An example ("Embodiment 188") of a subject matter (e.g., a method, system, or device) can include a temperature-compensated glucose sensor system comprising: a glucose sensor configured to generate a first signal representative of glucose concentration in a host; a temperature sensor configured to generate a second signal representative of temperature; and a processor. The processor can be programmed to perform operations comprising: detecting a motion state based at least in part on the first signal or the second signal; and modifying temperature compensation applied to the first signal.

[0199] In Example 189, the subject matter of Example 188 can be configured such that the operations further include determining that a noise floor of the first signal is above a first threshold.

[0200] In Example 190, the subject matter of any one or more of Examples 188-189 may be configured such that the operations further include determining that a noise floor of the second signal is above a second threshold.

[0201] In Example 191, the subject matter of any one or more of Examples 188-190 can be configured such that the operation further includes: determining that the noise floor of the first signal is above a first threshold; and determining that the noise floor of the second signal is above a second threshold.

[0202] In Example 192, the subject matter of any one or more of Examples 188-191 can be configured such that modifying the temperature compensation comprises: applying a motion model to the second signal to generate an estimated second signal; and generating a temperature-compensated glucose concentration value using the estimated second signal.

[0203] In Example 193, the subject matter of any one or more of Examples 188-192 may be configured such that detecting the motion state comprises determining that a distribution of a rate of change of the second signal satisfies a classifier.

[0204] In Example 194, the subject matter of any one or more of Examples 188-193 may be configured such that detecting the motion state includes determining that a distribution of a rate of change of the second signal is less than a threshold.

[0205] An example ("Embodiment 195") of a subject matter (e.g., a method, system, or apparatus) can include a processor-implemented method of measuring temperature at an analyte sensor system. The method can include: accessing a periodic temperature log stored on the analyte sensor system during a first sensor period; determining a peak temperature from the periodic temperature log; and performing a responsive action based on the peak temperature.

[0206] In Example 196, the subject matter of Example 195 can include determining that the peak temperature exceeds a peak temperature threshold, wherein the responsive action includes interrupting the first sensor period.

[0207] In Example 197, the subject matter of any one or more of Examples 195-196 may include determining an initial sensor period parameter based at least in part on the peak temperature; receiving raw sensor data from an analyte sensor of the analyte sensor system; and generating an analyte concentration value using the initial period parameter and the raw sensor data.

[0208] In Example 198, the subject matter of any one or more of Examples 195-197 can be configured such that the initial sensor period parameter includes a sensitivity or a baseline.

[0209] In Example 199, the subject matter of any one or more of Examples 195-198 may include: measuring a first temperature at the analyte sensor system before the first sensor period; writing the first temperature to a periodic temperature record; waiting for a period; and measuring a second temperature at the analyte sensor system.

[0210] An example of the subject matter ("Embodiment 200") can include a temperature-compensated analyte sensor system. The temperature-compensated analyte sensor system can include: an analyte sensor configured to generate a first signal representing an analyte concentration in a host; a temperature sensor configured to generate a second signal representing a temperature; and a processor. The processor can be programmed to perform operations including: accessing a periodic temperature record stored on the analyte sensor system during a first sensor period; determining a peak temperature from the periodic temperature record; and performing a responsive action based on the peak temperature.

[0211] In embodiment 201, the subject matter of embodiment 200 may be configured such that the operations further include determining that the peak temperature exceeds a peak temperature threshold, wherein the responsive action includes interrupting the first sensor period.

[0212] In Example 202, the subject matter of any one or more of Examples 200-201 can be configured such that the operation further includes determining an initial sensor period parameter based at least in part on the peak temperature; receiving raw sensor data from an analyte sensor of the analyte sensor system; and generating an analyte concentration value using the initial period parameter and the raw sensor data.

[0213] In Example 203, the subject matter of any one or more of Examples 200-202 may be configured such that the initial sensor period parameter includes a sensitivity or a baseline.

[0214] In Example 204, the subject matter of any one or more of Examples 200-203 can be configured such that the operation further includes: measuring a first temperature at the analyte sensor system before the first sensor period; writing the first temperature to a periodic temperature record; waiting for a period; and measuring a second temperature at the analyte sensor system.

[0215] An example ("Example 205") of a subject matter (e.g., a method, system, or apparatus) may include a temperature-sensing analyte sensor system. The temperature-sensing analyte sensor system may include: a diode; and electronic device circuitry; a sample-and-hold circuit; and a dual-slope integrated analog-to-digital converter (ADC). The electronic circuitry may be configured to perform operations including: applying a first current to the diode during a first time period, wherein a voltage drop across the diode when the first current is applied to the diode has a first voltage value; and applying a second current, different from the first current, to the diode during a second time period after the first time period, wherein the voltage drop across the diode when the second current is applied to the diode has a second voltage value. The sample-and-hold circuitry may be configured to receive the first voltage value when a first voltage is applied to the diode and generate an output indicative of the first voltage. The dual-slope integrated analog-to-digital converter (ADC) may include: a first input coupled to receive the first voltage value from an output of the sample-and-hold circuit; and a second input coupled to receive the voltage drop across the diode. The time it takes for the output of the dual-slope integrated ADC to decay from the first voltage value to the second voltage value may be proportional to the temperature at the diode.

[0216] In embodiment 206, the subject matter of embodiment 205 may further include a comparator coupled to compare the output of the sample and hold circuit with the output of the dual-slope integrated analog-to-digital circuit.

[0217] In embodiment 207, the subject matter of any one or more of embodiments 205-206 may further include a digital counter. The operations may further include starting the digital counter at a peak output of the dual-slope integrated ADC; and determining a value of the digital counter when the output of the comparator changes.

[0218] In Example 208, the subject matter of any one or more of Examples 205-207 may be configured such that the value of the digital counter indicates the time for the output of the dual slope integrating ADC to decay from the first voltage value to the second voltage value is proportional to the temperature at the diode.

[0219] In Example 209, the subject matter of any one or more of Examples 205-208 may further include an AND circuit configured to generate a logic between the output of the comparator and a clock signal, wherein the clock signal is low when the first current is applied to the diode.

[0220] In Example 210, the subject matter of any one or more of Examples 205-209 may be configured such that the diode comprises a diode-connected transistor.

[0221] In Example 211, the subject matter of any one or more of Examples 205-210 can be configured such that the analyte sensor of the analyte sensor is inserted into the skin of a host and the diode is positioned proximate to the skin of the host.

[0222] In Example 212, the subject matter of any one or more of Examples 205-211 may also include a first constant current source providing a first current; and a second pulse current source, wherein when the second pulse current source is turned on, the second current includes the sum of the first current and the current provided by the second pulse current source.

[0223] An instance (“Embodiment 213”) of a subject matter (e.g., a method, system, or device) may include: applying a first current to a diode during a first time period, wherein a voltage drop across the diode has a first voltage value when the first current is provided to the diode; after the first time period, applying a second current different from the first current to the diode, wherein the voltage drop across the diode has a second voltage value when the second current is provided to the diode; and providing the first voltage value and the second voltage value to a dual-slope integrated analog-to-digital converter (ADC), wherein the time for the output of the dual-slope integrated ADC to decay from the first voltage value to the second voltage value is proportional to the temperature at the diode.

[0224] In Example 214, the subject matter of Example 213 can include comparing the output of the sample and hold circuit with the output of the dual slope integrated analog-to-digital circuit to generate a comparator output.

[0225] In Example 215, the subject matter of any one or more of Examples 213-214 may include starting a digital counter at a peak output of the dual-slope integrating ADC; and determining a value of the digital counter when the output of the comparator changes.

[0226] In Example 216, the subject matter of any one or more of Examples 213-215 can be configured such that the value of the digital counter indicates that the time for the output of the dual-slope integrating ADC to decay from the first voltage value to the second voltage value is proportional to the temperature at the diode.

[0227] In Example 217, the subject matter of any one or more of Examples 213-216 may include an AND circuit configured to generate a logic between the output of the comparator and a clock signal. When the first current is applied to the diode, the clock signal may be low.

[0228] In Example 218, the subject matter of any one or more of Examples 213-217 can be configured such that the diode comprises a diode-connected transistor.

[0229] In Example 219, the subject matter of any one or more of Examples 213-218 can be configured such that the analyte sensor of the analyte sensor is inserted into the skin of a host, and wherein the diode is positioned proximate to the skin of the host.

[0230] An example ("Embodiment 220") of a subject matter (e.g., a method, system, or device) can include a method of determining a glucose concentration level. The method can include receiving a temperature sensor signal; receiving a glucose sensor signal from a glucose sensor inserted at an insertion site of a host; and applying the temperature sensor signal and the glucose sensor signal to a model describing a difference between a glucose concentration at the insertion site and a blood glucose concentration of the host to generate a compensated blood glucose concentration of the host.

[0231] In Example 221, the subject matter of Example 220 can include determining a model time parameter based at least in part on the temperature sensor signal; and determining the compensated blood glucose concentration based at least in part on the model time parameter.

[0232] In Example 222, the subject matter of any one or more of Examples 220-221 can be configured such that the model time parameters are adapted to the glucose concentration at the insertion site and to the blood glucose concentration.

[0233] In Example 223, the subject matter of any one or more of Examples 220-222 can further include determining glucose consumption of the host. The compensated blood glucose concentration can be determined based at least in part on the glucose consumption.

[0234] In Example 224, the subject matter of any one or more of Examples 220-223 can further include using a constant cell layer glucose concentration to determine glucose consumption.

[0235] In Example 225, the subject matter of any one or more of Examples 220-224 can further include using variable cell layer glucose concentrations to determine glucose consumption.

[0236] In Example 226, the subject matter of any one or more of Examples 220-225 can include determining glucose consumption using a linearly varying cell layer glucose concentration.

[0237] An example ("Embodiment 227") of a subject matter (e.g., a method, system, or device) can include a temperature-compensated glucose sensor system. The temperature-compensated glucose sensor system can include: a glucose sensor; and sensor electronics. The sensor electronics can be configured to perform operations including: receiving a temperature sensor signal; receiving a glucose sensor signal from a glucose sensor at an insertion site inserted into a host; and applying the temperature sensor signal and the glucose sensor signal to a model describing a difference between a glucose concentration at the insertion site and a host's blood glucose concentration to generate a compensated blood glucose concentration for the host.

[0238] In Example 228, the subject matter of Example 227 is configured such that the operations further include determining a model time parameter based at least in part on the temperature sensor signal; and determining the compensated blood glucose concentration based at least in part on the model time parameter.

[0239] In Example 229, the subject matter of any one or more of Examples 227-228 can be configured such that the model time parameters are adapted to the glucose concentration at the insertion site and to the blood glucose concentration.

[0240] In Example 230, the subject matter of any one or more of Examples 227-229 can be configured such that the operations further comprise determining a glucose consumption profile of the host, wherein the compensated blood glucose concentration is based at least in part on the glucose consumption.

[0241] In Example 231, the subject matter of any one or more of Examples 227-230 can be configured such that the operations further comprise determining glucose consumption using a constant cell layer glucose concentration.

[0242] In Example 232, the subject matter of any one or more of Examples 227-230 can be configured such that the operations further comprise determining glucose consumption using variable cell layer glucose concentrations.

[0243] In Example 233, the subject matter of any one or more of Examples 227-231 can be configured such that the operations further comprise determining glucose consumption using a linearly varying cell layer glucose concentration.

[0244] An instance (e.g., “Example 172”) of a subject matter (e.g., a system or apparatus) may optionally be combined with any portion or combination of any portion of any one or more of Examples 1-171 to include any portion for performing any one or more of the functions or methods of Examples 1-171, or a “machine-readable medium” (e.g., a general purpose, non-transitory medium, etc.) including instructions that, when executed by a machine, cause the machine to perform any portion of any one or more of the functions or methods of Examples 1-171.

[0245] This summary is intended to provide an overview of the subject matter of this patent application. It is not intended to provide an exclusive or exhaustive explanation of the present disclosure. The detailed description is included to provide more information about this patent application. Other aspects of the present disclosure will become apparent to those skilled in the art upon reading and understanding the following detailed description and examining the accompanying drawings, which form a part hereof, each of which is not to be construed in a limiting sense. BRIEF DESCRIPTION OF THE DRAWINGS

[0246] In the accompanying drawings, which are not necessarily drawn to scale, like numbers may describe similar components in different views. Like numbers with different letter suffixes may represent different instances of similar components. The accompanying drawings generally illustrate various embodiments discussed in this document by way of example and not limitation.

[0247] Figure 1 is an illustration of an exemplary analyte sensor system that may include a temperature sensor and in which a temperature compensation method may be implemented.

[0248] Figure 2A is a schematic illustration of an exemplary analyte sensor system.

[0249] Figure 2B is a schematic illustration of an exemplary sensor electronics portion of an analyte sensor system.

[0250] Figure 2C is a schematic illustration of an exemplary analyte sensor system interfaced with tissue of a host.

[0251] Figure 2Dis a schematic illustration of an exemplary analyte sensor system interfaced with tissue of a host.

[0252] Figure 3 is a schematic illustration of a temperature sensor on the distal portion of the analyte sensor.

[0253] Figure 4 is a schematic illustration of an exemplary temperature sensor on the proximal portion of an analyte sensor.

[0254] Figure 5A is a schematic illustration of another exemplary temperature sensor on the proximal portion of the analyte sensor.

[0255] Figure 5B yes Figure 5A An enlarged view of a portion of the temperature sensor is shown in FIG.

[0256] Figure 6 is a flow chart illustration of an exemplary method for determining temperature-compensated glucose concentration levels using a delay parameter.

[0257] Figure 7 is a flow chart illustration of an exemplary method for determining a temperature-compensated glucose concentration level based on an estimated (eg, verified) temperature value.

[0258] Figure 8 is a schematic illustration of an exemplary method for temperature compensating a continuous glucose sensor, the method including determining a mode from temperature information.

[0259] Figure 9 is a flow chart illustration of an exemplary method for temperature compensating a continuous glucose monitoring system based at least in part on detected conditions.

[0260] Figure 10 is a schematic illustration of a method for temperature compensation of a continuous glucose sensor system using a reference temperature value.

[0261] Figure 11 is a flow chart illustration of a temperature compensation method for an exemplary continuous glucose sensor.

[0262] Figure 12 is a flow chart illustration of an exemplary method for temperature compensation using two temperature sensors.

[0263] Figure 13 is a flow chart illustration of an exemplary method for determining a restart of a continuous glucose (or other analyte) monitor.

[0264] Figure 14 is a flow chart illustration of an exemplary method of determining an anatomical location of a sensor.

[0265] Figure 15A Shown is the output of a glucose sensor plotted against time.

[0266] Figure 15B The output of the temperature sensor is shown plotted against time.

[0267] Figure 15C The temperature is shown superimposed on the glucose sensor output, where the correlation is clear.

[0268] Figure 15D The temperature is shown superimposed on the glucose sensor output, where the correlation is not apparent.

[0269] Figure 16 is a graphical illustration showing temperature versus time for a sensor on a host's abdomen and a sensor on a host's arm.

[0270] Figure 17 is a graph of the standard deviation versus the average temperature for a number of sensor devices over the previous 24 hours.

[0271] Figure 18A is a graph of temperature versus time with the sensor electronics package removed from the sensor for one minute.

[0272] Figure 18B is a graph of temperature versus time with the sensor electronics package removed from the sensor for five minutes.

[0273] Figure 19 is a schematic illustration of an exemplary model that may be used to determine an output from two or more inputs, which may be received at different points in time.

[0274] Figure 20A is a flow chart illustration of an exemplary method for determining compensated glucose concentration values ​​using a model.

[0275] Figure 20B is a flow chart illustration of another exemplary method for determining compensated glucose concentration values ​​using a model.

[0276] Figure 21 is a graph showing temperature and impedance plotted against time.

[0277] Figure 22 is a flow chart illustration of an exemplary method for temperature compensation using conductivity or impedance.

[0278] Figure 23 is a flow chart illustration of an exemplary method for estimating subsequent temperature using conductivity or impedance measurements.

[0279] Figure 24is a flowchart illustration of an exemplary method for training a temperature compensation model.

[0280] Figure 25 is a flow chart illustration of an exemplary method of utilizing a trained temperature compensation model.

[0281] Figure 26 is a flowchart illustration of an exemplary method for detecting motion states.

[0282] Figure 27 is a graph showing a first change distribution function showing a host in a stationary (eg, non-moving) state and a second change distribution function showing a host in a moving state.

[0283] Figure 28 is a flowchart illustration of an exemplary method for detecting motion conditions using a rate-of-change distribution of temperature parameter signal samples.

[0284] Figure 29 is a flow chart illustration of an exemplary method for recording temperature at an analyte sensor system during transport.

[0285] Figure 30 is a flow chart illustration of an example method for beginning a sensor session with an analyte sensor session that includes recording of periodic temperature measurements from transport and / or storage of an analyte sensor system.

[0286] Figure 31 is a diagram of an example circuit arrangement that may be implemented at an analyte sensor system to measure temperature using a diode.

[0287] Figure 32 is a flow chart illustration of a method of measuring temperature at an analyte sensor system using a diode.

[0288] Figure 33 An exemplary sensor insertion site is illustrated showing a cell layer between the sensor insertion site and the host capillary site. DETAILED DESCRIPTION

[0289] Since the estimated glucose concentration level obtained from a glucose sensor can be used to determine the effectiveness of a therapy or to evaluate a therapy, the accuracy of a glucose sensor is important for patients, caregivers, and clinicians. There are many factors that can affect the accuracy of a glucose sensor. One factor is temperature. The inventors of the present invention have recognized that, among other things, steps can be taken to compensate for the effects of temperature on the glucose sensor, which can improve the performance of the sensor system by improving the accuracy of the estimated glucose level, thereby reducing the mean absolute relative deviation (MARD) of the sensor system. The MARD value within the effective or indicative range of glucose levels is a common method for describing the precision and accuracy of glucose measurements performed by a glucose sensing system. MARD is the result of a mathematical calculation that measures the average difference between the estimated glucose concentration level produced by a glucose sensor and a reference measurement value. The lower the MARD, the more accurate the device under consideration.

[0290] definition

[0291] To facilitate understanding of the various examples, a number of additional terms are defined below.

[0292] As used herein, the term "about" is a broad term and has its ordinary and customary meaning to those of ordinary skill in the art (and is not limited to a special or customary meaning), and when combined with a number of values ​​or ranges, it means, but is not limited to, the understanding that the amount or condition modified by the term may vary somewhat beyond the stated amount so long as the function of the embodiment is achieved.

[0293] As used herein, the term "A / D converter" is a broad term and has its ordinary and customary meaning to those skilled in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, hardware and / or software that converts analog electrical signals into corresponding digital signals.

[0294] As used herein, the term "analyte" is a broad term and has its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, an analyzable substance or chemical component in a biological fluid (e.g., blood, interstitial fluid, cerebrospinal fluid, lymph, or urine). Analytes can include natural substances, artificial substances, metabolites, and / or reaction products. In some embodiments, the analyte measured by the sensor head, device, and method disclosed herein is glucose. However, other analytes are also contemplated, including but not limited to lactate; bilirubin; ketones; carbon dioxide; sodium; potassium; acarbose; acylcarnitines; adenine phosphoribosyltransferase; adenosine deaminase; albumin; alpha-fetoprotein; amino acid profile (arginine (Krebs cycle), histidine / uridine, homocysteine, phenylalanine / tyrosine, tryptophan); androstenedione, antipyrine; arabinitol enantiomers; arginase; benzoylthreonine (cocaine); biotinidase; biopterin; C-reactive protein; carnitine; carnosinase; CD4; ceruloplasmin; chenodeoxycholic acid; chloride Quinone; cholesterol; cholinesterase; conjugated 1-beta-hydroxycholic acid; cortisol; creatine kinase; creatine kinase MM isoenzyme; cyclosporine A; d-penicillamine; desethylchloroquine; dehydroepiandrosterone sulfate; DNA (acetyltransferase polymorphism, alcohol dehydrogenase, alpha-1-antitrypsin, cystic fibrosis, Duchenne / Becker muscular dystrophy, analyte-6-phosphate dehydrogenase, hemoglobin A, hemoglobin S, hemoglobin C, hemoglobin E, D-Punjab, beta-thalassemia, hepatitis B virus, HCMV, HIV-1, HTLV-1, Leber genetics optic neuropathy, MCAD, RNA, PKU, vivax malaria, sexual differentiation, 21-deoxycortisol); desbutylhalofantrine; dihydropteridine reductase; diphtheria / tetanus antitoxin; erythrocyte arginase; erythrocyte protoporphyrin; esterase D; fatty acids / acylglycines; free beta-human chorionic gonadotropin; free erythrocyte porphyrin; free thyroxine (FT4); free triiodothyronine (FT3); fumarate acetoacetase; galactose / galactose-1-phosphate; galactose-1-phosphate uridyltransferase; gentamicin; analyte-6-phosphate dehydrogenase; glutathione peptides; glutathione peroxidase; glycocholic acid; glycated hemoglobin; haloaromatic bases; hemoglobin variants; hexosaminidase A; human erythrocyte carbonic anhydrase I; 17-alpha-hydroxyprogesterone; hypoxanthine phosphoribosyltransferase; immunoreactive trypsin; lactate; lead; lipoprotein ((a), A-1, beta); lysozyme; mefloquine; netilmicin; phenobarbital; phenytoin; phytate / inositol hexaphosphate; progesterone; prolactin; proline disaccharidase; purine nucleoside phosphorylase; quinine; reverse triiodothyronine (rT3); selenium; serum pancreatic lipase; ciprofloxacin; growth hormone C;Specific antibodies (adenovirus, antinuclear antibody, anti-zeta antibody, arbovirus, Oxygen virus, dengue virus, Guinea worm, Echinococcus granulosus, Entamoeba histolytica, enterovirus, Giardia lamblia, Helicobacter pylori, hepatitis B virus, herpes virus, HIV-1, IgE (atopic disease), influenza virus, Leishmania donovani, Leptospira, measles / mumps / rubella, Mycobacterium leprae, Mycoplasma pneumoniae, myoglobin, Onchocerca volvulus, parainfluenza virus, Plasmodium falciparum, polio-specific antigen, respiratory tract infection, Respiratory syncytial virus, Rickettsia (tsutsugamushi), Schistosoma mansoni, Toxoplasma gondii, Treponema pallidum, Trypanosoma cruzi / Trypanosoma langei, Vesicular stomatitis virus, Bancroftian filariasis, Yellow fever virus); specific antigens (Hepatitis B virus, HIV-1); succinylacetone; sulfadoxine; theophylline; thyroid-stimulating hormone (TSH); thyroxine (T4); thyroxine-binding globulin; trace elements; transferrin; UDP-galactose-4-epimerase; urea; uroporphyrinogen type I synthase; vitamin A; leukocytes; and zinc protoporphyrin. In certain embodiments, naturally occurring salts, sugars, proteins, fats, vitamins, and hormones in blood or tissue fluid may also constitute analytes.

