Systems, devices, and methods for compensating for the effects of temperature on sensors.

Abstract

This report focuses on systems and methods for generating estimated analyte values, such as glucose levels. [Solution] The analyte sensor system can access a first sensor signal from an in vivo analyte sensor and a first temperature signal from an extra vivo temperature sensor. The analyte sensor system can generate a first analyte sensor temperature based at least partially on the first temperature signal and generate a first estimated analyte value based at least partially on the first sensor signal and a first temperature-compensated sensitivity. Further examples involve using a non-analyte background signal.

Description

[Technical Field]

[0001] Claim of priority This application claims priority to U.S. Patent Application No. 17 / 545,711, filed on 8 December 2021, which is incorporated herein by reference in its entirety.

[0002] This development generally relates to medical devices such as analyte sensors, and more specifically, to systems, devices, and methods for compensating for the effects of temperature on analyte sensors, though not limited to such devices. [Background technology]

[0003] Diabetes is a metabolic disorder related to the production or use of insulin in the body. Insulin is a hormone that allows the body to use glucose as energy or to store glucose as fat.

[0004] When a person eats a meal containing carbohydrates, the food is processed in the digestive system, producing glucose in the blood. Blood glucose can be used as energy or stored as fat. The body normally maintains blood glucose levels within a range that provides enough energy to support bodily functions and avoids problems that can arise from glucose levels being too high or too low. Regulating blood glucose levels depends on the production and use of insulin, which regulates the movement of blood glucose into cells.

[0005] Blood glucose levels can rise above the normal range when the body does not produce enough insulin or when the body cannot effectively use the insulin that is present. A condition or state in which blood glucose levels are higher than normal is called "hyperglycemia." Chronic hyperglycemia can lead to many health problems, including cardiovascular disease, cataracts and other eye diseases, neuropathy, and kidney damage. Hyperglycemia can also lead to acute problems such as diabetic ketoacidosis (a condition or state in which the body becomes excessively acidic due to the presence of glucose in the blood and ketone bodies produced when the body is unable to use glucose). A condition or state in which blood glucose levels are lower than normal is called "hypoglycemia." Severe hypoglycemia can lead to an acute crisis that can result in seizures or death.

[0006] Diabetic patients can receive insulin to manage their blood glucose levels. Insulin can be administered, for example, through manual injection using a needle. Wearable insulin pumps are also available. Diet and exercise also affect blood glucose levels. Glucose sensors provide estimated glucose levels and can be used as guidance for patients and caregivers.

[0007] Diabetes is sometimes referred to as "type 1" and "type 2." Typically, people with type 1 diabetes can use insulin when it is present, but they cannot produce enough insulin in their bodies due to problems with the insulin-producing beta cells in the pancreas. People with type 2 diabetes can produce some insulin, but due to reduced insulin sensitivity, they are "insulin resistant." As a result, even when insulin is present in the body, it is not used sufficiently and does not effectively regulate blood glucose levels.

[0008] This "Background Technology" is provided to introduce a brief background to the subsequent "Summary of the Invention" and "Modes for Carrying Out the Invention." This "Background Technology" is not intended to help determine the scope of the claimed subject matter, nor should it be considered to limit the claimed subject matter to an implementation that solves any or all of the defects or problems presented above. [Overview of the project]

[0009] This specification particularly discusses systems, devices, and methods for determining subcutaneous temperature or for compensating for the effects of temperature on analyte sensors such as glucose sensors.

[0010] An example of the subject (e.g., a system) (e.g., "Example 1") may include determining a temperature-compensated glucose concentration level by receiving a temperature signal indicating the temperature parameters of an external component, receiving a glucose signal indicating the in vivo glucose concentration level, and determining the compensated glucose concentration level based on the glucose signal, the temperature signal, and a delay parameter.

[0011] In Example 2, the subject of Example 1 may be configured such that the temperature parameter is optionally temperature, temperature change, or temperature offset.

[0012] In Example 3, the subject of either Example 1 or 2 may be optionally configured to be detected in a second time, the temperature parameter being detected in a first time, and the glucose concentration level being detected in a second time, the second time after the first time, the delay parameter being configured to include a delay period between the first time and the second time, taking into account the delay between the first temperature change in the external components and the second temperature change near the glucose sensor.

[0013] In Example 4, one or more of the themes from Examples 1 to 3 may optionally include adjusting the delay period based on the rate of temperature change.

[0014] In Example 5, one or more of the themes from Examples 1 to 4 may optionally include adjusting the delay period based on the detected conditions or states.