[0295] The analyte may be naturally present in biological fluids (e.g., metabolites, hormones, antigens, antibodies, etc.). Alternatively, the analyte may be introduced into the body, such as a contrast agent for imaging, a radioisotope, a chemical reagent, a fluorocarbon-based synthetic blood, a pharmaceutical agent, or a pharmaceutical composition, including but not limited to: insulin; ethanol; cannabis (marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide, amyl nitrite, butyl nitrite, chlorinated hydrocarbons, hydrocarbons); cocaine (crack cocaine); stimulants (amphetamine, methamphetamine, Ritalin, Cialis, Prevodine, Dextromethorphan, Dextromethorphan, Forlanil, Dextromethorphan, Priligy); sedatives ( Barbiturates, methaqualones, tranquilizers such as diazepam, chlordiazepoxide, Milton, Cyrax, Anil, and Anthracene); hallucinogens (phencyclidine, lysergic acid, mescaline, peyote, psilocybin); narcotics (heroin, codeine, morphine, opium, meperidine, acetaminophen, oxycodone, hydrocodone, fentanyl, dapoxetine, talwin, lomotidine); designer drugs (fentanyl, meperidine, amphetamine, methamphetamine, and analogs of phencyclidine, such as ecstasy); anabolic steroids; and nicotine. Metabolites of drugs and drug combinations are also considered analytes. Analytes such as neurochemicals and other chemicals produced in the body can also be analyzed, such as, for example, ascorbic acid, uric acid, dopamine, norepinephrine, 3-methoxytyramine (3MT), 3,4-dihydroxyphenylacetic acid (DOPAC), homovanillic acid (HVA), serotonin (5HT), and 5-hydroxyindoleacetic acid (FHIAA).

[0296] As used herein, the term "baseline" is a broad term and has its ordinary and customary meaning (and is not limited to a special or customized meaning) to one of ordinary skill in the art, and refers to a component of the analyte sensor signal that is not related to the analyte concentration, but is not limited to. In one example of a glucose sensor, the baseline is essentially composed of signal contributions generated by factors other than glucose (e.g., interfering substances, hydrogen peroxide that is not related to the reaction, or other electroactive substances whose oxidation potential overlaps with hydrogen peroxide). In some embodiments, calibration can be defined by solving the equation y=mx+b, where the value of b represents the baseline of the signal. In certain embodiments, the value of b (i.e., the baseline) may be zero or approximately zero. For example, this may be the result of a baseline-subtracted electrode or a low bias potential setting. Thus, for these embodiments, calibration can be defined by solving the equation y=mx.

[0297] As used herein, the term "biological sample" is a broad term and has its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, a sample derived from the body or tissue of a host, such as blood, interstitial fluid, cerebrospinal fluid, saliva, urine, tears, sweat, or other similar fluids.

[0298] As used herein, the term "calibration" is a broad term and has its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, the process of determining the calibration of a sensor that gives a quantitative measurement (e.g., analyte concentration). As an example, calibration can be updated or recalibrated over time to account for changes associated with the sensor, such as changes in sensor sensitivity and sensor background. Additionally, calibration of a sensor can involve automated self-calibration, i.e., calibration without the need for the use of a reference analyte value after use.

[0299] As used herein, the term "co-analyte" is a broad term and has its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customary meaning), and refers to, but is not limited to, a molecule required to react with the analyte in an enzymatic reaction and an enzyme to form a specific product that is measured. In one embodiment of a glucose sensor, an enzyme, glucose oxidase (GOX), is provided to react with glucose and oxygen (co-analyte) to form hydrogen peroxide.

[0300] As used herein, the term "comprising" is synonymous with "including," "containing," or "characterized by," and is inclusive or open-ended and does not exclude additional unrecited elements or method steps.

[0301] As used herein, the term "computer" is a broad term and has its ordinary and customary meaning to those skilled in the art (and is not limited to a special or customary meaning) and refers to, but is not limited to, a machine that can be programmed to manipulate data.

[0302] As used herein, the terms "continuous analyte sensor" and "continuous glucose sensor" are broad terms and have their ordinary and customary meaning to one of ordinary skill in the art (and are not limited to special or customized meanings) and refer to, but are not limited to, devices that continuously or constantly measure analyte / glucose concentration, for example, at time intervals from a fraction of a second to, for example, 1 minute, 2 minutes, or 5 minutes or longer, and / or devices that calibrate the device (e.g., by continuously or constantly adjusting or determining the sensitivity and background of the sensor).

[0303] As used herein, the phrase "continuous glucose sensing" is a broad term and has its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, periods of continuous or ongoing monitoring of plasma glucose concentration, for example, at time intervals ranging from a fraction of a second to, for example, 1 minute, 2 minutes, or 5 minutes or longer.

[0304] As used herein, the term "counter" is a broad term and has its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customary meaning), and refers to, but is not limited to, a unit that measures digital signals. In one example, the raw data stream measured in the counter is directly related to a voltage (e.g., converted by an A / D converter), which is directly related to the current from the working electrode.

[0305] As used herein, the term "distal" is a broad term and has its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customized meaning) and refers to, but is not limited to, a space relatively far from a reference point such as an origin or attachment point.

[0306] As used herein, the term "domain" is a broad term and has its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customized meaning) and refers to, but is not limited to, a region of a film that can be a plurality of layers, a uniform or non-uniform gradient (e.g., anisotropic), a functional aspect of a material, or provided as part of a film.

[0307] As used herein, the term "electrical conductor" is a broad term and has its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customary meaning), and refers to, but is not limited to, materials containing movable electric charges. When a potential difference is applied between points on a conductor, the mobile charges within the conductor are forced to move, and an electric current appears between those points according to Ohm's law.

[0308] As used herein, the term "conductivity" is a broad term and has its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customary meaning), and refers to, but is not limited to, the tendency of a material to behave as an electrical conductor. In some embodiments, the term refers to an amount of conductivity (e.g., a material property) sufficient to provide the necessary function (conductivity).

[0309] As used herein, the terms "electrochemically active surface" and "electroactive surface" are broad terms and have their ordinary and customary meaning to one of ordinary skill in the art (and are not limited to a special or customary meaning), and refer to, but are not limited to, an electrode surface where an electrochemical reaction occurs. In one embodiment, the working electrode measures hydrogen peroxide (H2O2), generating a measurable current.

[0310] As used herein, the term "electrode" is a broad term and has its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, a conductor through which electrical current passes into or out of something such as a battery or a piece of electrical equipment. In one embodiment, an electrode is a metallic portion of a sensor (e.g., an electrochemically active surface) that is exposed to the extracellular environment to detect an analyte. In some embodiments, the term electrode includes a wire or conductive trace that electrically connects the electrochemically active surface to a connector (for connecting the sensor to an electronic device) or to an electronic device.

[0311] As used herein, the term "elongated conductive body" is a broad term and has its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customary meaning), and refers to, but is not limited to, an elongated body formed at least in part of a conductive material, and includes any number of coatings that may be formed thereon. For example, an "elongated conductive body" may refer to a bare elongated conductive core (e.g., a metal wire) or an elongated conductive core coated with one, two, three, four, five, or more layers of material, each of which may be conductive or non-conductive.

[0312] As used herein, the term "enzyme" is a broad term and has its ordinary and customary meaning (and is not limited to a special or customized meaning) to those of ordinary skill in the art, and refers to, but is not limited to, proteins or protein-based molecules that can accelerate chemical reactions occurring in organisms. An enzyme can act as a catalyst for a single reaction, converting a reactant (also referred to herein as an analyte) into a specific product. In one embodiment of a glucose oxidase-based sensor, an enzyme, glucose oxidase (GOX), is provided to react with glucose (analyte) and oxygen to form hydrogen peroxide.

[0313] As used herein, the term "filtering" is a broad term and has its ordinary and customary meaning to those skilled in the art (and is not limited to a special or customized meaning) and refers to, but is not limited to, modifying a data set to make it smoother and more continuous and to remove or reduce outliers, for example, by performing a moving average of the original data stream.

[0314] As used herein, the term "function" is a broad term and has its ordinary and customary meaning to persons of ordinary skill in the art (and is not limited to a special or custom meaning) and refers to, but is not limited to, an action or use for which something is adapted or designed.

[0315] As used herein, the term "GOx" is a broad term and has its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customary meaning) and refers to, but is not limited to, glucose oxidase (e.g., GOx is an abbreviation).

[0316] As used herein, the term "host" is a broad term and has its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customary meaning) and refers to, but is not limited to, animals, including humans.

[0317] As used herein, the term "inactive enzyme" is a broad term and has its ordinary and customary meaning to those of ordinary skill in the art (and is not limited to a special or customary meaning), and refers to, but is not limited to, an enzyme that has been rendered inactive (e.g., by denaturing the enzyme) and has substantially no enzymatic activity (e.g., glucose oxidase, GOx). The enzyme can be inactivated using a variety of techniques known in the art, such as, but not limited to, heating, freeze-thawing, denaturation in organic solvents, acids or bases, cross-linking, genetically altering enzymatically critical amino acids, and the like. In some embodiments, a solution containing an active enzyme can be applied to the sensor and then inactivated by heating or treatment with an inactivating solvent.

[0318] As used herein, the terms "insulating properties," "electrical insulator," and "insulator" are broad terms and have their ordinary and customary meanings to one of ordinary skill in the art (and are not limited to special or customized meanings), and refer to, but are not limited to, a material's lack of tendency to move electric charge to prevent charge from moving between two points. In one embodiment, an electrically insulating material can be placed between two conductive materials to prevent electricity from moving between the two conductive materials. In some embodiments, the terms refer to an amount of insulating properties (e.g., insulating properties of a material) sufficient to provide the necessary function (electrical insulation). The terms "insulator" and "non-conductive material" may be used interchangeably herein.

[0319] As used herein, the term "in vivo portion" is a broad term and has its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customary meaning), and refers to, but is not limited to, a portion of a device to be implanted or inserted into a host. In one embodiment, the in vivo portion of a transcutaneous sensor is the portion of the sensor that is inserted through the host's skin and remains within the host's body.

[0320] As used herein, the term "membrane system" is a broad term and has its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customary meaning), and refers to, but is not limited to, a permeable or semi-permeable membrane that can include two or more domains and is typically composed of a material having a thickness of several microns or more, the material being permeable to oxygen and optionally permeable to glucose. In one example, the membrane system includes immobilized glucose oxidase that is capable of undergoing an electrochemical reaction to measure glucose concentration.

[0321] As used herein, the term "operably connected" is a broad term and has its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, connecting one or more components to another component or components in a manner that allows signals to be transmitted between the components. For example, one or more electrodes can be used to detect the amount of glucose in a sample and convert this information into a signal; the signal can then be transmitted to an electronic circuit. In this case, the electrodes are "operably linked" to the electronic circuit. These terms are broad enough to encompass both wired and wireless connectivity.

[0322] As used herein, the term "potentiostat" is a broad term and has its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, an electrical system that applies a preset value of potential between a working electrode and a reference electrode of a two-electrode or three-electrode cell and measures the current flow through the working electrode. As long as the desired cell voltage and current do not exceed the compliance limits of the potentiostat, the potentiostat will force any current required to flow between the working electrode and the counter electrode to maintain the desired potential.

[0323] As used herein, the terms "processor module" and "microprocessor" are broad terms and have their ordinary and customary meanings to those of ordinary skill in the art (and they are not limited to special or customized meanings), and refer to, but are not limited to, computer systems, state machines, processors, etc., which are designed to perform arithmetic and logical operations using logic circuits that respond to and process the basic instructions that drive the computer.

[0324] As used herein, the term "proximal" is a broad term and has its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customized meaning) and refers to, but is not limited to, proximity to a reference point such as an origin or attachment point.

[0325] As used herein, the terms "raw data stream" and "data stream" are broad terms and have their ordinary and customary meanings to those of ordinary skill in the art (and are not limited to special or customized meanings), and refer to, but are not limited to, analog or digital signals directly related to the analyte concentration measured by an analyte sensor. In one example, a raw data stream is digital data in the form of counts converted by an A / D converter from an analog signal representing the analyte concentration (e.g., voltage or amperes). These terms broadly encompass data points from a substantially continuous analyte sensor at multiple time intervals, including, for example, individual measurements taken at time intervals ranging from a fraction of a second to, for example, 1 minute, 2 minutes, or 5 minutes or longer.

[0326] As used herein, the term "RAM" is a broad term and has its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customary meaning), and refers to, but is not limited to, data storage devices in which the order in which different locations are accessed does not affect the speed of access. RAM is broad enough to include, for example, SRAM, which is a type of static random access memory that retains data bits in its memory as long as power is supplied.

[0327] As used herein, the term "ROM" is a broad term and has its ordinary and customary meaning to those skilled in the art (and is not limited to a special or customary meaning), and refers to, but is not limited to, animal read-only memory, which is a type of data storage device made with fixed contents. ROM is broad enough to include EEPROM, for example, electrically erasable programmable read-only memory (ROM).

[0328] As used herein, the terms "reference analyte value" and "reference data" are broad terms and have their ordinary and customary meanings to those of ordinary skill in the art (and are not limited to special or customized meanings), and refer to, but are not limited to, reference data from a reference analyte monitor (such as a blood glucose meter, etc.), including one or more reference data points. In some embodiments, the reference glucose value is obtained from, for example, a self-monitored blood glucose (SMBG) test (e.g., from a finger or forearm blood test) or a YSI (Yellow Springs Instruments) test.

[0329] As used herein, the term "regression" is a broad term and has its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, finding a line where a set of data has a minimum measured value (e.g., deviation) from the line. Regression can be linear, nonlinear, first order, second order, etc. An example of a regression is least squares regression.

[0330] As used herein, the term "sensing region" is a broad term and has its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, the area of ​​a monitoring device responsible for detecting a specific analyte. In one embodiment, the sensing region may include: a non-conductive body; at least one electrode; a reference electrode; and optionally a counter electrode passing through and fixed within the body, thereby forming an electroactive surface at one location on the body and forming an electronic connection at another location on the body; and a membrane system attached to the body and covering the electroactive surface.

[0331] As used herein, the term "sensitivity" or "sensor sensitivity" is a broad term and has its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customary meaning) and refers to the amount of signal generated by a certain concentration of a measured analyte or a measured substance (e.g., H2O2) associated with a measured analyte (e.g., glucose). For example, in one embodiment, the sensor has a sensitivity of about 1 picoampere to about 300 picoamperes of current for every 1 mg / dL of glucose analyte.

[0332] As used herein, the terms "sensitivity profile" and "sensitivity curve" are broad terms and have their ordinary and customary meaning to one of ordinary skill in the art (and are not limited to a special or customized meaning) and refer to, but are not limited to, a representation of sensitivity changes over time.

[0333] As used herein, the terms "sensor" and "sensor data" are broad terms and have their ordinary and customary meaning to one of ordinary skill in the art (and are not limited to special or customized meanings) and refer to, but are not limited to, data received from a continuous analyte sensor, including sensor data points at one or more time intervals.

[0334] As used herein, the terms "sensor electronics" and "electronic circuitry" are broad terms and have their ordinary and customary meanings to one of ordinary skill in the art (and are not limited to special or customized meanings) and refer to, but are not limited to, components (e.g., hardware and / or software) of a device configured to process data. In the case of an analyte sensor, the data includes biological information obtained by the sensor regarding the concentration of an analyte in a biological fluid. U.S. Patent Nos. 4,757,022, 5,497,772, and 4,787,398 describe suitable electronic circuitry that can be used with the devices of certain embodiments.

[0335] As used herein, the term "sensor environment" or "sensor operating environment" is a broad term and has its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customized meaning) and refers to, but is not limited to, the biological environment in which the sensor operates.

[0336] As used herein, the terms "substantially" and "substantially" are broad terms and have their ordinary and customary meaning to those skilled in the art (and are not limited to a special or customary meaning) and refer to, but are not limited to, primarily but not necessarily entirely as specified.

[0337] As used herein, the term "thermal conductivity" is a broad term and has its ordinary and customary meaning to those skilled in the art (and is not limited to a special or customized meaning), and refers to, but is not limited to, the amount of heat transferred per unit time in a direction perpendicular to a surface per unit area under stable conditions due to a unit temperature gradient.

[0338] As used herein, the term "thermal conductivity" is a broad term and has its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customary meaning) and refers to, but is not limited to, the change in electrical resistance of a material at different temperatures.

[0339] As used herein, the term "thermally conductive material" is a broad term and has its ordinary and customary meaning to those of ordinary skill in the art (and is not limited to a special or customary meaning) and refers to, but is not limited to, materials that exhibit a high degree of thermal conductivity.

[0340] As used herein, the term "thermocouple" is a broad term and has its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to a special or customary meaning) and refers to, but is not limited to, a device comprising two dissimilar conductors (e.g., metal alloys) that produce a voltage between either end of the two conductors that is proportional to the temperature difference.

[0341] Overview

[0342] Some analyte sensors measure the concentration of a substance (e.g., glucose) in the body (e.g., measuring the glucose concentration in the blood or interstitial fluid at a subcutaneous location). The output of the analyte sensor can be affected by temperature. The temperature of the subcutaneous area of ​​the body where the sensor may be located can vary from person to person and can change over time in an individual. For example, subcutaneous temperature can be affected by changes in body temperature (such as fever or periodic changes) as well as changes in ambient temperature. For example, exposure to hot or cold water, wearing thermal clothing, exposure to cold weather, and sunlight can change the host's subcutaneous temperature. When conditions such as these are present, temperature changes can cause inaccuracies in the estimate of the glucose concentration level. When the host wears the sensor, the accuracy and precision of the estimated glucose concentration level can be improved by compensating for temperature fluctuations in the sensing site or sensor.

[0343] Compensating for these temperature effects can improve the performance of analyte sensor systems. For example, temperature compensation can improve sensory accuracy or reduce MARD. However, temperature compensation presents implementation challenges because it can be difficult to know the actual temperature at the sensing site or the temperature to be compensated, and the temperatures of the host body and various system components can differ from each other and vary over time.

[0344] In some instances, temperature compensation may be applied to the sensitivity value used to convert the signal from the sensor into an estimated analyte concentration level (e.g., a 3% change in sensitivity for every 1°C deviation from a reference temperature (e.g., 35°C). In some instances, temperature compensation may be applied directly to the estimated glucose value. In some cases, compensating the glucose value rather than the sensor sensitivity may produce a more accurate value. For example, in addition to changes in enzyme sensitivity, other effects may also affect the glucose concentration level or sensor response. Additional temperature effects may include changes in local glucose concentration (as opposed to systemic glucose levels), compartmental bias (differences in glucose concentration in interstitial fluid versus blood), and non-enzymatic sensor bias (e.g., an electrochemical baseline signal that is not generated by a glucose / enzyme interaction). A model can be developed to account for some or all of these additional factors, which may provide a more accurate estimate of the glucose concentration level.

[0345] Exemplary Systems

[0346] Figure 1An exemplary system 100 is depicted in which exemplary temperature compensation systems, devices, and methods can be implemented. The system 100 can include a continuous analyte sensor system 8, which includes a sensor electronics 12 and a continuous analyte sensor 10. The system 100 can include other devices and / or sensors, such as a drug delivery pump 2 (which can be communicatively coupled to the continuous analyte sensor system, for example, to implement closed-loop therapy) and a glucose meter 4, such as a blood glucose meter, which can be communicatively coupled to the continuous analyte sensor system 8. The continuous analyte sensor 10 can be physically coupled to the sensor electronics 12 and can be releasably attached to the sensor electronics 12 or integral with the sensor electronics 12 (e.g., non-releasably attached thereto). The sensor electronics 12, the drug delivery pump 2, and / or the glucose meter 4 can also be coupled to one or more devices (e.g., display devices 14, 16, 18, and / or 20).

[0347] In some exemplary embodiments, the system 100 may include a cloud-based analyte processor 490 configured to analyze analyte data (and / or other patient-related data) from the sensor system 8 and other devices associated with a host (also referred to as a subject or patient) (e.g., display devices 14 to 20 and the like) provided via a network 406 (e.g., via wired, wireless, or a combination thereof) and generate a report that provides high-level information (e.g., statistics) related to the measured analytes over a certain timeframe. A full discussion of the use of a cloud-based analyte processing system can be found in U.S. Patent Publication No. US-2013-0325352-A1, filed March 7, 2013, entitled "Cloud-Based Processing of Analyte Data," which is incorporated herein by reference in its entirety. In some embodiments, one or more steps of the temperature compensation algorithm can be performed in the cloud.

[0348] In some exemplary embodiments, the sensor electronics 12 may include electronic circuitry associated with measuring and processing data generated by the continuous analyte sensor 10. This generated continuous analyte sensor data may also include algorithms that can be used to process and calibrate the continuous analyte sensor data, but these algorithms can also be provided in other ways. The sensor electronics 12 may include hardware, firmware, software, or a combination thereof to provide measurements of analyte levels via a continuous analyte sensor (such as a continuous glucose sensor). Figure 2B An exemplary embodiment of sensor electronics 12 is further described.

[0349] In one embodiment, the temperature compensation method may be performed by the sensor electronics 12 .

[0350] As mentioned, the sensor electronics 12 may be coupled (e.g., wirelessly, etc.) to one or more devices, such as display devices 14, 16, 18, and / or 20. The display devices 14, 16, 18, and / or 20 may be configured to present information (and / or alerts), such as sensor information transmitted by the sensor electronics 12, for display at the display devices 14, 16, 18, and / or 20.

[0351] The display device may include a relatively small display device 14. In some exemplary embodiments, the relatively small display device 14 may be a key fob, a wristwatch, a belt, a necklace, a pendent, a piece of jewelry, an adhesive patch, a pager, a key card, a plastic card (e.g., a credit card), an identification (ID) card, and / or the like, or a portion thereof. This small display device 14 may include a relatively small display (e.g., smaller than the large display device 16) and may be configured to display certain types of displayable sensor information, such as numerical values ​​and arrows or color codes. The device 14 may be configured as a data receiving or tracking device 14 (e.g., a blood glucose meter or CGM receiver) and may include a communication device (e.g., a USB-B port or a wireless communication transceiver) for uploading data to another device.

[0352] In some exemplary embodiments, the relatively large handheld display device 16 may include a handheld receiver device, a palmtop computer, and / or the like. This large display device may include a relatively large display (e.g., larger than the small display device 14) and may be configured to display information, such as a graphical representation of continuous sensor data including current and historical sensor data output by the sensor system 8. The handheld display device 16 may be, for example, a CGM controller or a pump controller.

[0353] The display device may also include a mobile device 18 (e.g., a smartphone, tablet, or other smart device). The display device may also include a computer 20 and / or any other user equipment configured to at least present information (e.g., medication delivery information, discrete automatic monitoring glucose readings, heart rate monitor, calorie intake monitor, etc.).

[0354] Any display device can be coupled to the network 406 via a wired or wireless (e.g., cellular, Bluetooth, Wi-Fi, MICS, ZigBee) connection and can include a processor and memory circuitry for storing and processing information. In some examples, the temperature compensation method can be performed at least in part by one or more display devices.

[0355] In some exemplary embodiments, the continuous analyte sensor 10 may include a sensor for detecting and / or measuring an analyte, and the continuous analyte sensor 10 may be configured to continuously detect and / or measure an analyte in the form of a non-invasive device, a subcutaneous device, a transdermal device, and / or an intravascular device.

[0356] In some exemplary embodiments, while the continuous analyte sensor 10 may analyze multiple intermittent blood samples, other analytes may also be used.

[0357] In some exemplary embodiments, the continuous analyte sensor 10 may include a glucose sensor configured to measure glucose in blood or interstitial fluid using one or more measurement techniques, such as enzymatic, chemical, physical, electrochemical, spectrophotometric, polarimetric, calorimetric, iontophoresis, radiometric, immunochemical, and similar techniques. In embodiments where the continuous analyte sensor 10 includes a glucose sensor, the glucose sensor may include any device capable of measuring glucose concentration and may use a variety of techniques to measure glucose, including invasive, minimally invasive, and non-invasive sensing technologies (e.g., fluorescence monitoring) to provide data, such as a data stream, an indication of glucose concentration in the host. The data stream may be sensor data (raw and / or filtered) that can be converted into a calibrated data stream for providing glucose values ​​to the host, such as a user, patient, or caregiver (e.g., a parent, relative, guardian, teacher, doctor, nurse, or any other individual interested in the health status of the host). In addition, the continuous analyte sensor 10 may be implanted in the form of at least one of the following sensor types: an implantable glucose sensor, a transcutaneous glucose sensor, implanted in a host blood vessel or in vitro, a subcutaneous sensor, a refillable subcutaneous sensor, or an intravascular sensor.

[0358] Although the disclosure herein refers to some embodiments of the continuous analyte sensor 10 including a glucose sensor, the continuous analyte sensor 10 may also include other types of analyte sensors. In addition, although some embodiments relate to a glucose sensor in the form of an implantable glucose sensor, other types of devices capable of detecting glucose concentration and providing an output signal representing the glucose concentration may also be used. In addition, although the description herein relates to glucose as the analyte measured, processed, etc., other analytes may also be used, including, for example, ketone bodies (e.g., acetone, acetoacetic acid and beta-hydroxybutyric acid, lactate, etc.), glucagon, acetyl-CoA, triglycerides, fatty acids, intermediates in the citric acid cycle, choline, insulin, cortisol, testosterone, and the like.

[0359] Electronics of an Exemplary Analyte Sensor System

[0360] Figure 2Ais a schematic diagram of an exemplary analyte sensor system 250, which may be, for example, Figure 1 8. The analyte sensor system may include an analyte sensor, such as a glucose sensor 252, one or more temperature sensors 254, a processor 251, and a memory 256. The processor may receive a glucose sensor signal indicating a glucose concentration level from the glucose sensor 252 and a temperature sensor signal indicating a temperature parameter (e.g., an absolute or relative temperature or a temperature gradient) from the temperature sensor 254. The sensor system 250 may also include one or more additional sensors 258, which may include, for example, a heart rate sensor, an activity sensor (e.g., an accelerometer), or a pressure gauge (e.g., for measuring the pressure exerted by the sensor on the host).

[0361] The processor 251 can determine a temperature-compensated glucose concentration level (or other analyte concentration level) based on the glucose sensor signal, the temperature sensor signal, and optionally also based on one or more signals from the additional sensor 258. The processor 251 can determine a specific temperature-compensated sensitivity value (e.g., an analyte sensor sensitivity value based on temperature), or can determine a compensated estimated glucose value. The signal from the temperature sensor 254 can be used as an approximation of the temperature at the analyte sensor, or the signal from the temperature sensor 254 can be processed (e.g., using a method described in detail below) to determine an estimated analyte temperature sensor based on the signal from the temperature sensor 254. In some instances, the processor can retrieve instructions or information from the memory 256 to determine the temperature-compensated glucose concentration level. For example, the processor can access a lookup table, or apply an algorithm based on the glucose sensor signal and the temperature sensor signal, or apply the glucose sensor signal and the temperature signal to a model (e.g., using a state model or a neural network). In some instances, the processor can retrieve executable instructions from the memory 256 (or a separate memory that can be operably coupled to the processor or integrated into the processor). In some examples, the processor may include or be part of an application specific integrated circuit (ASIC) that may be configured to determine a temperature compensated glucose concentration level. In various examples, the processes described herein or in conjunction with the temperature compensated glucose sensor may be performed by the processor 251 or the temperature compensated glucose sensor alone or in combination with other processors or devices, such as the device shown in FIG. Figure 6-14 Any one or more of the methods shown in .

[0362] Figure 2B A more detailed description of an exemplary sensor electronics 12 is depicted in FIG. The sensor electronics may be, for example, Figure 1The sensor electronics 12 may include sensor electronics configured to process sensor information (e.g., sensor data) and generate converted sensor data and displayable sensor information (e.g., via the processor module 214). For example, the processor module 214 may convert the sensor data into one or more of the following: temperature compensated data, filtered sensor data (e.g., one or more filtered analyte concentration values), raw sensor data, calibrated sensor data (e.g., one or more calibrated analyte concentration values), rate of change information, trend information, ratio of acceleration / deceleration information, sensor diagnostic information, positioning information, alarm / alarm information, calibration information such as may be determined by a calibration algorithm, smoothing and / or filtering algorithms for sensor data, and / or the like.

[0363] Sensor electronics 12 may include a first temperature sensor 240. In some examples, the signal from temperature sensor 240 may be used for temperature compensation, for example, to compensate for the effects of temperature on the analyte sensor. In some examples, sensor electronics 12 may include an optional second temperature sensor 242. The signals from first temperature sensor 240 and second temperature sensor 242 may be used to determine heat flux or temperature gradient.

[0364] In some embodiments, the processor module 214 can be configured to implement the vast majority (if not all) of data processing, including data processing for factory calibration or temperature compensation. Factory calibration can be the calibration of a continuous analyte sensor that can achieve a high level of accuracy without (or reducing) reliance on reference data from a reference analyte monitor (e.g., a blood glucose meter). The processor module 214 can be integrated into the sensor electronics 12 and / or can be located at a remote end, such as at one or more of the devices 14, 16, 18, and / or 20 and / or the cloud 490. In some embodiments, the processor module 214 can include multiple smaller sub-components or sub-modules. For example, the processor module 214 can include an alarm module (not shown) or a prediction module (not shown), or any other suitable module that can be used to effectively process data. When the processor module 214 is composed of multiple sub-modules, the sub-modules can be located within the processor module 214, including within the sensor electronics 12 or other associated devices (e.g., 14, 16, 18, 20, and / or 490). For example, in some implementations, the processor module 214 may be located at least partially within the cloud-based analyte processor 490 or otherwise within the network 406 .

[0365] In some exemplary embodiments, the processor module 214 may be configured to calibrate the sensor data, and the data storage memory 220 may store the calibrated sensor data points as converted sensor data. In addition, in some exemplary embodiments, the processor module 214 may be configured to wirelessly receive calibration information from a display device (e.g., device 14, 16, 18, and / or 20) to implement calibration of the sensor data from the sensor 12. In addition, the processor module 214 may be configured to perform additional algorithmic processing on the sensor data (e.g., calibrated and / or filtered data and / or other sensor information), and the data storage memory 220 may be configured to store the converted sensor data and / or sensor diagnostic information associated with the algorithm. The processor module 214 may be further configured to store and use calibration information determined from the calibration.

[0366] In some exemplary embodiments, some or all of the sensor electronics 12 may be incorporated to include an ASIC 205 that may be coupled to a user interface 222 via a wired or wireless connection. For example, the ASIC 205 may further include a potentiostat 210, a telemetry module 232 for transmitting data from the sensor electronics 12 to one or more devices (e.g., devices 14, 16, 18, and / or 20), and / or other components for signal processing and data storage (e.g., a processor module 214 and a data storage memory 220). Although Figure 2B An ASIC 205 is depicted, but other types of circuitry may also be used, including a field programmable gate array (FPGA), one or more microprocessors configured to provide some, if not all, of the processing performed by the sensor electronics 12, analog circuitry, digital circuitry, or a combination thereof. Additionally, the ASIC 205 may include only a subset (one or more) of the devices, and any of the devices 210, 214, 216, 218, 220, 232, 240, 242 may be included in the ASIC or provided as discrete components or integrated together as a separate ASIC (e.g., as a second ASIC or a third ASIC).

[0367] exist Figure 2B In the example depicted in FIG, the potentiostat 210 can be coupled to a continuous analyte sensor 10, such as a glucose sensor, that generates sensor data from an analyte, via a first input port for sensor data. The potentiostat 210 can also provide data to an analyte sensor, such as a continuous analyte sensor 10 (shown in FIG5 ) or a glucose sensor, via a data line 212. Figure 2C 、 Figure 3 、 Figure 4 、 Figure 5A or Figure 5BThe sensor shown in FIG provides a voltage to bias the sensor to measure a value (e.g., current, etc.) indicative of the concentration of the analyte in the host (also referred to as the analog portion of the sensor). Depending on the number of working electrodes at the continuous analyte sensor 10, the potentiostat 210 may have one or more channels.

[0368] In some exemplary embodiments, the potentiostat 210 may include a resistor that converts the current value from the sensor 10 into a voltage value, and in some exemplary embodiments, a current-to-frequency converter (not shown) may also be configured to continuously integrate the measured current value from the sensor 10 using, for example, a charge counting device. In some exemplary embodiments, an analog-to-digital converter (not shown) may digitize the analog signal from the sensor 10 into so-called "counts" for processing by the processor module 214. The resulting counts may be directly correlated to the current measured by the potentiostat 210, which may be directly correlated to the analyte level (e.g., glucose level) in the host.

[0369] The telemetry module 232 can be operably connected to the processor module 214 and can provide hardware, firmware, and / or software that enables wireless communication between the sensor electronics 12 and one or more other devices (e.g., a display device, a processor, a network access device, and the like). Various radio technologies that can be implemented in the telemetry module 232 include Bluetooth, Bluetooth Low Energy, ANT, ANT+, ZigBee, IEEE 802.11, IEEE 802.16, cellular radio access technology, radio frequency (RF), infrared (IR), paging network communication, magnetic induction, satellite data communication, spread spectrum communication, frequency hopping communication, near field communication, and / or the like. In some exemplary embodiments, the telemetry module 232 can include a Bluetooth chip, but Bluetooth technology can also be implemented in combination with the telemetry module 232 and the processor module 214.

[0370] The processor module 214 may control the processing performed by the sensor electronics 12. For example, the processor module 214 may be configured to process data from the sensor (eg, counts), filter data, calibrate data, perform failsafe checks, and / or the like.

[0371] In some exemplary embodiments, the processor module 214 may include a digital filter, such as an infinite impulse response (IIR) or finite impulse response (FIR) filter. This digital filter can smooth the raw data stream received from the sensor 10. Typically, the digital filter is programmed to filter data sampled at predetermined time intervals (also known as a sampling rate). In some exemplary embodiments, such as when the potentiostat 210 is configured to measure an analyte (e.g., glucose and / or the like) at discrete time intervals, these time intervals determine the sampling rate of the digital filter. In some exemplary embodiments, the potentiostat 210 can be configured to measure the analyte continuously, for example, using a current-to-frequency converter. In these current-to-frequency converter embodiments, the processor module 214 can be programmed to request values ​​from the integrator of the current-to-frequency converter at predetermined time intervals (acquisition time). Due to the continuous nature of the current measurement, these values ​​obtained from the integrator by the processor module 214 can be averaged over the acquisition time. Thus, the acquisition time can be determined by the sampling rate of the digital filter.

[0372] The processor module 214 may further include a data generator (not shown) configured to generate data packets for transmission to devices, such as display devices 14, 16, 18, and / or 20. In addition, the processor module 214 may generate data packets for transmission from these external sources via the telemetry module 232. In some exemplary embodiments, as mentioned, the data packets may be customizable for each display device and / or may include any available data, such as temperature information or temperature-related information, temperature compensation data, accelerometer data, motion data, position data, time stamps, displayable sensor information, converted sensor data, identification codes of sensors and / or sensor electronics 12, raw data, filtered data, calibrated data, rate of change information, trend information, error detection or correction, temperature information, or any combination thereof.

[0373] The processor module 214 may also include a program memory 216 and other memory 218. The processor module 214 may be coupled to a communication interface, such as a communication port 238, and a power source, such as a battery 234. Additionally, the battery 234 may be further coupled to a battery charger and / or regulator 236 to power the sensor electronics 12 and / or charge the battery 234.

[0374] The program memory 216 may be implemented as a semi-static memory for storing data, such as an identifier of the coupled sensor 10 (e.g., a sensor identifier (ID)), and for storing code (also referred to as program code) for configuring the ASIC 205 to perform one or more of the operations / functions described herein. For example, the program code may configure the processor module 214 to process data streams or counts, filter, perform calibration methods, perform fault protection checks, and the like.

[0375] The memory 218 can also be used to store information. For example, the processor module 214 including the memory 218 can be used as a cache memory for the system, where temporary storage can be provided for receiving the latest sensor data from the sensors. In some exemplary embodiments, the memory can include memory storage components such as read-only memory (ROM), random-access memory (RAM), dynamic RAM, static RAM, non-static RAM, easily erasable programmable read only memory (EEPROM), rewritable ROM, flash memory, and the like.

[0376] The data storage memory 220 can be coupled to the processor module 214 and can be configured to store a variety of sensor information. In some example embodiments, the data storage memory 220 stores one or more days of continuous analyte sensor data. For example, the data storage memory can store 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, and / or 30 days (or more) of continuous analyte sensor data received from the sensor 10. The stored sensor information can include one or more of the following: temperature information or temperature-related information, temperature compensation data, time stamps, raw sensor data (one or more raw analyte concentration values), calibrated data, filtered data, converted sensor data, and / or any other displayable sensor information, calibration information (e.g., reference BG values ​​and / or previous calibration information such as from a factory calibration), sensor diagnostic information, temperature information, and the like.

[0377] The user interface 222 may include various interfaces, such as one or more buttons 224, a liquid crystal display (LCD) or organic light emitting diode (OLED) display 226, a vibrator 228, an audio transducer (e.g., a speaker) 230, a backlight (not shown), and / or the like. Components comprising the user interface 222 may provide controls for interacting with a user (e.g., a host). The one or more buttons 224 may implement, for example, toggles, menu selections, option selections, status selections, yes / no responses to on-screen questions, an "off" function (e.g., for alarms), an "acknowledged" function (e.g., for alarms), a reset, and / or the like. The display 226 may provide, for example, visual data output to the user. The audio transducer 230 (e.g., a speaker) may provide an audio signal in response to triggering of certain alarms (e.g., the presence and / or prediction of hyperglycemia and hypoglycemia). In some exemplary embodiments, the audio signal may be distinguished by tone, volume, duty cycle, pattern, duration, and / or the like. In some exemplary embodiments, the audio signal may be configured to be silenced (e.g., acknowledged or turned off) by pressing one or more buttons 224 on the sensor electronics 12 and / or by signaling the sensor electronics 12 using a button or selection on a display device (e.g., a key fob, cell phone, and / or the like).

[0378] Although about Figure 2B Audio and vibration alerts are described, but other alert mechanisms may be used. For example, in some exemplary embodiments, a tactile alert is provided that includes a poking mechanism configured to "poke" or physically contact the patient in response to one or more alert conditions.

[0379] The battery 234 can be operably connected to the processor module 214 (and possibly other components of the sensor electronics 12) and provide the required power to the sensor electronics 12. In some exemplary embodiments, the battery can be a lithium manganese dioxide battery, however, any battery of appropriate size and power can be used (e.g., AAA, nickel-cadmium, zinc-carbon, alkaline, lithium, nickel-metal hydride, lithium ion, zinc-air, zinc-mercury oxide, silver-zinc, or hermetically sealed). In some exemplary embodiments, the battery can be rechargeable. In some exemplary embodiments, multiple batteries can be used to power the system. In yet other embodiments, power can be supplied to the receiver transdermally, for example, via inductive coupling.

[0380] The battery charger and / or regulator 236 can be configured to receive energy from an internal and / or external charger. In some exemplary embodiments, the battery regulator (or balancer) 236 regulates the recharging process by releasing excess charging current to fully charge all cells or cells in the sensor electronics 12 without overcharging other cells or cells. In some exemplary embodiments, one or more batteries 234 can be configured to be charged via an inductive and / or wireless charging pad, but any other charging and / or powering mechanism can also be used.

[0381] One or more communication ports 238 (also referred to as external connectors) may be provided to enable communication with other devices, for example, a PC communication (com) port may be provided to enable communication with a system that is separate from or integrated with the sensor electronics 12. The communication port may include, for example, a serial (e.g., universal serial bus or "USB") communication port and enable communication with another computer system (e.g., a PC, a personal digital assistant or "PDA" server, or the like). In some exemplary embodiments, the sensor electronics 12 is capable of transmitting historical data to a PC or other computing device for retrospective analysis by the patient and / or HCP. As another example of data transmission, plant information may also be sent from the sensor or from a cloud data source to the algorithm.

[0382] The one or more communication ports 238 may further include a second input port in which calibration data may be received and an output port that may be used to transmit the calibration data or data to be calibrated to a receiver or mobile device. It should be understood that the ports may be physically separate, but in alternative embodiments, a single communication port may provide the functionality of both the second input port and the output port.

[0383] In some continuous analyte sensor systems, the on-skin portion of the sensor electronics can be simplified to minimize complexity and / or size of the on-skin electronics, such as providing only raw, calibrated, and / or filtered data to a display device configured to perform calibration and other algorithms required to display sensor data. However, the sensor electronics 12 can be implemented (e.g., via the processor module 214) to execute proactive algorithms for generating converted sensor data and / or displayable sensor information, including, for example, algorithms for evaluating clinical acceptability of reference and / or sensor data, evaluating calibration data for optimal calibration based on inclusion criteria, evaluating calibration quality, comparing estimated analyte values ​​to time-corresponding measured analyte values, analyzing changes in estimated analyte values, evaluating stability of the sensor and / or sensor data, detecting signal artifacts (noise), replacing signal artifacts, determining rates of change and / or trends in sensor data, performing dynamic and intelligent analyte value evaluations, performing diagnostics on the sensor and / or sensor data, setting operating modes, evaluating anomalous data, and / or the like.

[0384] Although Figure 2B Separate data storage and program memory are shown in FIG, but a variety of configurations may be used. For example, one or more memories may be used to provide storage space to support data processing and storage requirements at the sensor electronics 12.

[0385] In a preferred embodiment, the analyte sensor can be an implantable glucose sensor, such as described in U.S. Patent No. 6,001,067 and U.S. Patent Publication No. US-2005-0027463-A1. In another preferred embodiment, the analyte sensor can be a transcutaneous glucose sensor, such as described in U.S. Patent Publication No. US-2006-0020187-A1. In yet other embodiments, the sensor can be configured to be implanted in a host blood vessel or in vitro, such as described in U.S. Patent Publication No. US-2007-0027385-A1, U.S. Patent Publication No. US-2008-0119703-A1 (now abandoned), U.S. Patent Publication No. US-2008-0108942A1 (now abandoned), and U.S. Patent No. US 7,828,728. In an alternative embodiment, for example, the continuous glucose sensor can include a transcutaneous sensor, such as described in U.S. Patent No. 6,565,509 to Say et al. In another alternative embodiment, for example, the continuous glucose sensor may include a subcutaneous sensor, such as described in U.S. Patent No. 6,579,690 to Bonnecaze et al. or U.S. Patent No. 6,484,046 to Say et al. In another alternative embodiment, for example, the continuous glucose sensor may include a refillable subcutaneous sensor, such as described in U.S. Patent No. 6,512,939 to Colvin et al. In another alternative embodiment, the continuous glucose sensor may include an intravascular sensor, such as described in U.S. Patent No. 6,477,395 to Schulman et al. In another alternative embodiment, the continuous glucose sensor may include an intravascular sensor, such as described in U.S. Patent No. 6,424,847 to Mastrototaro et al.

[0386] Figure 2C2 is a schematic diagram of an exemplary analyte sensor system 8, which shows an analyte sensor 10 inserted through the epidermis 260, the dermis 262 and into the subcutaneous layer 264, so that the distal end 280 of the analyte sensor 10 is in the subcutaneous layer. In a human host, the epidermis 260 may typically be about 0.01 cm thick, the dermis 262 may typically be about 0.2 cm thick, and the subcutaneous layer may be thicker, for example, 1 cm to 1.5 cm. The working portion 282 (e.g., a working electrode) of the analyte sensor 10 may be at or near the distal end 280 of the analyte sensor, at a depth of about 0.5 cm. The working portion 282 may, for example, include a coating on a conductive portion 286 (e.g., a conductive core). The working portion 282 may be configured to, for example, generate a voltage proportional to the glucose concentration (e.g., the working portion may be part of a glucose sensor, such as that available from Dexcom). In some instances, a temperature sensor 284 may be provided at or near the distal end 280 of the analyte sensor. The temperature sensor 284 may compensate for temperature variations using one or more of the various techniques described below. Additionally, empirical measurements (discussed below and in Figure 21 Figure 3 (shown in Figure 4) shows that the conductivity of an analyte sensor can be significantly temperature-dependent. In some examples, this relationship between conductivity and temperature can be used to estimate subcutaneous temperature, which can be used in temperature compensation models or other methods. In other examples, the relationship between conductivity and temperature can be applied directly (e.g., without using an estimated temperature) to compensate for temperature changes.

[0387] The analyte sensor can be coupled to a base 274, which can be coupled to the housing 266. The housing can house Figure 2A Some or all of the components shown or Figure 2B Sensor electronics 12 are shown.

[0388] In some examples, the housing may include a heat shield 272 on the top surface (and optionally additionally on one or more sides) to reflect heat from the housing, which may, for example, reduce the effects of sunlight on the sensor 10 .

[0389] In some examples, the sensor electronics 12 can include a first temperature sensor 268 near a bottom portion of the housing 266 and a second temperature sensor 270 near a top portion of the housing. Circuitry, such as the processor 251 or the processor module 214, can be configured to determine a compensated glucose concentration level based at least in part on the glucose signal, the first temperature signal, and the second temperature signal.

[0390] In some examples, the temperature gradient or heat flux can be determined (e.g., by the processor 251 or the processor module 214) from signals received from the first temperature sensor 268 and the second temperature sensor 270. For example, if the housing is exposed to sunlight, the signal from the second temperature sensor 270 may indicate a higher temperature than the signal from the first temperature sensor 268. This information can be used, for example, to estimate the temperature at the analyte sensor 10, or can be used in a temperature compensation algorithm or model. In another example, the sensor can be exposed to low temperatures, in which case the second temperature sensor 270 may display a lower temperature than the first temperature sensor. In another example, the system can be immersed in cold water, in which case the first temperature sensor 268 and the second temperature sensor may initially display a gradient, but quickly transition to approximately equal temperature values. This information can be used directly for temperature compensation based on the relationship between one or more of the temperature sensors 268, 270 and the temperature at the analyte sensor 10, or the temperature information can be used indirectly as an indication of the environment of the analyte sensor or host (e.g., immersion in hot or cold water, exposure to cold air, exposure to sunlight) from which temperature or temperature compensation information can be inferred or applied to a model.