[0015] In Example 6, one or more of the themes from Examples 1 to 5 may be optionally configured such that the detected conditions or states include a sudden change in temperature.

[0016] In Example 7, the subject of Example 5 or 6 may be optionally configured such that the detected conditions or states include motion.

[0017] In Example 8, one or more of the themes from Examples 1 to 7 may be optionally configured to include detecting a glucose signal by receiving a glucose signal from a wearable glucose sensor.

[0018] In Example 9, the subject of Example 8 may optionally be configured to include detecting a temperature signal, which in turn includes measuring the temperature parameters of the components of a wearable glucose sensor.

[0019] In Example 10, the subject of Example 8 or 9 may optionally be configured such that determining the compensated glucose concentration level includes executing instructions on a processor to receive a glucose signal and a temperature signal, and determining the compensated glucose concentration level using the glucose signal, the temperature signal, and a delay parameter.

[0020] In Example 11, one or more of the themes from Examples 8 to 10 may optionally include storing a value corresponding to a temperature parameter in a memory circuit and retrieving the stored value from the memory circuit for use in determining the compensated glucose concentration level.

[0021] In Example 12, one or more subjects from Examples 1 to 11 may optionally include delivering therapy based at least partially on a compensated glucose concentration level.

[0022] An example of the subject (e.g., a system) (e.g., "Example 13") 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 indicating 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.

[0023] In Example 14, the subject of Example 13 may be configured such that the temperature parameter is temperature, temperature change, or temperature offset.

[0024] In Example 15, the subject of Example 13 or 14 may be configured such that the delay parameter includes a delay period that takes into account the delay between the first temperature change in the temperature sensor circuit and the second temperature change in the glucose sensor circuit.

[0025] In Example 16, the subject of Example 15 is configured such that the processor adjusts the delay period based on the rate of temperature change determined using a temperature parameter.

[0026] In Example 17, the subject of Example 15 or 16 may be configured so that the processor adjusts the delay period based on the detected condition or determined state.

[0027] In Example 18, the subject of any one or any combination of Examples 13-17 may be configured such that the processor receives a glucose signal and a temperature signal and executes an instruction to apply a delay parameter in order to determine a compensated glucose concentration level.

[0028] In Example 19, the subject matter of any one or any combination of Examples 13 to 19 may further include a memory circuit, which may be configured so that the system stores values ​​corresponding to temperature parameters in the memory circuit, and the processor retrieves the stored values ​​from the memory for use when determining the compensated glucose concentration level.

[0029] In Example 20, the subject of any one or any combination of Examples 13 to 19 is a glucose sensor circuit which comprises an electrode operably coupled to an electronic circuit configured to generate a glucose signal, and a membrane covering at least a portion of the electrode, wherein the membrane may contain an enzyme configured to catalyze the reaction of glucose and oxygen from a biological fluid that comes into contact with the membrane in a living organism.

[0030] An example (Example 21) of a subject (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 evaluated temperature parameter signal, and determining a temperature-compensated glucose concentration level based on the evaluated temperature parameter signal and the glucose sensor signal.

[0031] In Example 22, the subject of Example 21 may be configured such that receiving a third sensor signal includes receiving a heart rate signal.

[0032] In Example 23, the subject of Example 21 or 22 may be configured such that receiving a third signal includes receiving a blood pressure signal.

[0033] In Example 24, the subject of any one or any combination of Examples 21-23 may be configured such that receiving a third signal includes receiving an activity signal.

[0034] In Example 25, the subject of any one or any combination of Examples 21 to 24 may be configured such that receiving a third sensor signal includes receiving a position signal.

[0035] In Example 26, the subject of any one or any combination of Examples 21-25 may be configured such that evaluating the temperature parameter signal includes determining the presence of a location having known temperature characteristics.

[0036] In Example 27, the subject of any one or any combination of Examples 21 to 26 may be configured such that the method includes determining the presence of a location having known ambient temperature characteristics.

[0037] In Example 28, the subject matter of any one or any combination of Examples 21 to 27 may be configured such that the method includes determining the presence of a location having a flooded environment.

[0038] In Example 29, the subject of Example 28 may be configured such that the immersion environment is a swimming pool or a beach.

[0039] In Example 30, the subject of any one or any combination of Examples 21 to 29 may be configured such that receiving a third sensor signal includes receiving temperature information from an ambient temperature sensor.