[0391] Figure 2D is a schematic illustration of another exemplary configuration of an analyte sensor system 8 for interfacing with tissue of a host. Figure 2D In the example of FIG, temperature sensor 281 is positioned on base 274 in contact with epidermis 260 of the host's skin. For example, temperature sensor 281 can be incorporated into an adhesive pad used to secure base 274 to the host's skin.

[0392] Figure 3is a schematic diagram of an exemplary distal portion 11 of an analyte sensor 10, which may include an analyte sensor region 302 configured to generate a sensor signal indicating a glucose concentration level of a host substance (e.g., interstitial fluid). The signal may be conducted along one or more elongated members 304, 306, which may be metal wires (e.g., platinum or tantalum or an alloy thereof). The sensor signal may be transmitted to the sensor electronics for processing. The analyte sensor 10 may also include a temperature sensor 308, which may be at or near the analyte sensor region 302. In one example, the temperature sensor 308 may be, for example, a thermocouple, which may generate a voltage proportional to the temperature difference between a junction 310 of the conductors 304, 306 and a second junction (not shown), which may be at the proximal end of the conductors (e.g., outside the host body). In order to form a working thermocouple, the conductors 304, 306 may be formed of different materials. For example, one of the conductors 304, 306 may be platinum, while the other of the conductors 304, 306 may be tantalum. The signal generated by the thermocouple may be communicated to the sensor electronics for processing (ie, used to compensate the glucose sensor value for temperature).

[0393] In another example, the temperature sensor 308 can be a thermistor. The resistance value of the thermistor can be measured using the conductors 304, 306 and communicated to the sensor electronics for processing.

[0394] In one example, a sequential approach can be used, using a pair of conductors (e.g., a platinum conductor and a tantalum conductor as mentioned above, which can be Figure 3 Glucose concentration level and temperature can be measured by applying a voltage (e.g., 0.6 volts) to a conductor, for example, and then a temperature measurement can be obtained by measuring the open circuit potential across the conductor, or by applying a low voltage input across the conductor and measuring the current (e.g., determining the resistance of a thermistor, and thus the temperature parameter).

[0395] In another example, the temperature sensor can be located at the proximal end of the sensor wire, which can have high thermal conductivity so that the temperature measurement at the proximal end is close to the temperature near the analyte sensor (i.e., at the distal end). In various examples, such approximate temperature measurements can be used for temperature compensation.

[0396] Figure 4is a schematic illustration of an exemplary proximal portion 401 and an electrical contact portion 402 of an analyte sensor 10, which may be, for example, a portion of sensor electronics or a transmitter (such as a transmitter produced by Dexcom and configured to couple to a base portion comprising a subcutaneous glucose sensor). The proximal portion 402 of the analyte sensor may include a first conductor 404 and a second conductor 406, which may have a distal end (not shown) coupled to an analyte sensor (e.g., a glucose sensor). The electrical contact portion 404 may include a first contact 412 configured to contact the first conductor 404 and a second contact configured to contact the second conductor 406. The proximal portion may also include a thermistor 408 and a third conductor 411, which is coupled to the thermistor and configured to couple to a third contact 414 on the electrical contact portion. The temperature-sensitive resistance of the thermistor may be used to compensate for the effects of temperature on the glucose sensor.

[0397] Figure 5A is similar to Figure 4 The diagram of the constructed configuration, but Figure 4 The thermistor has been replaced with a temperature sensitive coating 508. Figure 5B is an enlarged view of temperature-sensitive coating 508 that may be on conductor 406. Conductive element 510 may be configured to couple with or be connected to the coating and to couple with third contact 414 so that the resistance of the coating may be measured by applying a voltage or driving a current across contacts 412, 414.

[0398] Overview of Exemplary Temperature Compensation Methods

[0399] The system can use a learned or defined relationship between an input (e.g., a temperature sensor signal or one or more other sensor signals) and an analyte level to compensate for the effects of temperature on an analyte sensor (e.g., a glucose sensor) to provide an estimate (e.g., an estimated glucose concentration value) that is less affected by temperature variations. The relationship can be defined, for example, by a theoretical model, or determined from baseline data, clinical trial data, or a combination thereof.

[0400] Various methods and models or algorithms can be applied or combined to compensate for temperature signal changes caused by temperature changes. For example, the system can compensate for long-term trends or averages, or can compensate for short-term (e.g., real-time) changes, or a combination thereof.

[0401] In some examples, a linear relationship between temperature and the glucose sensor signal can be determined and used to approximate the relationship between temperature and the glucose sensor signal and compensate for temperature effects (e.g., compensated glucose value = (sensed glucose value) x constant f(Tmeasured, Treference, sensed glucose value)).

[0402] In some examples, the sensitivity of the sensor to the analyte (glucose) concentration (Mt) can be compensated for temperature effects by determining a compensated sensitivity value (Mt,comp) based on a programmed (e.g., factory calibrated) sensitivity value (Mt,pro) and a % sensitivity change (Z) per degree Celsius. The temperature difference (ΔT) can be determined as the difference between the sensed or measured subcutaneous temperature (TSc at time t) and a reference temperature (TSc,ref), for example, (ΔT = (TSc at time t) - (TSc,ref). The reference temperature (TSc,ref) can be, for example, an average or predetermined subcutaneous temperature value. The compensated analyte sensitivity (Mt,comp) can be determined by solving the equation (Mt,comp - Mt,pro) / Mt,pro = Z * ΔT). The value of Z can be determined from a benchmark test for a specific sensor configuration. Solving the equation for Mt,comp yields the compensated analyte sensitivity, Mt,comp = Z * (ΔT) * (Mt,pro) + (Mt,pro). The compensated analyte sensitivity (Mt,comp) can be used to convert raw analyte sensor data to estimated glucose values, for example, using the following equation:

[0403] Estimated glucose value = Mt,comp * (sensor value) + offset. In some examples, offset can be determined for a specific analyte sensor design configuration, as is routinely done with existing commercial sensors. In other examples, multiple blood glucose readings (or biological samples in the case of other analytes) can be obtained (e.g., via a user interface) and used to determine the offset for a specific sensor.

[0404] In some instances, the temperature compared to the reference value is a long-term average temperature. In some instances, this is to account for differences in body temperature between hosts (patients). In other instances, real-time compensation of temperature can correct for temperature-based sensor changes that may be due to, for example, exposure to hot water (e.g., showering), cold water (e.g., swimming), air conditioning, sunlight, thermal changes during sleep (e.g., due to heat contained in a warm blanket), or other hot or cold environments. Some instances can combine long-term and real-time compensation methods.

[0405] In some instances, where the temperature sensor is not subcutaneous, a delay parameter may also be used (in combination with a linear model, or a more complex model as described below) to compensate for the delay between detecting a temperature change at the temperature sensor and the actual temperature change at the analyte sensor. Various exemplary methods for determining subcutaneous temperature based on a signal from a non-subcutaneous sensor are provided below.

[0406] Measure subcutaneous temperature from a non-subcutaneous temperature sensor.

[0407] In some systems, devices or methods, the subcutaneous temperature can be determined using a temperature signal from a non-subcutaneous temperature sensor, such as a temperature sensor in a sensor electronics of an external device (e.g., a transmitter), which can be coupled to a subcutaneous analyte (e.g., glucose) sensor. One or more of a variety of methods can be used to determine the subcutaneous temperature based on the temperature signal received from the non-subcutaneous temperature sensor. In some instances, a linear relationship between the non-subcutaneous temperature value and the subcutaneous temperature value can be used to approximate the subcutaneous temperature. In some instances, a delay parameter (combined with a linear model, or a more complex model as described below) can also be used to compensate for the delay between the temperature change detected at the temperature sensor and the actual temperature change at the analyte sensor. In some instances, a nonlinear relationship (e.g., a quadratic equation or a higher order polynomial or other relationship) can be determined and used to compensate for temperature, and a delay parameter can be optionally included. In some instances, a relationship (e.g., a heat transfer relationship) can be determined by solving a differential equation to determine temperature compensation. For example, the sensor system may solve the differential equation each time an analyte value is needed (eg, every 5 minutes or every 15 minutes) to provide a temperature-compensated analyte value. In another example, a filter or predetermined relationship based on the differential equation may be applied to compensate for temperature.

[0408] Linear Model Example

[0409] In some examples, a linear model can be used to determine the subcutaneous temperature from the non-subcutaneous temperature. The linear model can be developed, for example, based on a biothermal model (e.g., the Pennes biothermal equation), known host tissue parameters (e.g., typical heat transfer parameters of human skin and subcutaneous tissue), and sensor electronics (e.g., transmitter) parameters determined, for example, through benchmark testing. Tissue parameters can include, for example, thermal conductivity or heat flux across the tissue.

[0410] The subcutaneous temperature (Tsubcutaneous) can be determined from the measured non-subcutaneous temperature (Texternal) using a linear equation (e.g., Tsubcutaneous = a*Texternal + b), where the gain / slope (a) and offset (b) can be determined, for example, using empirical data, theoretical or model data, or a combination thereof.

[0411] In some instances, when the analyte temperature sensitivity is known, the gain (a) and offset (b) of the above equation can be determined or updated based on an analyte calibration value (e.g., a blood glucose value): In other words, if confidence in the glucose sensitivity is high, the temperature can be estimated based on the blood glucose value from the fingertip and the signal received from the glucose sensor. The system can calculate the true analyte sensitivity using the input glucose value, then determine the subcutaneous temperature based on the true analyte sensitivity, and then determine the relationship between the subcutaneous temperature and the signal from the non-subcutaneous temperature sensor (e.g., the gain and offset values). The system can determine or receive the temperature sensor value at calibration (e.g., from a temperature sensor in an external sensor electronics device) to ensure that an updated temperature sensor signal is used when determining the sensitivity, gain, and offset. In some instances, a weighted average or probabilistic model can be used rather than using new gains and offsets so that the gains and offsets are not overly affected by independent factors that may change sensitivity, such as inaccuracies in the time after initial placement of the sensor (e.g., the “dip and recovery” phenomenon where the sensor signal produces a low sensor signal (dip) during an initial “warm-up” period, followed by a more accurate (recovery) reading after warm-up).

[0412] Delay between non-subcutaneous sensor detection and subcutaneous temperature change

[0413] In some instances, the system may take into account the delay between the time a temperature change is recorded at a non-subcutaneous temperature sensor and the time the temperature change actually occurs at the subcutaneous analyte (glucose) sensor: If the analyte sensing system includes a subcutaneous temperature sensor, direct subcutaneous temperature measurement can be used for temperature compensation, but if the system relies on a non-subcutaneous (e.g., external) temperature sensor, the accuracy of the temperature compensation method can be improved by taking into account the delayed temperature change at the subcutaneous glucose sensor.

[0414] For example, the linear model described above assumes that the subcutaneous temperature matches the external temperature (e.g., the sensor electronics or transmitter temperature), but skin tissue heats and cools much more slowly than the transmitter, so there is a delay between the time the external sensor registers a temperature change and the time the change occurs at the subcutaneous location. For example, if a person walks from a cold, air-conditioned room to a warmer location, the external sensor will quickly register the temperature change, but the subcutaneous temperature will take longer to warm up. In another example, when the host and sensor are immersed in cold water (e.g., a pool, ocean, or lake that is cooler than ambient temperature), the temperature drop will first be detected in the sensor in the external sensor electronics (e.g., in the CGM transmitter), and some time later, the temperature of the subcutaneous sensor will drop due to heat loss through the sensor or through the host tissue. The accuracy of the subcutaneous temperature estimate can be improved by building in a delay to reflect this reality.

[0415] In some instances, the delay can account for a delay in recording a temperature change in a non-subcutaneous temperature sensor based on other temperature information or a model or estimate of the time delay for the non-subcutaneous sensor to record a temperature change. For example, when the ambient temperature changes, a non-subcutaneous temperature sensor may take a relatively short time (e.g., 1 minute) to record the change, especially if the sensor is embedded in a sensor electronics housing through which heat must be conducted to record the temperature change. If a subcutaneous temperature change is observed after a certain time (e.g., 6 minutes), the net delay is the difference between the two readings (e.g., 5 minutes).

[0416] In some instances, a constant delay can be used. For example, the temperature compensation method can assume a delay period (d) and use the temperature of the previous period (e.g., using the temperature at time td) to compensate for the temperature effect based on the assumed delay. In other instances, compensation can be performed using the temperature at the current time (t) and the temperature of the previous period (e.g., using the temperature at time t–d) based on the assumed delay. In still other instances, compensation can be performed using multiple temperature measurements from different time periods associated with the assumed delay (e.g., using the temperature of t–d1 and the temperature of t-d2). In some instances, the delay can be, for example, 30 seconds to 4 minutes (e.g., 1 minute), 1 minute to 10 minutes (e.g., 5 minutes), 5 minutes to 15 minutes (e.g., 10 minutes), or 20 minutes to one hour (e.g., 30 minutes). In some instances, the delay can be determined based on known information about the host, such as average body temperature or body mass index.

[0417] In some instances, a variable delay period can be used. In some instances, the variable delay period can be, for example, based at least in part on the change between the detected temperature and a baseline. In another instance, the delay can be based at least in part on the difference or rate of change of the detected temperature from the previously detected temperature (e.g., a longer delay can be used when a larger temperature change is observed because the heat transfer process will take longer to complete in order for the subcutaneous temperature to reach a steady state). In some instances, a delay can be implemented only when the temperature change satisfies a condition, for example, when a sudden temperature change exceeding a threshold occurs (e.g., a change greater than 5°C or 10°C).

[0418] In some examples, the variable delay can be based on a temperature gradient, e.g., the difference between the sensed temperature and the measured subcutaneous temperature. In some examples, the variable delay can be based on a heat transfer equation or model that can take into account, for example, a temperature gradient (e.g., between ambient temperature and subcutaneous temperature) and one or more heat transfer rates, and optionally can also take into account biological processes (e.g., heat transfer via blood flow).

[0419] The delays may be calculated or used in various other exemplary methods (eg, partial differential equation models, polynomial models, state models, time series models, models with subgroups or conditions).

[0420] Figure 6 6 is a flow chart illustration of an exemplary method 600 for determining a temperature-compensated glucose concentration level using a delay parameter. The method 600 may include, at 602, receiving a temperature signal indicative of a temperature parameter of an external component. The temperature parameter may be, for example, a temperature, a change in temperature, or a temperature offset. Detecting the temperature signal may include, for example, measuring a temperature parameter of a component of a wearable glucose sensor. The method 600 may include, at 604, receiving a glucose signal indicative of an in vivo glucose concentration level. Receiving the glucose signal may include, for example, receiving the glucose signal from a wearable glucose sensor.

[0421] Method 600 may include determining a compensated glucose concentration level based on the glucose signal, the temperature signal, and the delay parameter at 606. In some examples, a temperature-compensated sensor sensitivity value may be determined based on the temperature signal and the delay parameter, and an estimated glucose concentration value may be determined using the sensor sensitivity value and the glucose signal. In some examples, a model or neural network may be used (at least in part) to determine the estimated glucose concentration level based on the glucose signal, the temperature, and the delay parameter.

[0422] In various examples, as described above, the delay parameter can be constant or variable based on temperature or information about the host or other factors. In some examples, the temperature parameter can be detected at a first time, and the glucose concentration level can be detected at a second time after the first time. The delay parameter can include a delay period between the first time and the second time, the delay period being the cause of the delay between a first temperature change of the external component and a second temperature change of the adjacent glucose sensor. In some examples, determining the compensated glucose concentration level can include executing instructions on a processor to receive the glucose signal and the temperature signal, and using the glucose signal, the temperature signal, and the delay parameter to determine the compensated glucose concentration level. The method can also include storing a value corresponding to the temperature parameter in a storage circuit, and retrieving the stored value from the storage circuit for determining the compensated glucose concentration level. In some examples, the temperature-compensated glucose concentration level, the estimated subcutaneous temperature, or the delay parameter (or any combination thereof) can be determined using a linear model (e.g., a linear equation), a nonlinear model, a partial differential equation model, a time series model, a linear or nonlinear model with subgroups, or any other technique described herein.

[0423] The method may also include adjusting the delay period based on the temperature change rate or temperature gradient (or other factors or techniques as described above) or based on detected conditions at 608. In some examples, the detected conditions may include sudden changes in temperature, location, or exercise state or period (e.g., using an accelerometer).

[0424] Optionally, the method may further include, at 610 , delivering therapy based at least in part on the compensation glucose concentration level.

[0425] Partial Differential Equation (PDE) Model Examples

[0426] In some instances, a partial differential equation (PDE) model can be used to determine subcutaneous temperature from non-subcutaneous temperature sensor signals. A PDE temperature compensation approach can make the system more accurate, for example, by accounting for the fact that the rate of temperature change in external electronics (e.g., a CGM transmitter) is higher than the rate of temperature change in subcutaneous tissue or fluid. Subcutaneous tissue and fluid temperatures can change more slowly, in part because the body acts as a heat sink. Using a PDE model can be particularly advantageous in situations where temperature changes rapidly.

[0427] In one example, the sensor electronics, subcutaneous sensors, and skin layers can be treated as a multi-layer model. Figure 2CThe sensors and skin layers (epidermis 260, dermis 262, and subcutaneous tissue 264) are shown in . In one example, the multi-layer structure can be considered a one-dimensional (1D) system, where the 1D space is the depth relative to the skin surface.

[0428] The temperature distribution in space and time can be described by the heat equation:

[0429]

[0430] The definitions of variables and parameters in equation (1) are shown in Table 1 below.

[0431]

[0432] Table 1

[0433] The thermal conductivity of each layer in the one-dimensional model can be determined or estimated. For example, the thermal conductivity of each skin layer can be determined empirically or theoretically. The thermal conductivity of the sensor electronics (including the battery and epoxy glue) can also be determined. Example values ​​are provided in Table 2:

[0434]

[0435] Table 2

[0436] The external boundary condition (BC) of this PDE is set to the time-varying temperature measured by a non-subcutaneous temperature sensor, and the internal BC is set to the constant core body temperature.

[0437] Based on these assumptions, Equation 1 can be solved so that the temperature at the sensor (e.g., the working electrode) can be estimated. In some instances, the equation can be solved each time a temperature value is needed. In some values, a lookup table can be developed by solving the PDE within a range of plausible values, and the lookup table can be queried to determine the approximate subcutaneous temperature. In some instances, a linear correlation between the temperature at the subcutaneous sensor and the temperature of the external sensor can be determined based on the PDE model. In some instances, the PDE model can be used to perform spatiotemporal filtering to capture transient processes of temperature changes and time lags.

[0438] The estimated temperature of the subcutaneous sensor can be used to correct for sensitivity variations of the subcutaneous sensor. In one example, the temperature at the electrochemical reaction surface of an analyte sensor (e.g., a glucose sensor) can be estimated and used to determine the estimated sensitivity of the electrochemical sensor at the estimated temperature.

[0439] Time Series Model Examples

[0440] In some instances, a time series model can be used to estimate subcutaneous temperature using signals from non-subcutaneous temperature sensors or to compensate for the effect of temperature on analyte sensor sensitivity. In some instances, the temperature-compensated sensitivity can be determined directly, i.e., without estimating subcutaneous temperature.

[0441] In one example, a 4th order polynomial can be used as the model. For example, the following model can be used:

[0442]

[0443] in,

[0444] p i (i=1,…,5): model parameters

[0445] y: Sensitivity error

[0446] Point-by-point sensitivity at time t calculated from glucose meter data (e.g., finger stick)

[0447] Point-by-point sensitivity at time t calculated according to the factory calibration algorithm

[0448] x: measured temperature

[0449] For example, curve fitting or optimization techniques can be used to determine model parameters based on an empirical data set.

[0450] After the model parameters are determined, the model can be used to compensate for temperature changes. For example, the compensation sensitivity can be determined using the following equation:

[0451]

[0452] In some instances, model parameters can be updated as calibration entries become available (e.g., based on blood glucose meter data). For example, a time series model can be converted to a recursive form of the model so that the model can be updated in real time as fingerstick measurements become available. The values ​​of the constants can be determined based on population data, patient-specific data, etc. The values ​​can be, for example, as follows: p1: -0.0004334, p2: 0.04955, p3: -2.035, p4: 36.7, p5: -259.7

[0453] Although a 4th order polynomial has been provided as an example, a 3rd order, 5th order, or higher order polynomial may also be used. Higher order polynomials may provide higher compensation accuracy but may require more time, input data, or processing power to determine and update model parameters.

[0454] Temperature compensation example

[0455] A kind of algorithm or model can be used to determine the analyte sensor value (for example, glucose concentration level) of temperature compensation.In some instances, a neural network, a state model (for example, a hidden Markov model), a probability model or other models can be used to develop a temperature compensation model.For example, a model of a specific subject (for example, a patient) can be learned based on the data from the subject, and the model can be used to determine the estimated glucose concentration level of compensation.In some instances, a model can be learned from the data (for example, clinical trial data) from a patient population, and the model can be used for a patient population.In some instances, the same model (meeting exclusion criteria) can be used for most or all patients.In some instances, the patient can be matched with a model developed from a similar patient population (for example, based on average temperature, age, sex, BMI or other factors).The input of the model can include temperature measurement value, time, sensor sensitivity, estimated glucose value, insulin sensitivity, accelerometer data (for example, for detecting activity or posture), heart rate, respiratory rate, meal state, size or type, on-board insulin or insulin delivery amount or pattern, body mass index (BMI) or other factors. Outputs from the model can include sensor sensitivity, local glucose levels, compartment bias values, non-enzymatic bias levels (any of which can be combined to determine a glucose concentration level), or the model can output a compensated glucose / analyte concentration level. Model-based approaches can be particularly effective because various temperature effects (e.g., sensor sensitivity, local glucose levels, compartment bias values, non-enzymatic bias levels) can be linear, nonlinear, or dynamic (e.g., dependent on a combination of both time and temperature).

[0456] Long-term averaging method

[0457] The temperature compensation system can account for long-term average temperature values ​​or trends. For example, the long-term average can be used to compensate for temperature variations. The long-term average can, for example, account for variations in body or skin temperature between the host and a reference value. In some examples, the long-term average method can be combined with one or more short-term (e.g., real-time) temperature compensation methods described below.

[0458] The average subcutaneous temperature of an individual can be determined and updated in a variety of different ways. For example, the subcutaneous temperature can be determined as an average value (e.g., mean or median) over the entire sensor period, or as an average value over a rolling window (e.g., the past 12 hours or 24 hours). In some instances, the subcutaneous temperature can be updated at intervals, such as being remeasured or updated every 6, 9, 12, 18, or 24 hours. In some instances, the subcutaneous temperature can be determined as a weighted average, where newer values ​​(e.g., the past 6 hours, 12 hours, or 24 hours) are weighted more heavily, while past time intervals are weighted less heavily.

[0459] In one instance, the temperature sensor can initially be calibrated for an initial reference value (e.g., 35°C), which can represent the average temperature of the population. During the learning period, the temperature sensor can determine the actual temperature of the host. The learning period can be selected to be long enough (e.g., 6-12 hours) to filter out temperature fluctuations (e.g., so that the average cannot be determined during hot / cold events such as showers). The learned average can be used to compensate for host temperatures that are different from the population average. For example, if the operating temperature of the population is assumed to be 35.0°C, but the temperatures detected from a particular host are shown to be 35.5°C on average, a half-degree change can be used to compensate for the analyte value. In some instances, an initial average can be determined (e.g., on the first day) and the operating average can be updated by subsequent temperature measurements (e.g., using the average temperature on the second day or two days). As described above, a time window can also be used.

[0460] If the system has a subcutaneous temperature sensor, a series of temperature measurements can be obtained from the subcutaneous temperature sensor and used to determine a long-term average. In other examples, the subcutaneous temperature can be determined based on the sensed non-subcutaneous temperature (e.g., based on a linear or higher-level relationship) using one of the various methods described below. After establishing the individual's subcutaneous temperature (Tsub,ind), the temperature-corrected analyte sensitivity can be determined based on the deviation from the reference temperature (Tsub,ref), for example, using the equation provided above.

[0461] Glucose change rate

[0462] In some instances, the rate of change of the estimated glucose value or the rate of change of the signal from the glucose sensor can be used as an input for determining temperature compensation. For example, when the rate of change meets a condition (e.g., exceeds a specified value), temperature compensation can be suspended or switched to a different model. For some subcutaneous glucose sensors, the glucose concentration level measured by the subcutaneous sensor reflects a time lag relative to the blood glucose level due to a physiological delay in changes in interstitial fluid glucose levels compared to changes in blood (e.g., it may take several minutes for changes in interstitial fluid glucose levels as measured by a subcutaneous glucose sensor to be reflected). The periodicity of the sensor readings may also introduce delays (e.g., if a sensor reading is taken every 5 minutes, the estimated glucose level may be 4 minutes older at some points in the cycle). If time lag errors are present in the system during periods of rapid glucose changes, temperature compensation may be performed on inaccurate (outdated) glucose estimates: in some cases, temperature compensation for outdated glucose levels may worsen the estimate, so suspending or changing temperature compensation during periods of rapid change may be useful. For example, when the glucose concentration level drops rapidly (e.g., due to strenuous exercise), the estimate from the subcutaneous temperature sensor may be "lower" than the blood glucose concentration level (e.g., as measured by a blood glucose meter), and thus the subcutaneous sensor will show a higher estimated glucose concentration level than the blood glucose level. This discrepancy may be exacerbated if temperature compensation increases the estimated blood glucose concentration level of the subcutaneous sensor. This situation may be avoided by suspending temperature compensation or switching to another model. In some instances, when the high-speed change condition is met, temperature compensation may be applied only when temperature compensation increases the rate of change (e.g., to avoid exacerbating discrepancies due to physiological delays).

[0463] In some instances, the output of the analyte sensor can be used to assess the signal from the temperature sensor relative to the deviation of the output of the temperature sensor. These correlations or deviations can be used to establish the confidence of temperature signal, analyte sensor signal or both. During the time when glucose level meets the stability condition, it is expected that temperature and glucose concentration level will show correlation. Stability condition can, for example, be determined based on the rate of change of glucose concentration level. In some instances, stability condition can include multiple sub-conditions, such as short-term condition and long-term condition. For example, when the average rate of change in the rate of change and / or specified time period meets the condition (for example, increase or decrease by no more than 1mg / dL per minute, and / or increase or decrease by no more than 15mg / dL in 15 minutes), it can be considered as glucose level stability. When the rate of change and / or average rate of change or specific time period meet the condition (for example, glucose level rises (or decreases) 1-2mg / dL per minute and / or rises (or decreases) 15-30mg / dL in 15 minutes), it can be considered as glucose level moderate stability.

[0464] like Figures 15A-15C As shown, when the glucose level is stable (or in some instances, moderately stable), the slopes of the temperature curve and the glucose curve should be correlated because changes in the glucose curve reflect changes in the output of the analyte sensor caused by temperature. Figure 15A The output of the glucose sensor is shown plotted against time. The gain (mg / dL) is relatively high, showing a change in slope over time relative to glucose stability. Figure 15B The output of the temperature sensor is shown plotted against time. Figure 15C shows the temperature superimposed on the glucose sensor output (ie, Figure 15B and Figure 15A The analyte sensor output is correlated with the temperature sensor output: when the temperature sensor output rises, the analyte sensor value rises (positive slope), when the temperature sensor output falls, the analyte sensor value falls (negative slope), and when the temperature sensor output is flat, the analyte sensor value is flat. From this correlation, one can infer the confidence level of the temperature signal. Conversely, Figure 15D An example is shown where the temperature sensor output (dashed line) does not correlate well with the glucose sensor output during a time period with relatively stable glucose values, indicating that the temperature sensor output may be unreliable.

[0465] In various examples, when confidence in the temperature sensor output is low, temperature compensation may be suspended, reduced, or changed, or other information (eg, motion detection as described below) may be used or requested to increase the accuracy of temperature compensation.

[0466] Figure 7 7 is a flow chart illustration of an exemplary method 700 for determining a temperature-compensated glucose concentration level based on an estimated (e.g., verified) temperature value. The method 700 may include receiving a glucose sensor signal at 702. For example, the glucose sensor signal may be received from a continuous glucose monitor (CGM).

[0467] Method 700 may include receiving a temperature parameter signal at 704. Receiving a temperature parameter signal may, for example, include receiving a signal indicative of a temperature, a temperature change, or a temperature excursion.

[0468] Method 700 may include receiving a third sensor signal at 706. Receiving the third sensor signal may include, for example, receiving a heart rate signal, receiving a pressure signal, receiving an activity signal or an accelerometer signal (e.g., to detect motion), or receiving a location signal (e.g., to infer proximity to a hot or cold environment such as a pool, a beach, or an air-conditioned facility). In some instances, receiving the third sensor signal may include receiving temperature information from an ambient temperature sensor. In some instances, receiving the third sensor signal may include receiving information from a wearable device such as a watch. In some instances, receiving the third sensor signal may include receiving temperature information from a physiological temperature sensor, which may, for example, be integrated into a watch or other wearable device. In some instances, the third signal may include a heart rate signal, a breathing signal, a pressure signal, or an activity signal, and the motion state may be detected by a rise in the heart rate signal, the breathing signal, the pressure signal, or the activity signal.

[0469] Method 700 may include, at 708, evaluating the temperature parameter signal using a third sensor signal to generate an evaluated temperature parameter signal. In some instances, evaluating the temperature parameter signal may include determining presence at a location having known temperature characteristics. For example, a low or high temperature signal may be confirmed by a location signal indicating presence at a location having known ambient temperature characteristics (e.g., a hot or cold environment), such as a swimming pool, a beach, an air-conditioned facility, or an area having known weather characteristics, such as may be determined by reference to a network resource (e.g., a website) or a stored lookup table. In some instances, the method may include determining presence at a location having an immersive water environment (e.g., a swimming pool or a beach). In some instances, evaluating the temperature parameter signal may include determining that a change in the temperature parameter signal is consistent with a period of exercise. For example, evaluating the temperature parameter signal may include determining that the temperature parameter signal is consistent with the occurrence of an increase in body temperature due to exercise.

[0470] Method 700 may include determining a temperature-compensated glucose concentration level based on the evaluated temperature parameter signal and the glucose sensor signal at 710. In some instances, determining the temperature-compensated glucose concentration level may include applying the temperature parameter signal to a motion model. In some instances, the method may include using a motion model (e.g., an outdoor or convection-cooled motion model) when motion is detected and a change in the temperature parameter signal indicates a decrease in temperature. For example, temperature compensation based on a non-subcutaneous temperature sensor may be discontinued (e.g., in the sensor electronics) when the detected temperature decreases, but when motion is detected (e.g., an increase in HR or activity) because subcutaneous temperature may stabilize or even increase during exercise when the patient is exercising vigorously (e.g., running) outdoors in a cool environment (e.g., when convection-cooled by a fan, or when exercising outdoors in a cold weather environment).

[0471] Figure 8 8 is a schematic illustration of an exemplary method 800 for temperature compensating a continuous glucose sensor, the method including determining a pattern from temperature information. The method 800 may include determining a pattern based on the temperature data at 802. In some examples, determining the pattern may include determining a pattern of temperature changes, and the method may include compensating the glucose concentration level based on the pattern.

[0472] Method 800 may include, at 804 , receiving a glucose signal from a continuous glucose sensor, the glucose signal indicating a glucose concentration level.

[0473] Method 800 may include determining a temperature-compensated glucose concentration level based at least in part on the glucose signal and the pattern at 806. For example, the method may include receiving a temperature parameter, comparing the temperature parameter to the pattern, and determining the temperature-compensated glucose concentration level based at least in part on the comparison. In some instances, the pattern may include a temperature pattern associated with a physiological cycle (e.g., a circadian rhythm). In some instances, method 800 may include determining whether the temperature parameter is reliable based on the comparison with the pattern, and when the temperature parameter is determined to be reliable, using the temperature parameter to temperature compensate the glucose concentration level.

[0474] In some examples, the degree of compensation can be determined based at least in part on a comparison of the temperature parameter to the pattern.For example, the degree of compensation can be based on a defined range or confidence interval.

[0475] In some examples, the mode can be determined by determining a state, and the temperature-compensated glucose concentration level can be determined based at least in part on the determined state. For example, method 800 may further include receiving a temperature parameter, and determining the state may include applying the temperature parameter to a state model. In some examples, determining the state may include applying one or more of glucose concentration level, carbohydrate sensitivity, time, activity, heart rate, respiratory rate, posture, insulin delivery, meal time, or meal size to the state model. In some examples, determining the state may include determining a motion state, and the method may include adjusting temperature compensation based on the motion state model.

[0476] Conditional temperature compensation

[0477] In some instances, a model can be selected or changed based on the detected condition. For example, a set of different linear models can be developed, and a model can be selected from the set based on the detected condition. In some instances, a state model can be used to determine the condition.

[0478] In some instances, the condition may include a location or geographic feature. The location may, for example, include a geographic location parameter (e.g., longitude, latitude, or altitude), or a city or place or destination point (e.g., a beach or a mountain range). In various instances, location information, geographic information, or physiological sensor information (e.g., activity or heart rate as described below) may be collected from the patient's smart device, such as a cell phone, watch, or other wearable sensor.

[0479] In some examples, the condition can include the deviation of the temperature reading from the mean. For example, a rolling mean temperature value and a rolling standard deviation can be determined based on a series of temperature values, and a model (e.g., a linear model) can be used based on whether the temperature differs from the mean by +1σ, -1σ, +2σ, -2σ, +3σ, or -3σ. In some examples, the rolling mean can be determined from a predetermined number of previous temperature values. In various examples, the current reading can be included in or excluded from the rolling mean. In some examples, the rolling mean can be exponentially weighted.

[0480] In some examples, the conditions can include patient demographics. For example, demographics can include gender (e.g., different models are used for male and female hosts / patients), diagnosis (e.g., type 1 diabetes or type 2 diabetes or non-diabetic), age (e.g., age, or youth, teenager, adult, elderly), biological cycle (e.g., circadian rhythm or menstrual cycle), medical condition (e.g., pregnancy or health / disease or chronic disease).

[0481] In some instances, a condition can be determined from a wearable sensor or physiological sensor (such as a heart rate sensor, accelerometer, pressure gauge, or temperature sensor). The condition can include a state determined based on one or more sensor inputs. In some instances, the condition can include an activity state, for example, an activity state can be determined from a heart rate or accelerometer. In some instances, the condition can include a wake-sleep state, which can be determined from one or more physiological sensors (e.g., based on a biorhythm) or from a posture sensor (e.g., a three-axis accelerometer). In some instances, the condition can include a compression state, which can be determined, for example, based on a pressure sensor or a temperature sensor or a combination thereof. For example, when a patient lies on a wearable glucose sensor (e.g., as may occur during sleep), the sensor can generate an inaccurate glucose sensor reading (e.g., "low pressure" means a lower glucose value). In some instances, each of these inputs or conditions can trigger a different temperature relationship (e.g., applying a specific temperature compensation model).

[0482] In some examples, temperature compensation or its application (or suspension) may be based at least in part on the rate of temperature change (e.g., the condition may be the rate of temperature change). Because the temperature detected externally (e.g., by the sensor electronics) may change much faster than the subcutaneous temperature, it may be difficult to correctly predict the subcutaneous temperature in a rapidly changing temperature environment. In some examples, temperature compensation may be suspended or reduced when the detected rate of temperature change meets a condition (e.g., the rate of change exceeds a specified value). In another example, when the first condition is met (e.g., the rate of temperature change is below a specified value), a first model (e.g., a linear model) may be used, and when the second condition is met (e.g., the rate of change is above a specified value), a second model (e.g., a linear delay model) may be used.

[0483] In some examples, temperature compensation or its application (or suspension) can be based on the magnitude of a heat flux or temperature gradient (e.g., a condition can be based on a heat flux or temperature gradient). A temperature can be determined (e.g., approximated) based on a measured subcutaneous temperature (e.g., a previous temperature measurement) and a detected temperature outside the subcutaneous region (e.g., outside the sensor electronics). In one example, when a temperature gradient or heat flux condition is met (e.g., the temperature gradient or heat flux exceeds a threshold), the model can be adjusted to, for example, reflect the fact that the external temperature changes faster than the subcutaneous temperature. In one example, the "gain" in the linear model (described above) can be reduced, which can have the effect of reducing the rate of change of the measured subcutaneous temperature to more accurately track the actual rate of temperature change. In another example, temperature compensation can be suspended when a temperature gradient or heat flux condition is met. In some examples, temperature compensation or its application can be based on the direction of the temperature gradient; for example, temperature compensation when the external temperature is higher than the subcutaneous temperature can be different than temperature compensation when the external temperature is lower than the measured subcutaneous temperature.

[0484] In some instances, the condition may be exercise. For example, one or more wearable sensors (e.g., accelerometers, heart rate sensors, respiratory sensors) can be used to determine whether the subject is performing some type of aerobic exercise (e.g., running, cycling, or metabolic conditioning). In one example, it can be assumed that the subject's core body temperature (and subcutaneous temperature) increases, for example, from 37°C to 38°C. During mobile exercise such as running or cycling, it can also be assumed that the convection coefficient increases (e.g., ×10) due to the subject's movement. An appropriate motion model that takes into account these parameter changes can be applied. For example, the "gain" (slope) of the linear model can be increased, and the offset (constant) can be changed to reflect the effects of exercise (e.g., the basic linear model: Tsubcutaneous=0.395*Texternal+22.346 can be transformed into an aerobic exercise linear model, such as: Tsubcutaneous=0.416*Texternal+22.178).

[0485] In some instances, the amount of temperature compensation applied may be limited when cool temperatures and motion are detected. For example, during exercise, the transmitter temperature may be lower than when the subject is at rest, for example, because the subject is outdoors and the sensor electronics are exposed to increased convection due to motion, or because the subject's skin is cooler due to sweating. However, subcutaneous temperature may increase due to increased heat production, so a standard temperature compensation model (which does not account for the combination of motion / low temperature) may result in inaccuracies. Such "low temperature motion" conditions can be detected, for example, by combining temperature sensor input with accelerometer, heart rate, respiratory rate, or position input. When the subject is detected to be exercising, temperature compensation can be modified at low temperatures, for example, temperature compensation can be suspended, limited, or gradually reduced, or an alternative compensation model can be applied. In one example, during exercise, any temperature below a threshold can be used for temperature compensation because the threshold (for example, a sensor temperature <29°C is replaced by 29°C for temperature compensation purposes) or temperature compensation can be limited by an algorithm. In another example, a gradual reduction in compensation can be achieved by reducing the temperature sensitivity factor (Z) so that for a given detected sensor temperature, a smaller change in subcutaneous temperature is made (e.g., Mt,comp = Mt,pro*(Z)*(TSc – Tsc,ref); or Mt,comp = Mt,pro*(Z)*(TSc – Tsc,ref)+Mt,pro). For example, if a typical temperature sensitivity factor is 3.3%, then in a moving device, the temperature sensitivity factor can be changed to 1.5% based on the detected conditions.

[0486] The compensation model can be selected or determined based at least in part on the individual's average subcutaneous temperature. In one example, the average subcutaneous temperature can be determined for the first few hours of a session (e.g., during or after a warm-up period) or on the first day of a session. The long-term averaging method described above can be used to determine the compensation. In some examples, the average subcutaneous temperature can be updated periodically or cyclically, for example, every 6, 12, or 24 hours.

[0487] In some instances, it can be determined (e.g., using an algorithm, model, or lookup table) whether temperature compensation is likely to increase the accuracy of the estimated glucose concentration level. For some patients or in some situations, temperature compensation may actually decrease accuracy: identifying these patients or identifying factors and discontinuing or maintaining temperature compensation may improve sensor performance or reduce MARD. Identifying factors may, for example, include the host's surface or body temperature, BMI, gender, age, or any combination of the other conditions identified above.

[0488] Figure 9 is a flow chart illustration of an exemplary method 900 for temperature compensating a continuous glucose monitoring system based at least in part on a detected condition. The method 900 may include, at 902, receiving a glucose signal indicative of a glucose concentration level.

[0489] Method 900 may include, at 904 , receiving a temperature signal indicative of a temperature parameter.

[0490] Method 900 may include detecting a condition at 906. In some instances, the condition may include a high rate of change of the glucose signal, wherein temperature compensation may be reduced or suspended during a time period when the glucose signal experiences a high rate of change. In some instances, the condition may include a body mass index (BMI) value. For example, it may be assumed that a host with a high BMI naturally warms up or changes temperature more slowly than a host with a low BMI. In some instances, the condition may include detecting a fever, and in response to detecting a fever, reducing, suspending, limiting, or tapering off temperature compensation. In some instances, the detected condition may include the presence of radiant heat on a continuous glucose monitoring system. In some instances, the condition may include detecting motion. The method may, for example, include reducing, tapering off, limiting, or suspending temperature compensation when a condition (e.g., motion) is detected.

[0491] In some instances, the glucose signal can be received from a continuous glucose sensor, and the condition can include stress on the continuous glucose sensor. For example, stress on the sensor can be detected based, at least in part, on a rapid drop in the glucose signal. In some instances, the condition can include sleep. In some instances, the condition can include stress during sleep. For example, sleep can be detected using one or more of temperature, posture, activity, and heart rate, and the method can include applying a specified glucose alarm trigger based on the detected sleep.

[0492] Method 900 may include determining a temperature-compensated glucose concentration level based at least in part on the glucose signal, the temperature signal, and the detected condition at 908 .

[0493] In some instances, the condition may include a sudden change in the temperature signal. For example, temperature compensation may be reduced or suspended in response to detecting a sudden change in temperature. Sudden temperature changes may not occur at the analyte sensor site in a subcutaneous location, where temperature changes tend to occur more gradually as heat is conducted to or from the sensor site through the skin. Therefore, when a sudden temperature change occurs at an external sensor, it may be appropriate to suspend temperature compensation for a period of time or to "phase in" temperature compensation over a period of time to reflect the gradual temperature change at the sensor site. After detecting a sudden change in temperature or other rapid change or signal discontinuity, a temperature-compensated glucose level may be determined using one or more of a variety of techniques. In some instances, the temperature-compensated glucose concentration level may be determined using a previous temperature signal value instead of the temperature signal value associated with the sudden change in temperature. In some instances, the temperature-compensated glucose concentration level may be determined by inferring a temperature signal value based on a previous temperature signal value and using the inferred temperature signal value instead of the temperature signal value associated with the sudden change in temperature. In some instances, a delay model may be invoked in response to detecting a sudden change in temperature. For example, the delay model may specify a delay period for determining the temperature-compensated glucose level.

[0494] One or more of a variety of techniques can be used to measure the temperature-compensated glucose concentration level based on the glucose signal, the temperature signal, and the detected conditions. For example, a linear model can be used to measure the temperature-compensated glucose concentration level. In another example, a time series model can be used to measure the temperature-compensated glucose concentration level. In some examples, a partial differential equation can be used to measure the temperature-compensated glucose concentration level. In some examples, a probability model can be used to measure the temperature-compensated glucose concentration level. For example, a state model can be used to measure the temperature-compensated glucose concentration level.

[0495] In some examples, the method can include determining a long-term average using the temperature signal, and can determine a temperature-compensated glucose concentration level using the long-term average.

[0496] In some examples, the method may further include receiving a blood glucose calibration value, and updating the temperature compensation gain and offset upon receiving the blood glucose calibration value.

[0497] The method may also include delivering insulin therapy.The insulin therapy may be determined (eg, via a pump or smart pen) based at least in part on the temperature-compensated glucose level.

[0498] Other factors to consider in temperature compensation

[0499] In some instances, temperature compensation may be based at least in part on body mass index (BMI). In one example, height and weight may be received from a subject, for example, via an interface of a smartphone application. Temperature compensation parameters may be determined or adjusted based at least in part on BMI. In some instances, temperature compensation may be based on a preloaded model, which may be associated with a specified BMI window. For example, a standard temperature compensation model may assume a certain distance from the subcutaneous layer (where the working electrode is designed to reside during use) and tissue at core body temperature. In individuals with a high BMI, a thicker layer of adipose tissue (body fat) may increase the distance from the subcutaneous layer to tissue at core body temperature, which may result in a decrease in subcutaneous or skin surface temperature. In some instances, a set of models and a single model (e.g., a PDE model in which the distance from core body temperature is varied) may be used, and a model from the set may be selected based at least in part on the individual's BMI. In some instances, because BMI cannot perfectly predict adipose tissue thickness, particularly when not located at the location of an analyte sensor (e.g., a CGM), information other than BMI may be used to select a model.

[0500] In some examples, the temperature compensation model can be based at least in part on the subject's core body temperature. For example, body temperature tends to correlate with BMI, so average body temperature can be estimated based on BMI.

[0501] Other physiological factors or influences may also be considered in determining the compensated analyte concentration level, such as local glucose concentration variations (as opposed to systemic glucose levels), compartmental bias (differences in glucose concentrations in interstitial fluid versus blood), and non-enzymatic sensor bias.

[0502] In some instances, sensor signals from optical sensors and light sensors with light sources can serve as inputs to temperature compensation methods. For example, optical sensors can be used to detect blood flow or perfusion in a subject's skin. An optical sensor and light detector with a light source near the subject's skin can detect blood flow velocity and red blood cell count in the area directly beneath the sensor. Blood flow near the skin varies with temperature, activity, and stress levels. In some instances, blood perfusion information obtained from the optical sensor can be used to determine effort (e.g., athletic effort). For example, when running uphill or downhill, the number of steps will be roughly the same, but uphill runs require more effort, resulting in higher blood perfusion. On downhill runs, blood perfusion will decrease. Specific motion detection can be used to refine temperature compensation algorithms used during exercise. In some instances, optical sensors can detect exercise that is less noticeable from an accelerometer (e.g., weight training) because it involves fewer or slower movements. In some instances, optical sensors can be used in combination with accelerometers to detect exercise status and the amount of motion during exercise.

[0503] In some instances, location information (e.g., global positioning sensor data or network connectivity) can be used as input for determining temperature compensation or determining confidence in temperature measurements. For example, location can be used to determine confidence in temperature measurements by comparing temperature measurements with temperature characteristics of the location. For example, activities associated with the location (e.g., swimming, sunbathing, running, skiing) can determine confidence in low-speed, high-speed, or rapidly changing temperature measurements. In another example, weather characteristics at the location (e.g., ambient temperature) can determine confidence in temperature measurements. In another example, location information related to circadian rhythms (e.g., typically sleeping at home) can be used to confirm temperature measurements, or deviations from circadian rhythms can be confirmed by deviations from patterns in the location information (e.g., if the subject is not at home, such as outdoors at night, camping, or in a location that may have different temperature characteristics, confidence in low temperatures at night can be determined).

[0504] In some instances, the detection of fever (e.g., using a sensor) or the reporting of fever (e.g., via an application on a smart device) can be used as an input for determining temperature compensation. For example, temperature compensation can be suspended during fever because the normal model may not be applicable. In another example, the model can be modified or a different model can be applied to compensate for temperature changes caused by fever. In some instances, fever can be confirmed with other information. For example, Figure 15A The correlation of the rate of change of the sensor output shown in Figure C can be used to confirm the detection of fever. In another example, the patient can be queried about fever ("Do you have a fever?") or other events that may cause temperature changes ("Have you taken a shower recently?"), such as through a smart device.

[0505] Exemplary Model

[0506] Figure 19 is a schematic illustration of an exemplary model that can be used to determine an output from two or more inputs. For example, the model can learn patterns or relationships from prior data and apply the learned patterns or relationships to determine the output. This may include, for example, learning from prior data for a specific host, population, or one or more clinical trials.

[0507] In various instances, input can be received or sensed simultaneously or at different time points. In some instances, two inputs (e.g., temperature and analyte sensor output) can be applied to the model. The model can also receive other inputs, such as time (e.g., from a clock circuit) or sensitivity (e.g., factory-calibrated sensitivity). In one instance, the model can include submodels 1902, 1904, 1906. The submodel can take into account temperature-related factors, such as local glucose levels, compartment bias values, non-enzymatic sensor bias levels, and sensor sensitivity. In one instance, each model can define different relationships (e.g., linear, nonlinear) between input and temperature-related factors. For example, model 1902 can be based on a first nonlinear relationship, model 1904 can be based on a second nonlinear relationship, and the output model can be based on a linear relationship. In various instances, the processor can retrieve model information or input data from a lookup table in a memory, or can store and retrieve values ​​or states from the past in the memory, or can retrieve the function or other aspects of the model from the memory. The retrieved information may be combined with the latest or real-time information and applied to a model to generate an output, which may be a compensated glucose concentration level, or the output may be used to determine a compensated glucose concentration level.

[0508] Figure 20A2 is a flowchart diagram of an exemplary method 2000 for determining a compensated glucose concentration value using a model. Method 2000 may include receiving a temperature sensor signal at 2002. For example, the temperature sensor signal may be received from a subcutaneous temperature sensor near an analyte sensor, or the temperature sensor signal may be received from a non-subcutaneous sensor (e.g., on an external sensor electronic device, such as a CGM transmitter). At 2004, method 2000 may include receiving an analyte sensor signal, such as a signal from a glucose sensor. At 2006, the temperature sensor signal and the glucose sensor signal may be applied to the model. For example, the temperature sensor signal and the glucose sensor signal may be applied to a state model (e.g., a hidden Markov model) or a neural network. In some instances, multiple temperature sensor signals may be applied to the model. The signal may be processed or analyzed to determine a pattern (e.g., one or more linear or nonlinear trends). The relationship between the defined or learned temperature and glucose sensor values ​​and the compensated glucose concentration value may be used to return a compensated glucose concentration value using the model. At 2008, the compensated glucose concentration value may optionally be displayed on a user device. At 2010, therapy may be delivered based at least in part on the compensated glucose concentration value. For example, insulin delivery via a pump may be controlled based at least in part on the compensated glucose concentration value. In some instances, the processor may determine an insulin dose, delivery time, or delivery rate (or any combination thereof) based at least in part on the glucose concentration value. In some instances, the pump may automatically deliver insulin, or the pump may recommend an insulin time, rate, and dose to the user. In other instances, the smart pen may receive the compensated glucose concentration value and determine a dose or delivery time, which may be displayed to the user or automatically loaded for delivery, or both.

[0509] exist Figure 20A In an example, the model is trained to provide a compensated glucose concentration as its output. In other examples, as described herein, the model is trained to generate an output comprising one or more compensated characteristics of a glucose sensor. For example, as described herein, temperature compensation can be applied to sensor characteristics to produce one or more compensated sensor characteristics. The one or more compensated sensor characteristics can then be applied to raw sensor data to generate a compensated glucose concentration. Exemplary sensor characteristics that can be compensated using the trained model include, for example, sensitivity, sensor baseline, and the like.

[0510] Figure 20Bis a flowchart illustration of another exemplary method 2001 for determining a compensated glucose concentration value using a model. Method 2001 may include receiving a temperature sensor signal at 2012. For example, the temperature sensor signal may be received from a subcutaneous temperature sensor near a glucose sensor, or the temperature sensor signal may be received from a non-subcutaneous sensor (e.g., on an external sensor electronics device, such as a CGM transmitter). At 2014, method 2001 may include receiving a glucose sensor signal, such as a signal from a glucose sensor. In some instances, the glucose sensor signal received at 2014 includes a raw sensor signal related to the current at the working electrode, such as one or more counts related to the current at the working electrode of the glucose sensor. In some instances, an analyte concentration, such as a glucose concentration, is included, such as derived from the raw sensor signal. In some instances, the glucose sensor signal includes the raw sensor signal and the analyte concentration.

[0511] At 2016, the temperature sensor signal and the glucose sensor signal can be applied to a model. For example, the temperature sensor signal and the glucose sensor signal can be applied to a state model (e.g., a hidden Markov model), a neural network, or other suitable network. In some instances, multiple temperature sensor signals can be applied to the model. The signals can be processed or analyzed to determine a pattern (e.g., one or more linear or nonlinear trends). The defined or learned relationship between the temperature and glucose sensor values ​​and one or more glucose sensor characteristics (e.g., sensitivity, baseline, etc.) can be used to return values ​​for one or more compensated glucose sensor characteristics.

[0512] At 2018, the compensated glucose sensor characteristics are used to generate a compensated glucose concentration. At 2020, the compensated glucose concentration value can optionally be displayed on the user device. At 2022, therapy can be delivered based at least in part on the compensated glucose concentration value. For example, insulin delivery via a pump can be controlled at least in part based on the compensated glucose concentration value. In some instances, the processor can determine insulin dosage, delivery time, or delivery rate (or any combination thereof) based at least in part on the glucose concentration value. In some instances, the pump can automatically deliver insulin, or the pump can suggest insulin time, rate, and dosage to the user. In other instances, the smart pen can receive the compensated glucose concentration value and determine dosage or delivery time, which can be displayed to the user or automatically loaded for delivery, or both.

[0513] Conductivity-based compensation

[0514] In some examples, the temperature compensation or estimated subcutaneous temperature can be based at least in part on the conductivity (or resistance, the inverse of conductivity) of the analyte sensor or portion thereof. For example, Figure 2C The measured conductance of the illustrated analyte sensor 10 or the conductive portion 286 of the analyte sensor may be used for temperature compensation or to estimate subcutaneous temperature.

[0515] Empirical measurements (discussed below and in Figure 21 Figure 2 (shown in Figure 3) shows that the conductance of an analyte sensor can be significantly dependent on temperature. In some examples, this relationship between conductance and temperature can be used to estimate subcutaneous temperature, which can be used in temperature compensation models or other methods. In other examples, the relationship between conductivity and temperature can be applied directly (e.g., without using an estimated temperature) to compensate for subcutaneous temperature variations.

[0516] Figure 21 is a graph of sensor conductivity 2102 and transmitter temperature 2104 versus time. A strong correlation can be observed between temperature and conductivity: as transmitter temperature increases, sensor conductivity increases (approximately 6% per degree Celsius), and vice versa. Although the data shown is for transmitter temperature, the same correlation exists between subcutaneous temperature and conductivity.

[0517] The correlation between temperature and sensor conductance can be used to determine a temperature estimate at the working electrode temperature (e.g., to determine the subcutaneous temperature at the analyte sensor). In various examples, the system or method can use a non-subcutaneous temperature (e.g., the transmitter temperature), or the system or method can compensate without using a non-subcutaneous temperature (e.g., as described above, the system can use an assumed reference temperature or a factory calibrated temperature).

[0518] A preliminary estimate of the working electrode temperature can be made using one (or more) of a variety of models (e.g., linear models, delay models, partial differential equation models, time series models). The preliminary estimate can also be based on a predetermined reference value or other methods described herein. This preliminary estimate can then be used to determine an adjusted temperature using one or more sensor conductivity measurements. For example, as the conductivity changes, a corresponding temperature change can be calculated, and this temperature change can be applied to (e.g., added to or subtracted from) the initial temperature estimate or reference temperature to determine the temperature at the time of the sensor conductivity measurement.

[0519] In various examples, conductivity-based temperature compensation techniques can be combined with any of the examples described herein to determine an estimated subcutaneous temperature, or the effect of an estimated subcutaneous temperature on a signal from an analyte sensor. For example, an estimated subcutaneous temperature (e.g., the temperature at a working electrode of an analyte sensor) can be determined from a first measured non-subcutaneous temperature (e.g., the transmitter temperature), and the conductivity of the analyte sensor or a portion thereof can be measured simultaneously with the non-subcutaneous temperature measurement. At a later time, a second subcutaneous temperature can be estimated based on the difference between the conductivity value (single point or average) at the later time and the first conductivity value (single point or average).

[0520] Figure 21 The conductance values ​​plotted in 2012 show an upward drift over time. This drift component may be related to sensor sensitivity drift as described in US Patent Publication No. US20150351672, which is incorporated by reference.

[0521] In some examples, the system can implement one or more techniques to account for drift and avoid or reduce the impact of conductivity drift on subcutaneous temperature estimates or compensated data. Such drift-addressing techniques can include, for example, resetting the temperature estimate (e.g., recalculating the estimated temperature and conductivity baseline for compensating future values), average-based compensation (e.g., compensating for a moving baseline conductivity based on a long-term average, a weighted average, or a rolling window).

[0522] In various examples, the subcutaneous temperature estimate or conductivity baseline can be periodically refreshed. For example, a new subcutaneous temperature estimate (e.g., working electrode temperature) can be cyclically (e.g., periodically) refreshed by recalculating the estimate (e.g., using the techniques described above). Future analyte concentration values ​​can be compensated for conductivity values ​​(or averages) that are temporally correlated (e.g., contemporaneous) with the new subcutaneous temperature estimate. This refreshing (resetting) of the conductivity-based temperature estimate can remove or reduce the effects of drift components, thereby producing a more accurate temperature estimate.

[0523] In some instances, a reset, refresh, or error state can be triggered based on the satisfaction of a condition. The condition can be based, for example, on comparing a conductivity-compensated temperature estimate to a subcutaneous temperature estimate determined in a different manner (e.g., not based on conductivity) (such as a newly calculated subcutaneous temperature estimate based on transmitter temperature and a linear model, a delay model, or other models discussed herein). For example, the condition can be satisfied when the two values ​​differ by more than a set threshold. In some instances, the error state can be changed (e.g., an error state can be declared) when the comparison satisfies the error condition. In some instances, the conductivity baseline can be reset (e.g., the baseline can be updated to a new value or average), or the new temperature estimate can be associated with a particular conductance value. In some instances, a tiered approach can be applied such that a reset procedure can be applied when the difference exceeds a reset threshold, and an error condition can be applied when the difference is above an error threshold that is greater than the reset threshold (in which case a reset may or may not still occur). Such a reset of the conductance-based temperature estimate can eliminate or reduce Figure 21 A drift component is visible in the conductance signal (e.g., the conductance value drifts upward over time).

[0524] In some examples, a digital high-pass filter can be applied to block low-frequency drift components in the conductance signal and pass only temperature-related changes. The filter characteristics, such as the cutoff frequency, can be based on actual measured temperature data, preferably subcutaneous temperature measurement data (e.g., by frequency analysis such as Fourier decomposition).

[0525] While the above discussion focuses on conductivity and resistance, it will be appreciated that temperature compensation or temperature estimates may alternatively be based on other conductive properties (eg, impedance or admittance), depending on the configuration of the analyte sensor system and the type of signal applied.

[0526] Figure 2222 is a flowchart illustrating an exemplary method 2200 for temperature compensation using conductivity or impedance. At 2202, a first value indicative of the conductivity of a sensor component at a first time is determined. At 2204, a second value indicative of the conductivity of the sensor component at a later time is determined. At 2206, a signal representing an analyte concentration in a host is received. At 2208, a compensated analyte concentration level is determined based at least in part on a comparison of the second value and the first value. In some examples, determining the first value may include determining an average conductivity over a time period approximately including or including the first time. In some examples, the method may further include determining a first estimated subcutaneous temperature temporally correlated with the first value, and determining a second estimated subcutaneous temperature temporally correlated with the second value, wherein the second estimated subcutaneous temperature is determined based at least in part on a comparison of the second value and the first value. In some examples, the method may include determining a third estimated subcutaneous temperature temporally correlated with the second value, determining whether a condition is satisfied based on a comparison of the third estimated subcutaneous temperature and the second estimated subcutaneous temperature, and declaring an error or triggering a reset in response to the condition being satisfied. The method may include triggering a reset, wherein triggering a reset includes determining a subsequent estimated subcutaneous temperature based on the third estimated temperature and the second value, or based on a third value indicative of conductivity at a subsequent time and a fourth estimated subcutaneous temperature having a time correlation with the third value.

[0527] In some examples, method 2200 can include compensating for drift in the conductance value by applying the above-described methods, or by applying a filter.

[0528] Figure 23 23 is a flowchart illustrating an exemplary method 2300 for determining subsequent temperature estimates using conductivity or impedance. At 2302, a first value indicative of the conductivity of a sensor component at a first time can be determined, for example, by measuring the conductivity or impedance of the sensor component. At 2304, a second value indicative of the conductivity of the sensor component at a later time can be determined, for example, by taking a second measurement to determine the conductivity or impedance. At 2306, an estimated subcutaneous temperature can be determined based at least in part on a comparison of the second value with the first value. As described above, a non-subcutaneous temperature measurement can be used to determine the initial estimated temperature, and subsequent estimated subcutaneous temperatures can be determined based on changes in the value indicative of conductivity. When the change exceeds a threshold, or the comparison satisfies an error condition or reset condition, an error condition can be declared or a reset can be triggered. It should be understood that any estimated temperature described herein can be used as an input to any temperature compensation model described herein.

[0529] Temperature sensor calibration

[0530] In some instances, the temperature sensor may be calibrated during a manufacturing step where the processing temperature is known or controlled. For example, some sensor electronics packages that use adhesives or structural agents (e.g., epoxy resins) can be cured at a known or controlled temperature. The temperature sensor can be calibrated during the curing step. In another example, the temperature sensor can be calibrated when the analyte sensor is calibrated. In another example, the temperature sensor can be calibrated during the initial wear period. For example, the temperature sensor output during the initial period (e.g., the first one or two hours after the analyte sensor is activated) can be calibrated to a predetermined average value (e.g., 37°C).

[0531] Figure 10 1 is a schematic illustration of a method 1000 for temperature compensating a continuous glucose sensor system using a reference temperature value. The method may include determining a first value from a first signal indicating a temperature parameter of a component of the continuous glucose sensor system at 1002. The method may include receiving a glucose sensor signal indicating a glucose concentration level at 1004. The method may include comparing the first value to a reference value at 1006.

[0532] The method may include, at 1008 , determining the temperature compensated glucose level based on the glucose sensor signal and a comparison of the first signal to the reference value.

[0533] In some examples, the method may further include determining a reference value. For example, the reference value may be determined from the first signal. For example, a continuous glucose sensor system may include a glucose sensor that is insertable into a host, and the reference value is determined during a specified time period after the glucose sensor is inserted into the host or after the glucose sensor is activated. In other examples, the reference value may be determined during the manufacturing process.

[0534] In some examples, the reference value may be during a first time period, and the first value may be determined during a second time period after the first time period (e.g., the reference value may be determined after sensor insertion and subsequent sensor readings may be compensated relative to the reference value). In some examples, the reference value may be a long-term average, and the first value may be a short-term average. In some examples, the reference value may be updated based on subsequently received temperature values. For example, the reference value may be updated based on one or more temperature signal values ​​obtained during a third time period after the second time period.

[0535] In some examples, the reference value may be determined based on an average of a plurality of sample values ​​obtained from the first signal.

[0536] Figure 111 is a flow chart illustration of a method 1100 for temperature compensation of an exemplary continuous glucose sensor. The method may include receiving a calibration value for a temperature signal at 1102. In some instances, the calibration value may be obtained during a manufacturing step with a known temperature. In some instances, the calibration value for the temperature signal may be obtained within a specified time period after the continuous glucose sensor is inserted into the host. For example, the calibration value may be determined after a warm-up period, which may be, for example, a time period of two hours after the sensor is inserted or activated. For example, the calibration value may be determined within a subsequent time period after the warm-up period (e.g., 2-4 hours after insertion). The method may include receiving a temperature signal indicating a temperature parameter from a temperature sensor at 1104. The method may include receiving a glucose signal indicating a glucose concentration level from a continuous glucose sensor at 1106. The method may include determining a temperature-compensated glucose concentration level based at least in part on the glucose signal, the temperature signal, and the calibration value at 1108.

[0537] Methods involving relative temperature differences

[0538] In some examples, relative temperature changes can be used for temperature compensation. For example, uncalibrated temperature sensors or those with lower absolute accuracy can be used for temperature compensation by basing temperature compensation on deviations from a reference, as opposed to knowledge of the absolute temperature. This can include, for example, using a personalized dynamic reference temperature (e.g., a reference temperature determined for a specific sensor or time period that can be periodically refreshed or recalculated) and applying compensation using deviations from that reference temperature.

[0539] In some examples, a temperature difference from a reference state can be determined based on a change in the first value relative to the reference value, without calibrating the temperature against the reference value. This can, for example, compensate for temperature differences from a reference value even if the absolute temperature is not determined, which can be useful when ensuring accurate absolute temperature when the temperature sensor is not factory calibrated or when using a sensor with good relative accuracy or precision but less reliable absolute accuracy or precision. In some examples, a temperature-compensated glucose level can be determined based at least in part on a temperature-dependent sensitivity value that varies based on the deviation of the first value from the reference value.

[0540] In some instances, temperature compensation can be performed using a temperature sensor with low absolute accuracy. For example, even if a sensor is inaccurate in an absolute sense (e.g., a ±3°C or 5°C variation in absolute temperature), it may be sufficiently accurate in a relative sense (e.g., accurately detecting that the sensor is 1°C warmer than a previous (reference) time point). Using these types of sensors can be advantageous because they can be built into the sensor electronics for other purposes (e.g., detecting overheating) and may require simpler or less expensive calibration procedures.

[0541] In one example, a reference temperature may be obtained when a blood glucose value is received (e.g., using a finger stick blood glucose meter). For example, when a blood glucose value is received, a glucose sensitivity may be determined (e.g., calculated) based on a signal from an analyte sensor (glucose sensor), and the signal from the temperature may be used (e.g., declared) as a reference temperature. Thereafter, the signal from the temperature sensor may be used to determine a temperature difference from the reference temperature, and temperature compensation may be performed based on the temperature difference. For example, it may later be determined that the temperature is 1.5°C higher than the reference temperature, and temperature compensation may be applied based on the 1.5°C difference. In some examples, temperature compensation may be based on a raw signal or a processed signal from the temperature sensor, as opposed to a calculated temperature difference.

[0542] In various instances, a reference temperature can be determined over a specified time period, for example, the first two hours or the first 24 hours after the sensor period is started. In one instance, the reference temperature can be the average (e.g., mean or median) temperature over the specified time period. In some instances, the reference temperature can be used for the remainder of the time period. In other instances, the reference temperature can be updated cyclically or periodically. For example, the reference can be updated every 24 hours, and the reference temperature can be used for the subsequent 24 hours. In some instances, for the purpose of temperature compensation, the reference temperature can be assumed to be a specific value (e.g., 35°C, which can be assumed to be the average subcutaneous temperature of a general subject population). In some instances, the temperature sensor value at the time of calibration (during manufacturing or after insertion) can be used as a reference value.

[0543] The real-time temperature compensation can be determined using any of the compensation methods described herein (linear method, delayed linear method, polynomial method, etc.) using the real-time (or most recent) temperature signal and the reference temperature value. In some examples, using relative temperature temperature compensation can achieve a 75% (or greater) improvement in MARD achieved using a calibrated temperature sensor.

[0544] sports

[0545] Motion or conditions indicative thereof may be detected and used to determine temperature compensation. For example, motion may be detected based on temperature data, accelerometer data (e.g., to detect walking or running), position data (e.g., based on presence in a location associated with motion, or based on positional movement associated with walking, running, or cycling), or physiological data (e.g., respiration, heart rate, or skin surface condition).

[0546] In some examples, a method may include detecting a rise in a first temperature signal and a fall in a second temperature signal, and adjusting a temperature compensation model based on the detected rise and fall. In some examples, a period of exercise (e.g., outdoor exercise or convective cooling exercise) may be detected based at least in part on the detected rise in the first signal and the fall in the second signal. For example, a fall in the second signal may indicate the start of an exercise period in a cool environment (e.g., being outside on a cold day, or in an actively cooled environment, such as near a fan). A temperature signal from an external second sensor (e.g., in the sensor electronics) may indicate a temperature drop in response to the outdoor ambient temperature being lower than the indoor ambient temperature, or in response to convective cooling (e.g., due to running or cycling, or from a fan, such as a treadmill or other exercise space). A temperature rise (or steady-state temperature) in a first temperature signal, such as received from an external sensor positioned closer to the body than the second sensor, or from a sensor located subcutaneously (e.g., on or integrated into a glucose sensor), may indicate body warming due to exercise, or a lack of temperature drop despite a change in ambient temperature due to heat generated by exercise.

[0547] Figure 12 is a flow chart illustration of an exemplary method 1200 for temperature compensation using two temperature sensors. The method 1200 may be performed, for example, in Figure 2C Method 1200 may include, at 1202, receiving a glucose signal representing a glucose concentration level of a host from a glucose sensor.

[0548] The method 1200 may include receiving a first temperature signal indicative of a first temperature parameter proximate the host or the glucose sensor at 1204. The method 1200 may include receiving a second temperature signal indicative of a second temperature parameter at 1206. In some examples, the first temperature signal may be received from a first temperature sensor coupled to the glucose sensor, and the second temperature signal may be received from a second temperature sensor coupled to the glucose sensor.

[0549] Method 1200 may include determining a compensated glucose concentration level based at least in part on the glucose signal, the first temperature signal, and the second temperature signal at 1208. In some examples, the compensated glucose concentration level may be determined based at least in part on a temperature gradient between the first temperature sensor and the second temperature sensor or based at least in part on a heat flux between the first temperature sensor and the second temperature sensor. In some examples, method 1200 may include detecting a period of exercise based on the two temperature signals (e.g., based on a detected divergence in temperature) and compensating accordingly (e.g., applying a motion model) as described above.

[0550] In some examples, method 1200 may also include determining, based at least in part on the second temperature signal, whether the temperature change is due to radiant heat or ambient heat, and adjusting the temperature compensation model based on the determination. For example, when the second temperature signal is from a sensor near an outer surface of the wearable sensor and the second temperature signal is significantly higher than the first temperature signal, it can be inferred that the sensor is exposed to radiant heat. In some examples, the rate of change may also be considered. For example, a rapid rate of change may indicate immersion in hot water, wherein a more gradual rate of change may indicate exposure to radiant heat. In some examples, the state model may include one or more of a radiant heat state, a submersion state, a motion state, an ambient air temperature state, or an ambient water temperature state, and the state model may be used to temperature compensate the estimated glucose concentration value.

[0551] Other uses for temperature sensors

[0552] Temperature sensors can be used for a variety of other purposes. In some instances, BMI can be estimated from temperature. For example, lower temperatures tend to be associated with higher BMI. The estimated BMI value can be shared with other applications. For example, a decision support system can use BMI as an input to a model or algorithm to determine guidance for a subject (e.g., a glucose correction dose, exercise recommendations, or eating a certain amount or type of carbohydrates or food).

[0553] In some instances, an alarm or alert may be triggered when a temperature sensor indicates a temperature that satisfies a condition. For example, an alarm or alert may be triggered when a temperature sensor indicates a temperature that satisfies a statistical condition (e.g., a temperature that differs from an average or reference value by more than one standard deviation, or differs from an average or reference value by more than a specified number of standard deviations). For example, a subcutaneous temperature sensor, or a temperature sensor in a sensor electronics device, may be used to detect a patient's potentially dangerous or harmful condition (e.g., high fever, heat stroke, hypothermia, etc.), and the condition may be communicated by an alarm or alert (e.g., via the subject's smart device, or to a caregiver's smart device via a wireless network or the Internet). In other instances, potential overheating or overcooling of a sensor or sensor electronics device may be detected. In some instances, a potentially faulty temperature sensor (e.g., when a temperature sensor indicates a temperature within an unlikely range) may be identified based on a temperature sensor signal that satisfies a condition.

[0554] In various examples, temperature compensation, as described herein, can be used in conjunction with analyte sensors for measuring analytes other than glucose. Temperature compensation techniques can be used with analyte sensors for measuring any analyte, including the example analytes described herein.

[0555] Additionally, in some instances, the temperature measured by a subcutaneous temperature sensor or a temperature sensor used in the sensor electronics described herein can be used to determine insulin dosage recommendations. For example, the host's body can utilize insulin differently depending on temperature. Temperature-dependent adjustments can be made to the host's insulin dosage based on the measured temperature.

[0556] Detect sensor disconnection or reuse of disposable sensors.

[0557] Sensor disconnection or reuse ("restart") of a disposable sensor can be detected based at least in part on a temperature change or the absence of a temperature change. Some analyte-based sensor systems can be configured with a disposable (replaceable) sensor component and a reusable sensor electronics package, such as a CGM transmitter, which can be mechanically and electrically coupled to the disposable sensor component. The disposable sensor component can be designed to extend into the subcutaneous layer of the host and operate for several days (e.g., 7 days, 10 days, or 14 days) before the disposable sensor component is removed and replaced with a new disposable sensor component. Figure 1 As described in detail in the discussion of , the reusable transmitter can be wirelessly coupled to a control device (e.g., a smart device), which can include a user interface for inputting commands that can be sent to the transmitter. The user interface on the control device can allow one sensor session to be stopped and a new sensor session to be started.

[0558] A sensor session can be programmed to a defined period of time (e.g., 7 days), after which the session will expire (if not manually stopped via the user interface). After a sensor session expires or is stopped, a new session can be started via the user interface.

[0559] In some cases, a subject (e.g., a patient) can start a new sensor session without replacing the disposable sensor component, i.e., the subject can "restart" a session with the same disposable component that was used before stopping the session. Detecting such restart events can be useful for various reasons.

[0560] A sensor "reset" can be detected based at least in part on a signal from a temperature sensor in a sensor electronics package (e.g., a CGM transmitter). For example, if a subject intends to reuse a disposable sensor component, the subject will typically stop one sensor session and start a new session without removing the transmitter from the disposable component. This "reset" condition can be detected based on the absence of a temperature signal associated with removing the transmitter from the sensor.

[0561] When the transmitter is removed from the host and reconnected to a new sensor, a temperature signal including a temperature drop may be observed if the sensor electronics are away from the host for a long enough time (eg, one minute). Figure 18A Graph 1 shows the relationship between temperature and time, where the sensor electronics package (Dexcom CGM transmitter) was removed from the sensor (Dexcom glucose sensor) for one minute at 1:27 PM. A temperature drop of 1802 is visible in the temperature graph. Figure 18B A similar graph is shown where the sensor electronics package was removed for five minutes at 3:34 PM. A large temperature drop 1804 can be seen in the temperature graph, and a trend that the sensor takes over half an hour to return to the steady-state temperature 1806 (approximately 33° C.) detected before the change.

[0562] In various instances, a disconnect event (e.g., removal of a CGM transmitter from a sensor) can be identified based on the amount of the temperature drop (e.g., 3°C or 5°C over a short period of time), the slope of the drop, or the consistency of the signal during the drop (smoothness or lack of variability), or a combination thereof.

[0563] A sensor restart can be identified by the absence of a disconnection event when a period is stopped or started. In some instances, a disconnection event can be determined from a temperature signature (e.g., a temperature drop) in combination with other information (e.g., the end of a sensor period). For example, when a temperature signature associated with a disconnection occurs shortly after (or shortly before) the end of a period, it can be inferred that the sensor electronics have been removed from the disposable sensor. And when stopping and starting a sensor period but the absence of the above and Figure 18A and18B , it can be inferred that the disposable sensor has been reused because replacing it with a new sensor requires removing the sensor electronics (CGM transmitter) from the sensor. In some examples, sensor removal can be determined from the temperature signature in combination with accelerometer data (e.g., rapid or large movements that may occur during a transmitter disconnection followed by a temperature drop) or other sensor data.

[0564] Figure 13 1 is a flow chart illustrating an exemplary method 1300 for determining a restart of a continuous glucose (or other analyte) monitor. Method 1300 may include, at 1302, receiving a temperature signal indicative of a temperature parameter from a temperature sensor on the continuous glucose monitor. Method 1300 may also include determining, from the temperature signal, that the continuous glucose monitor has been restarted. For example, as described above, a restart may be identified from the absence of a disconnection event in the temperature signature, optionally in combination with other sensor information.

[0565] A subcutaneous temperature sensor can also be used to detect restarts. When the temperature sensor is located on the subcutaneous analyte sensor, the temperature reading of the sensor will typically be lower than the body temperature when the sensor is first inserted (e.g., closer to the ambient air temperature), and as the sensor absorbs heat from the body, the detected temperature may be expected to gradually rise to body temperature. In one instance, determining that the continuous glucose monitor has been restarted by the temperature signal can include comparing a first temperature signal value before the sensor is turned on with a second temperature signal value after the sensor is turned on, and declaring the continuous glucose monitor to be restarted when the comparison meets a similarity condition. The similarity condition can include a temperature range. For example, when the sensor is restarted (rather than replaced), the temperature of the subcutaneous sensor will typically not change, or any change will be gradual. After replacing the sensor, more significant temperature changes may occur (e.g., the temperature displayed by the new sensor may be different from that of the old sensor).

[0566] Determine anatomical location

[0567] In some instances, temperature information can be used to determine the anatomical location or type of anatomical location at which the sensor is worn. For example, the sensor can be worn on the arm or the abdomen. A sensor (or sensor electronics) may experience colder temperatures when worn on the arm compared to the abdomen. For example, this may be driven by the fact that the upper arm is farther from the body core, or the arm is more likely to be exposed to air (e.g., when wearing short-sleeved clothing). Sensors worn on the arm may also experience greater temperature variability, especially during sleep (e.g., at least part of the night, when the arm is more likely to be outside of any sheets or blankets than the abdomen). In some instances, the anatomical location can be determined based on the average (e.g., mean or median) temperature over a specified time period (e.g., within the first 24 hours of wearing). For example, the sensor device location can be declared as the abdomen when the average temperature meets a condition, such as when the average temperature exceeds a specified temperature threshold (e.g., 32°C). In another example, for example, an abdominal sensor position may be detected based on a variability condition, such as a first standard deviation of temperature variation over a specified period (e.g., nighttime or sleep time) being less than a specified amount (e.g., less than 1° C.). In some examples, an abdominal position may be detected based on a combination of temperature and variability conditions, such as an abdominal position may be declared when the average temperature exceeds a specified temperature threshold (e.g., 32° C.) or when the first standard deviation of temperature variation over a specified period is less than a specified amount (e.g., less than 1° C.). Figure 16 is a graphical illustration showing temperature (y-axis) versus time (x-axis) for two sensors. A first graph 1602 (dashed line) shows data from a sensor placed on the abdomen. A second graph 1604 (solid line) shows data from a sensor placed on the arm. For the first four hours, the host was not wearing the sensor (e.g., not yet inserted), and the data from the sensors were roughly correlated. After four hours, the sensor was inserted into the host, and the temperature rose rapidly. After this transition, the change between the first graph 1602 and the second graph 1604 is apparent, as the second graph 1604 (corresponding to the arm-mounted sensor) shows a lower temperature and higher variability. Figure 17is a graph of the standard deviation versus mean temperature for dozens of sensor devices over the previous 24 hours. Using the method discussed above (SD>1.0 and mean temperature<32°C), the sensor device located on the arm was identified with high sensitivity (all arm-mounted sensors except five were identified as such) and good specificity (only six abdomen-mounted sensors were identified as being on the arm according to the method). In some instances, the exemplary temperature method can be combined with information from other sensors (e.g., accelerometer data) to further improve sensitivity and specificity. In some instances, a learned model (e.g., using a neural network) can be used to identify patterns or relationships, and the model can be applied to determine location. Such methods can achieve higher sensitivity or specificity. Although specific "arm" and "abdomen" locations are shown, other locations or categories can also be used (e.g., a lower back location can be determined, or a "torso" location can include both the abdomen and the lower back)

[0568] Figure 14 1400 is a flowchart illustrating an exemplary method for determining an anatomical location of a sensor. The method may include, at 1402, receiving a temperature signal indicative of a temperature of a component of a continuous glucose sensor on a host. The method may include, at 1404, determining an anatomical location of the continuous glucose sensor on the host based at least in part on the received temperature signal. In some examples, the anatomical location may be determined based at least in part on the sensed temperature. In some examples, the anatomical location may be determined based at least in part on the variability of the temperature signal. For example, a sensor inserted in a peripheral location (e.g., on an arm) or in a location less likely to be covered by clothing may see greater temperature variation than a sensor inserted in the abdomen or lower back. In some examples, the method may also include receiving an accelerometer signal, and determining the anatomical location may include determining the anatomical location based on the accelerometer signal. For example, a higher activity level or more frequent posture changes (both determined from the accelerometer signal) may indicate a peripheral location (e.g., on the back of the arm), while a lower activity level or less frequent posture changes, or more periodic posture changes (e.g., associated with sleeping or sitting), may indicate an abdominal or lower back location. In some examples, the distribution of the rate of change of position can be used to identify anatomical locations. For example, a distribution skewed toward higher rates of change can indicate peripheral locations (e.g., on the arm), while a distribution skewed toward lower rates of change can indicate locations on the torso (e.g., the abdomen). In another example, a neural network or other learned model can be used to learn patterns or relationships that can be used to determine or predict anatomical locations (e.g., using sensor data and optionally data indicating anatomical locations based on user input).

[0569] At 1406, in some examples, temperature compensation can be based at least in part on anatomical location. For example, a temperature compensation algorithm can account for the fact that subcutaneous temperature in the abdomen or lower back may change more slowly than that in the arm, which has less mass and can act as a heat sink or heat source.

[0570] Compression detection

[0571] In some instances, compression can be detected based at least in part on a signal from a temperature sensor. For example, compression of the sensor may occur when a person lies or leans against the sensor, which may occur during sleep. Compression of the glucose sensor may produce an estimated glucose value that is lower than the actual value. When a subject lies on a glucose sensor, the temperature of the sensor may rise. Compression of the sensor can be detected based at least in part on a rise in the temperature of the sensor. In one instance, a rapid drop in glucose levels that occurs simultaneously with or after a temperature rise can indicate that the sensor is compressed. In some instances, additional information such as activity information can be used in combination with temperature. For example, a rapid drop in estimated glucose combined with low activity (indicating that the subject is not moving) and a rise in sensor temperature (indicating that the subject is lying on the sensor) can indicate that compression is low. In some instances, an alarm can be triggered in response to possible low compression. For example, a notification can be delivered via a smart device, or a sound can be emitted from a smart device or sensor, which can prompt the subject to move away from the sensor to allow an accurate estimated glucose concentration level to be obtained.

[0572] Sleep detection

[0573] In some instances, sleep can be detected based at least in part on temperature sensor information. For example, higher temperatures can be observed during sleep. More consistent temperatures or temperature patterns can be observed during sleep. Sleep can be detected by applying a model or algorithm to detect periods of warm temperatures, consistent temperatures or temperature patterns (e.g., a binary pattern corresponding to a covered or uncovered arm sensor), and optionally combined with other sensor information. In some instances, temperature information can be combined with posture information, activity information, breathing or heart rate, or any combination thereof, from a 3D accelerometer to detect sleep. In some instances, alarm behavior can change in response to sleep detection. For example, the alarm threshold can be adjusted to reduce the number of alarms during sleep, or the alarm trigger can be adjusted to provide time to handle hypoglycemic events, or only certain types of alarms (e.g., more urgent alarms) can sound when sleep is detected.

[0574] In some examples, stress detection, compensation, or alarms can be provided or modified during sleep. For example, when a person is sleeping and an estimated glucose value suddenly drops sharply, stress can be inferred based on the sleep state and the sudden drop in the estimated glucose value, optionally in combination with other information such as a discontinuity in the glucose curve, a temperature increase, or other information.

[0575] Other Exemplary Temperature Sensors

[0576] In some instances, it is desirable to reduce the Figure 1 The hardware costs included in the analyte sensor system 8 are described. For example, components of the analyte sensor system 8, such as all or part of the sensor electronics 12 and / or the continuous analyte sensor 10, may be disposable products that are used for a sensor session lasting several days and then discarded. Therefore, it may be desirable to obtain highly accurate temperature values ​​from an inexpensive temperature sensor.

[0577] Various examples described herein relate to systems and methods for generating compensated temperature values ​​from a system temperature sensor using a trained temperature compensation model. In some examples, the trained temperature compensation model can compensate for factors that cause errors in the raw temperature data, such as noise or other nonlinearities. As described herein, using a trained model to compensate for temperature values ​​from a system temperature sensor can allow the use of a less expensive or more readily available system temperature sensor to generate acceptably accurate temperature values. For example, in some examples, as described herein, using a trained model can allow the use of a less expensive or more readily available temperature sensor, such as a sensor included with or generated from a suitable diode at an application specific integrated circuit (ASIC) or other component of the analyte sensor system 8.

[0578] The temperature compensation model can be any suitable type of model, including, for example, a neural network, a state model, or any other suitable training model. The input to the temperature compensation model can include, for example, raw temperature data and uncompensated temperature data. The raw temperature data includes data generated by a system temperature sensor to indicate temperature, such as current, voltage, count, etc. The uncompensated temperature data can include data indicating the uncompensated temperature. For example, in some instances, the temperature sensor provides data indicating the temperature derived from the raw temperature data. In some instances, the input to the temperature compensation model can include raw temperature data and uncompensated temperature data. In some instances, the output of the temperature compensation model can include a compensated temperature value.

[0579] In some examples, in addition to or in lieu of the compensated temperature value, the output of the temperature compensation model may include sensor characteristics that describe the relationship between the raw temperature data generated by the system temperature sensor and the corresponding temperature value. For example, the output of the temperature compensation model may include a slope and an offset. The slope and offset may be applied to the raw temperature data generated by the system temperature sensor to generate the compensated temperature value.

[0580] For example, a temperature compensation model can be trained using a reference temperature sensor that is more accurate than the system temperature sensor. Figure 24 24 is a flowchart illustrating an exemplary method 2400 for training a temperature compensation model. A system temperature sensor and a reference temperature sensor may be positioned to measure the temperature of an object (e.g., a surface), the amount of liquid in a container, etc. At 2402, the object is heated and / or cooled to a first temperature. While the object is at various temperatures, inputs may be provided to the temperature compensation model at 2404. In response to the inputs, the temperature compensation model generates one or more model outputs. At 2406, the one or more model outputs are compared to a reference temperature measured by a reference temperature sensor.

[0581] At 2408, the model parameters are modified based on the error between the reference temperature and the output of the temperature compensation model. The error indicates the difference between the compensated temperature value output by the model and / or generated using the model output and the reference temperature. The error is used to modify the parameters of the model. For example, method 2400 can be executed and repeated until the model converges. When the error of the temperature compensation model is always within an acceptable range, the model may converge. In some instances, a temperature compensation model is trained for each analyte sensor system 8. In other instances, the analyte sensor system 8 and the associated system temperature sensor can have similar characteristics, thereby allowing the temperature compensation model trained on one analyte sensor system 8 to be used for other analyte sensor systems 8, such as other analyte sensor systems 8 having similar components to the analyte sensor system 8 used to train the model, other analyte sensor systems 8 manufactured in the same batch as the analyte sensor system 8 used to train the model, and so on.

[0582] Figure 25 25 is a flow chart illustrating an exemplary method 2500 for utilizing a trained temperature compensation model. At 2502, data is received from a system temperature sensor. The data may include, for example, raw temperature data and / or uncompensated temperature data. At 2504, the data received from the system temperature sensor is applied to the model to generate one or more model outputs. The model outputs may include compensated temperature values ​​and / or system temperature sensor parameters, such as slope and offset, which may be used to generate the compensated temperature values.

[0583] Optionally, at 2506, the model output is used to generate a compensated temperature value. For example, if the model output includes a compensated temperature value and / or if the model output does not include system temperature sensor parameters, generating the compensated temperature value at operation 2506 can be omitted. At 2508, the compensated temperature value is applied. For example, the compensated temperature value can be applied in any manner described herein in conjunction with utilizing temperature for the analyte sensor.

[0584] In some examples, operations 2502 and 2504 are performed using a portion of raw sensor data received from the sensor at the beginning of a sensor period, for example. Applying the raw temperature value to the model can generate system temperature parameters, such as slope and offset. The system temperature parameters are applied to subsequently received raw sensor data to generate subsequent compensated temperature values.

[0585] As described herein, the host's motion state can affect the temperature compensation model to be applied to generate the temperature compensated glucose concentration. The host's motion state can be determined in a variety of different ways, including, for example, using a temperature compensation model as described herein. Figure 7 In some examples, in addition to or instead of using a third sensor, other techniques can be used to detect motion.

[0586] Figure 26 2 is a flowchart illustrating an exemplary method 2600 for detecting motion state. Exemplary method 2600 detects the motion state of a host by examining the background noise of a glucose sensor signal, the background noise of a temperature parameter signal, or both. Background noise is the noise level associated with a signal. For example, background noise can be the sum of noise sources in the signal other than the target value. For example, the background noise of a glucose sensor signal is the sum of noise sources in the signal other than glucose. The background noise of a temperature parameter signal is the sum of noise sources in the temperature parameter other than the temperature indication. In some instances, when the host is in motion, physiological behaviors associated with motion manifest as additional noise sources that affect the glucose sensor signal and / or the temperature parameter signal. Therefore, method 2600 detects motion state by measuring the corresponding background noise. Method 2600 can be performed at the analyte sensor system 8, for example, at the sensor electronics 12 and / or at the display devices 14, 16, or 20.

[0587] Method 2600 may include accessing a glucose sensor signal at 2602. For example, the glucose sensor signal may be received from a continuous glucose monitor (CGM). Method 2600 may include accessing a temperature parameter signal at 2604. Accessing the temperature parameter signal may include, for example, receiving a signal indicating a temperature, a temperature change, and / or a temperature excursion.

[0588] Method 2600 may include, at operation 2606, determining a noise floor of the glucose sensor signal, the temperature parameter signal, or both. One or more noise floors may be determined in any suitable manner. In some examples, the noise floor of a signal may be approximated by finding a minimum value of the signal. In another example, spectral analysis may be used to find the noise floor of the signal.

[0589] Method 2600 can include determining whether a noise floor threshold is met at 2608. In some examples, the threshold at 2610 is met if the noise floor of the glucose sensor signal is greater than a first threshold or if the noise floor of the temperature parameter signal is greater than a second threshold. In some examples, the threshold at 2610 is met if the noise floor of the glucose sensor signal is greater than a first threshold and if the noise floor of the temperature parameter signal is greater than a second threshold.

[0590] If the background noise threshold is met, the host is in motion. Therefore, method 2600 includes modifying temperature compensation based on the temperature parameter signal at 2610 to take into account the motion state. Examples of how to perform this operation are described herein with respect to method 700 (e.g., 708 and 710) and method 800 (806). For example, as described herein, the temperature parameter signal can be applied to the motion model before being used to generate a temperature-compensated glucose concentration. If the background noise threshold is not met, the host may not be in motion, and an indication of a no-motion state can be returned at 2612. Alternatively, instead of sending an indication of a no-motion state, method 2600 can instead suppress modifying temperature compensation at 2612.

[0591] As described herein, detecting that a host is in motion is based on the rate of change of a temperature parameter. In some examples, this can be performed using a change distribution function. The change distribution function indicates the distribution of the rate of change over consecutive samples of a signal. Figure 27 27 is a graph 2700 showing a first change distribution function and a second change distribution function 2702, the first change distribution function showing a host in a stationary (e.g., not moving) state, and the second change distribution function 2704 showing a host in motion. In graph 2700, the horizontal axis indicates the rate of change of the temperature signal, which indicates the subcutaneous temperature at the glucose sensor. The vertical axis indicates the cumulative distribution of the rate of change. As shown, the cumulative distribution function 2702 is approximately centered around a zero rate of change, which means that approximately half of the rate of change between consecutive samples is greater than zero, and approximately half is less than zero. The cumulative distribution function 704 is tilted toward the low side, which means that when the host is in motion, there are more rates of change less than zero than rates of change greater than zero. As described herein, this can be exploited by examining the rate of change between temperature parameter signal samples (such as the exemplary histogram 2706).

[0592] Figure 28 is a flowchart illustration of an exemplary method 2800 for detecting motion using a rate-of-change distribution of temperature parameter signal samples. The method 2800 may be performed at the analyte sensor system 8, such as at the sensor electronics 12 and / or at the display devices 14, 16, 20.

[0593] Method 2800 may include accessing a current temperature parameter signal sample at 2802. Method 2800 may include determining a rate of change between the current temperature parameter signal sample and a previous temperature parameter signal sample at 2804. The rate of change may be a difference. As described herein, the rate of change may be negative (e.g., if the current sample is less than the previous sample) or positive (e.g., if the current sample is greater than the previous sample). Method 2800 may include storing the current rate of change at 2806.

[0594] At 2808, a classifier is applied to the historical rates of change, including the newly stored rate of change. For example, the classifier may represent a distribution of rates of change over a predetermined number of samples (e.g., 30 samples). The distribution of rates of change measured over the predetermined number of samples is compared to the classifier. At 2810, a determination is made as to whether the measured distribution of rates of change satisfies the classifier. For example, the classifier may describe a number or range of rates of change measured between samples that fall within a plurality of ranges. Table 3 below provides exemplary classifiers:

[0595] Table 3:

[0596] <-0.4 -0.4->-0.2 -0.2->0.2 0.2-0.4 >0.4 >2 >10 >10 <5 <2

[0597] In the example of Table 3, the rate of change measurement distribution satisfies the classifier if the number of measured rates of change less than -0.4°C / min is greater than 2, the number of measured rates of change between -0.4 and -0.2°C / min is greater than 10, and so on.

[0598] If the classifier is satisfied, the host is in motion. Therefore, method 2800 includes modifying temperature compensation based on the temperature parameter signal at 2612 to take into account the motion state. Examples of how to perform this operation are described herein with respect to method 700 (e.g., 708 and 710) and method 800 (806). For example, as described herein, the temperature parameter signal can be applied to the motion model before being used to generate a temperature-compensated glucose concentration. If the classifier is not satisfied, the host may not be in motion, and an indication of a no-motion state can be returned at 2614. Alternatively, instead of sending an indication of a no-motion state, method 2600 can instead suppress modifying temperature compensation at 2614.

[0599] In some instances, any of the various temperature sensor arrangements described herein can be used to measure the temperature of an analyte sensor during storage and / or transport. For example, the peak temperature to which the analyte sensor was exposed prior to a sensor session may affect the sensor's performance. For example, the peak temperature to which the analyte sensor was exposed prior to a sensor session may affect the initial sensitivity and baseline of the analyte sensor when inserted into the host's skin. Similarly, in some instances, if the peak temperature to which the analyte sensor was exposed is too high, the sensor may no longer be suitable for use.

[0600] In various examples, analyte sensor systems, such as Figure 1 The analyte sensor system 8 is configured to use one or more of the various sensor arrangements described herein to periodically record the temperature at the analyte sensor system during storage and / or transport. Figure 29 29 is a flowchart illustration of an exemplary method 2900 for recording the temperature at an analyte sensor system during transport. For example, the analyte sensor system (e.g., its sensor electronics 12) can be programmed to perform the method 2900 when the analyte sensor system is packaged for storage and / or transport.

[0601] Method 2900 may include waking up the analyte sensor system at 2902. For example, as described herein, the processor of the analyte sensor system can be programmed to wake up periodically. After waking up, the analyte sensor system can measure the current temperature at 2904. The analyte sensor system can include any temperature sensor arrangement described herein, and can use one or more temperature sensors to be arranged to measure the temperature at 2904. The analyte sensor system can record the measured temperature at 2906. The measured temperature can be recorded at another suitable data storage location at a data storage memory (e.g., 220 in FIG. 2 ) or at the analyte sensor system. At 2908, the analyte sensor system waits for a period. A period can be, for example, 10 minutes, one hour, one day, etc. After waiting for a period, the analyte sensor system returns to 2902 and wakes up again as described above.

[0602] When the analyte sensor system is stored and / or shipped to a host for use, it can perform method 2900. In this manner, the analyte sensor system can arrive at the host to begin a sensor session with a record of periodic temperature measurements stored in the data storage memory.

[0603] Figure 303000 is a flowchart illustrating an example method for starting a sensor session with an analyte sensor session, which includes a record of periodic temperature measurements from the transport and / or storage of an analyte sensor system. At 3002, the analyte sensor system begins a sensor session. For example, this may occur when an analyte sensor of the analyte sensor system is located at a host, such as when the analyte sensor is inserted into the host's skin. At 3004, the analyte sensor system determines the peak temperature to which the analyte sensor system was exposed prior to the sensor session. This may include, for example, reading the record of periodic temperature measurements and determining a highest temperature measurement from the record. The highest temperature measurement may be the peak temperature measurement.

[0604] At 3006, the analyte sensor system determines whether the peak temperature measurement is greater than a threshold. The threshold can be, for example, the highest temperature to which the analyte sensor can be exposed before the sensor session without compromising sensor performance during the session. If the peak temperature is greater than the threshold, the analyte sensor system can interrupt the sensor session at 3010. For example, this can include sending a message to one or more display devices, such as display devices 14, 16, 18, and / or 20, indicating that the analyte sensor or sensor system is not suitable for use and that a different sensor or analyte sensor system should be used.

[0605] If the peak temperature is not higher than the threshold value at 3006, the analyte sensor system can select an initial sensor period parameter based on the peak temperature. The initial sensor period parameter can be or include, for example, initial sensitivity, initial baseline, etc. The initial sensor period parameter can be used by the analyte sensor system, for example, for generating an analyte concentration value using raw sensor data. In some instances, the initial sensor period parameter is used after the sensor trial (break-in) period. In some instances, the analyte sensor applies the trained model to the peak temperature to determine one or more initial sensor period parameters. In another instance, the relationship between the peak temperature and the initial sensor period parameter is stored at the analyte sensor system, such as in a lookup table. In some instances, as a supplement or alternative to the peak temperature, the analyte sensor system can determine an average temperature, a median temperature, etc., or other suitable indications of temperature during packaging.

[0606] In some examples, methods 2900 and / or 3000 may include humidity considerations in addition to or in lieu of temperature. For example, referring to method 2900 , the analyte sensor system may measure humidity upon awakening. Humidity may be measured in any suitable manner. For example, U.S. patent application Ser. No. 62 / 786,166, filed on December 28, 2018, describes a system and method for measuring humidity at an analyte sensor based on the sensor's membrane impedance. This patent application, attorney docket number 638PRV, is entitled "ANALYTE SENSOR WITH IMPEDANCE DETERMINATION," which is incorporated herein by reference in its entirety. Referring to method 3000, if the analyte sensor is exposed to a humidity outside of a determined range, the sensor session can be interrupted. Furthermore, the peak humidity to which the analyte sensor is exposed can be used to determine an initial sensor session parameter.

[0607] In some instances, a diode can be used as a temperature sensor. For example, by exploiting the temperature dependence of the voltage drop across a diode, a diode can be used as a temperature sensor. Consider the Shockley diode equation given in Equation 2 below:

[0608]

[0609] In Equation 2, V T This is given by Equation 3 below:

[0610]

[0611] In Equation 2 and Equation 3, I is the forward current through the diode. Is is the reverse bias saturation current. V D is the voltage across the diode. V T is the thermal voltage, given by Equation 3. n is the ideality factor of the diode, and k is the Boltzmann constant. q is the elementary electron charge. T is the absolute temperature of the diode in Kelvin. Rearranging Equations 2 and 3 for voltage yields the following Equation 4:

[0612]

[0613] To remove the unknown reverse bias saturation current, two known diode currents can be supplied to the diode, such as ΔV D There is a voltage difference at two different known currents as shown and given by Equation 5:

[0614]

[0615] Solving for temperature yields:

[0616]

[0617] In some examples, the dependence of the temperature T on the ideality factor n of the diode can be reduced by using a diode-connected NPN transistor with the base connected to the collector (e.g., "diode-connected") as the diode. In this arrangement, the ideality factor n is close to unity and can be removed from Equation 6.

[0618] Various examples measure temperature at an analyte sensor system using a diode utilizing the relationship of Equation 6. For example, the NPN transistor and associated circuitry described herein can be less expensive, and in some examples, much less expensive, than a suitably accurate temperature sensor.

[0619] Figure 31 FIG. 3 is a diagram of an exemplary circuit arrangement 3100 that may be implemented at an analyte sensor system to measure temperature using a diode. The circuit arrangement 3100 includes first and second current sources 3102, 3104 and a diode-connected NPN transistor 3106. Figure 31 A diode-connected transistor 3106 is shown in FIG, but in other examples, a different type of diode may be used.

[0620] Current source 3102 is a constant current source that provides approximately 10uA of current in this example. Current source 3104 is a pulsed current source that provides 40uA pulses. Current sources 3102 and 3104 can be implemented in any suitable manner, for example, using one or more transistors. The current from current source 3102 and current source 3104 is provided to diode-connected transistor 3106, so that the current at diode-connected transistor 3106 is the sum of the current from current source 3102 and the current from current source 3104. This is illustrated by graph 3108. In this example, when pulsed current source 3104 is turned on, the current provided to diode-connected transistor 3106 is 50uA, which is the sum of the constant current 10uA from current source 3102 and the pulsed current 40uA from current source 3104. When pulsed current source 3104 is turned off, the current provided to diode-connected transistor 3106 is the 10uA provided by constant current path 3102. In this example, current sources 3102 , 3104 provide current via resistors 3110 , 3112 .

[0621] In this arrangement, diode-connected transistor 3106 receives two known currents, as shown in graph 3108. As demonstrated herein, the difference between the value of the voltage drop across diode-connected transistor 3106 at the first current (V1) and the value of the voltage drop across diode-connected transistor 3106 at the second current (V2) indicates the temperature of the pn junction at diode-connected transistor 3106.

[0622] To measure the voltage difference, a sample-and-hold (S / H) circuit 3116 receives a voltage value at its input that indicates the voltage drop across the diode-connected transistor 3106. At its clock input, the S / H circuit 3116 receives an indication of when the pulsed current source 3104 is turned off. This can be accomplished, for example, by inverting the signal generated by the pulsed current source 3104 using an inverter 3118. Thus, the output of the S / H circuit 3116 can be a voltage value, V1, that indicates the voltage drop across the diode-connected transistor 3106 under the current provided by the first current source 3102.

[0623] A dual-slope integrated analog-to-digital converter (ADC) 3114 can be used to convert the difference between the voltage value V1 and the voltage value V2 into a digital signal that can be consumed, for example, by a processor of the analyte sensor system. The dual-slope integrated ADC 3114 includes a first input 3120 and a second input 3122. The comparator 3124 has a non-inverting input connected to ground and an inverting input coupled to a switch 3128. The switch 3128 alternately connects the first input 3120 (via a resistor RA) or the second input 3122 (via a resistor RB) to the inverting input. A capacitor 3126 is coupled between the inverting input of the comparator 3124 and the output VOUT of the ADC 3114.

[0624] exist Figure 31 In the example of FIG, the output of the S / H circuit 3116, representing V1, is provided to an input 3122 of the ADC 3114. The voltage drop across the diode-connected transistor 3106 is provided at an input 3120. The switch 3128 is timed to provide the input 3120 to the inverting input of the comparator 3124 when the pulsed current source 3104 is on, and to provide the input 3122 to the inverting input when the current source 3104 is off.

[0625] Therefore, when current source 3104 is disconnected, capacitor 3126 is charged to voltage V1, which is the voltage drop across diode-connected transistor 3106 from current source 3102. When current source 3104 is turned on, switch 3128 connects input 3120 to the inverting input, thereby charging capacitor 3126 to voltage V2, which is the voltage drop across diode-connected transistor 3106 due to the combined current of current sources 3102 and 3104. When current source 3104 is disconnected again, switch 3128 connects voltage V1, and the voltage at capacitor 3126 (and VOUT) decays to V1. This is illustrated by graph 3130, which shows VOUT on the vertical axis and time on the horizontal axis. When VOUT increases, current source 3104 is turned on, and switch 3128 connects V2 to the inverting input. When VOUT decays, current source 3104 is disconnected, and switch 3128 connects V1 to the inverting input. The time required for VOUT to decay from V2 to V1 indicates the difference between V2 and V1. This can be used to derive the temperature at the diode-connected transistor 3106, for example, according to Equation 6 above.

[0626] In some examples, the circuit arrangement 3100 includes a comparator 3132 that compares the output of the S / H circuit 3116, which indicates a voltage value V1, with the VOUT output of the ADC 3114. When VOUT is equal to or less than V1, the output of the comparator (COMP OUT) can change state. Thus, the sensor electronics of the analyte sensor system can measure the difference between V1 and V2 by starting a digital counter when the switch 3128 is connected to the input 3122 and stopping the digital counter when the comparator output COMP OUT changes state.

[0627] In some embodiments, an AND gate 3134 is provided to generate a logical AND of the comparator output (COMP OUT) and the clock signal. The output of AND gate 3134 can be used to stop the digital counter. This ensures that the comparator state changes only when the voltage on capacitor 3126 decays.

[0628] Figure 32 is to use diodes such as Figure 31 3200 for measuring the temperature at an analyte sensor system using a diode-connected transistor 3106. The method 3200 may include, at 3202, applying a first current to the diode. The method 3200 may also include, at 3204, measuring a voltage V1 indicating a voltage drop across the diode at the first current. The method 3200 may also include applying a second current to the diode (3206) and measuring a second voltage V2 indicating a voltage drop across the diode at the second current (3208).

[0629] At 3210, a first voltage V1 and a second voltage V2 are provided to a dual slope integrated ADC. At 3212, the time it takes for the output of the ADC to decay from the second voltage V2 to the first voltage V1 is measured, for example, using a digital timer. As described herein, the result can be a digital value indicating the temperature at the diode.

[0630] As described herein, one way in which temperature can affect the performance of an analyte sensor system, such as a glucose sensor system, is related to temperature-dependent compartmental bias. A glucose sensor is inserted into an insertion site within the host's skin. Under the host's skin, the glucose sensor directly measures the glucose concentration at the insertion site, for example, the glucose concentration of the interstitial fluid present at the insertion site. However, the glucose concentration in the interstitial fluid may differ from the glucose concentration in the patient's blood. Compartmental bias represents the difference between the glucose concentration at the glucose sensor insertion site and the host's blood glucose concentration.

[0631] Compartmental bias can be caused by glucose consumption by host cells. For example, glucose from the host bloodstream is supplied to host cells in the capillaries of the host vasculature. Glucose diffuses from the capillaries into the host cells. Cells between the nearest capillaries or the capillary system and the insertion site consume glucose. Due to this consumption, the glucose concentration at the insertion site, also known as the interstitial glucose concentration, is lower than the blood glucose concentration, also known as the blood glucose concentration or capillary glucose concentration. The amount by which the interstitial glucose concentration is lower than the blood glucose concentration is compartmental bias.

[0632] In some examples, the rate at which glucose diffuses from the host capillaries to the insertion site and / or the rate at which glucose is consumed by cells between the host capillaries and the insertion site varies with temperature. For example, glucose may diffuse faster when the host skin is warmer. Therefore, the glucose sensor system can employ a compartmental model to compensate for the glucose sensor signal, where compartmental bias may depend on temperature. An exemplary compartmental model is given in Equation 7:

[0633]

[0634] In Equation 7, IG(t) is the interstitial glucose concentration. is the first derivative of the interstitial glucose concentration IG(t) over time. BG(t) is the blood glucose. The values ​​τ1 and τ2 are model time parameters. Equation 7 is a differential equation that can be solved to derive the model relationship between interstitial glucose concentration IG and blood glucose concentration BG, as given by Equation 8:

[0635]

[0636] The time parameters τ1 and τ2 can be temperature-dependent. For example, the glucose sensor system can model the time parameters τ1 and τ2 as functions of temperature. When the glucose sensor system receives a glucose sensor signal and a temperature sensor signal, the glucose sensor system can derive the time parameters τ1 and τ2 using the temperature sensor signal and then use the time parameters in the compartment model, such as those given in Equations 7 and 8, to find the compensated glucose concentration.

[0637] In some examples, the glucose sensor system utilizes a compartmental model that includes a single time parameter τ for both the interstitial glucose concentration term IG and the blood glucose concentration term BG. In some examples, the difference between the time parameters τ1 and τ2 of the compartmental models of Equations 7 and 8 is related to glucose consumption by cells between the host capillary and the insertion site. Therefore, in some examples, a single time parameter τ can be used by accounting for glucose consumption. Equation 9 provides an example compartmental model that accounts for glucose consumption:

[0638]

[0639] In Equation 9, C(t) is the consumption term.

[0640] In some examples, the consumption term C(t) can be modeled as given in Equation 10:

[0641]

[0642] In Equation 10, V max is the maximum consumption rate of the host cell. K m The consumption rate is V max The glucose concentration at half the i ] is the cell layer glucose concentration of the ith cell layer between the host capillary and the insertion site. As shown in Equation 10, the consumption is obtained by summing the number of cells per unit volume (e.g., per dL), n.

[0643] Figure 33 An exemplary sensor insertion site 3300 is illustrated, showing the cell layers between the sensor insertion site 3300 and the host capillary site. In this example, five cell layers are shown, with i = 0-4. Cells in layer 0, such as cell 3302, have a cell layer glucose concentration of S0. Cells in layer 1, such as exemplary cell 3304, have a cell layer glucose concentration of S1. Cells in layer 2, such as exemplary cell 3306, have a cell layer glucose concentration of S2. Cells in layer 3, such as exemplary cell 3308, have a cell layer glucose concentration of S3. Cells in layer 4, such as exemplary cell 3310, have a cell layer glucose concentration of S4.

[0644] In some examples, a glucose sensor system can apply Equations 9 and 10, assuming that the cell layer glucose concentration [si] is constant regardless of the distance from the sensor insertion site. For example, in some examples, the cell layer glucose concentration [si] of all cells is assumed to be the average of the interstitial glucose concentration IG and the blood glucose concentration. Under this assumption, Equations 9 and 10 can be approximated as given by Equation 11:

[0645]

[0646] Solving Equation 11 for blood glucose concentration yields a model that can be used to generate a compensated glucose concentration. For example, a glucose sensor system can use a temperature sensor signal to determine a value for τ and then apply τ to the solution to Equation 11 to generate a compensated glucose concentration.

[0647] In other examples, the gluco...

Claims

1. A method for determining a temperature-compensated glucose concentration level, the method comprising: receiving a temperature signal indicative of a temperature parameter of an external component; receiving a glucose signal indicative of a glucose concentration level in the body; and determining a compensated glucose concentration level based on the glucose signal, the temperature signal, and a delay parameter; wherein the temperature parameter is detected at a first time and the glucose concentration level is detected at a second time after the first time, wherein the delay parameter comprises a delay period between the first time and the second time, the delay period accounting for a delay between a first temperature change of the external component and a second temperature change of an adjacent glucose sensor. The method according to claim 1 , wherein the temperature parameter is temperature, temperature change or temperature offset. 3 . The method of claim 1 , further comprising adjusting the delay period based on a rate of temperature change.

4. The method of any one or any combination of claims 1-3, further comprising adjusting the delay period based on the detected condition. The method of claim 4 , wherein the detected condition comprises a sudden change in temperature. The method of claim 4 , wherein the detected condition comprises motion.

7. The method of any one or any combination of claims 1-6, wherein receiving the glucose signal comprises receiving the glucose signal from a wearable glucose sensor.

8. The method of claim 7, wherein detecting a temperature signal comprises measuring a temperature parameter of a component of the wearable glucose sensor.

9. The method of claim 7 or 8, wherein determining the compensated glucose concentration level comprises executing instructions on a processor to receive the glucose signal and the temperature signal, and determining the compensated glucose concentration level using the glucose signal, the temperature signal, and the delay parameter.

10. The method according to any one or any combination of claims 7 to 9, wherein the method comprises: A value corresponding to the temperature parameter is stored in a memory circuit, and the stored value is retrieved from the memory circuit for use in determining the compensated glucose concentration level.

11. The method of any one or any combination of claims 1-10, further comprising delivering therapy based at least in part on the compensated glucose concentration level.

12. A temperature-compensated glucose sensor system, the system comprising: a glucose sensor circuit configured to generate a glucose signal representative of a glucose concentration level; a temperature sensor circuit configured to generate a temperature signal indicative of a temperature parameter; and a processor configured to determine a compensated glucose concentration level based on the glucose signal, the temperature signal, and a delay parameter; wherein the temperature parameter is detected at a first time and the glucose concentration level is detected at a second time after the first time, wherein the delay parameter comprises a delay period between the first time and the second time, the delay period accounting for a delay between a first temperature change of the external component and a second temperature change of an adjacent glucose sensor.

13. The temperature compensated glucose sensor system of claim 12, wherein the temperature parameter is temperature, temperature change, or temperature offset.

14. The temperature compensated glucose sensor system of claim 12 or 13, wherein the delay parameter comprises a delay period that is responsible for a delay between a first temperature change of the temperature sensor circuit and a second temperature change of the glucose sensor circuit.

15. The temperature compensated glucose sensor system of claim 14, wherein the processor adjusts the delay period based on a rate of temperature change determined using the temperature parameter.

16. The temperature compensated glucose sensor system of claim 14 or 15, wherein the processor adjusts the delay period based on a detected condition or a measured state.

17. The temperature compensated glucose sensor system of any one or any combination of claims 12-16, wherein the processor executes instructions to receive the glucose signal and the temperature signal and apply the delay parameter to determine the compensated glucose concentration level.

18. The temperature-compensated glucose sensor system of any one or any combination of claims 12-17, further comprising a memory circuit, wherein the system stores a value corresponding to the temperature parameter in the memory circuit, and the processor subsequently retrieves the stored value from the memory for determining the compensated glucose concentration level.

19. The temperature-compensated glucose sensor system of any one or any combination of claims 12-18, wherein the glucose sensor circuit comprises an electrode operably coupled to an electronic circuit configured to generate the glucose signal and a membrane over at least a portion of the electrode, the membrane comprising an enzyme configured to catalyze a reaction of glucose and oxygen from a biological fluid in vivo in contact with the membrane.

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