[0040] In Example 31, the subject of any one or any combination of Examples 21 to 30 may be configured such that receiving a third sensor signal includes receiving information from a wearable device.

[0041] In Example 32, the subject of Example 31 may be configured such that receiving a third sensor signal includes receiving information from a wristwatch.

[0042] In Example 33, the subject of any one or any combination of Examples 21-32 may be configured such that receiving a third sensor signal includes receiving temperature information from a physiological temperature sensor. In some examples, the subject may include a wristwatch or other wearable device that includes a temperature sensor.

[0043] In Example 34, the subject of any one or any combination of Examples 21 to 33 may be configured such that receiving a temperature parameter signal includes receiving a signal indicating temperature, temperature change, or temperature offset.

[0044] In Example 35, the subject of any one or any combination of Examples 21 to 34 may be configured such that receiving a third signal includes receiving an accelerometer signal.

[0045] In Example 36, the subject of any one or any combination of Examples 21-35 may further include detecting motion using a third signal.

[0046] In Example 37, the subject of any one or any combination of Examples 21 to 36 may be configured to include evaluating a temperature parameter signal and determining whether the change in the temperature parameter signal is consistent with the exercise session.

[0047] In Example 38, the subject of any one or any combination of Examples 21 to 37 may be configured to include evaluating a temperature parameter signal and determining that the temperature parameter signal is consistent with the occurrence of an increase in body temperature due to exercise.

[0048] In Example 39, the subject of any one or any combination of Examples 21-38 may be configured such that determining a temperature-compensated glucose concentration level includes applying a temperature parameter signal to a motion model.

[0049] In Example 40, the subject matter of any one or any combination of Examples 21 to 39 may be configured such that the method includes applying a motion model when motion is detected and a change in the temperature parameter signal indicates a decrease in temperature (for example, suggesting motion in a cold or convectively cooled environment).

[0050] In Example 41, the subject of any one or any combination of Examples 21 to 40 may be configured such that the third signal includes a heart rate signal, a respiration signal, a blood pressure signal, or an activity signal, and exercise is detected from an increase in the heart rate signal, respiration signal, blood pressure signal, or activity signal.

[0051] A glucose sensor of an example subject (e.g., a system, device, or method) configured to generate a first signal representing glucose concentration in a host, wherein the sensor includes a temperature sensor configured to generate a second signal representing 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 evaluation of the first and second signals.

[0052] In Example 43, the subject of Example 42 may be configured such that the processor evaluates the second signal by confirming the detected temperature or temperature change using the third signal.

[0053] In Example 44, the subject of Example 42 or 43 may be configured such that the processor determines a condition or state based on a third signal and confirms the temperature or temperature change detected based on the condition or state.

[0054] In Example 45, the subject of any one or any combination of Examples 42 to 44 may be configured such that the condition or state is a location, surrounding environment, activity conditions or state, or physiological conditions.

[0055] In Example 46, the subject of any one or any combination of Examples 42-45 may be configured such that the processor suspends temperature compensation at least in part based on a third signal.

[0056] In Example 47, the subject of any one or any combination of Examples 42-46 may be configured such that the processor detects motion based at least partially on a third signal.

[0057] In Example 48, the subject of Example 47 may be configured such that, in response to detecting motion, the processor temporarily suspends temperature compensation despite the temperature drop indicated by the second signal, and the processor may be configured to avoid inaccurate temperature compensation when the host is moving in a low-temperature environment (e.g., cold outdoors or convectively cooled).

[0058] In Example 49, the subject of any one or any combination of Examples 42 to 48 may be configured such that the processor specifies a temperature compensation model based at least in part on a third signal.

[0059] In Example 50, the subject of any one or any combination of Examples 42 to 49 may further include a third sensor, the third sensor generating a third signal.

[0060] In Example 51, the subject matter of any one or any combination of Examples 42 to 50 may be configured such that the third signal includes location information, and the processor evaluates the second signal at least in part on the location information.

[0061] In Example 52, the subject matter of any one or any combination of Examples 42 to 51 may 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.

[0062] In Example 53, the subject of any one or any combination of Examples 42 to 52 may be configured such that a temperature-compensated glucose sensor system includes a wearable continuous glucose monitor comprising a glucose sensor and a temperature sensor.

[0063] In Example 54, the subject of any one or any combination of Examples 42 to 53 may be configured such that a temperature-compensated glucose sensor system includes an activity sensor, and a third signal includes activity information from the activity sensor.

[0064] In Example 55, the subject of any one or any combination of Examples 42-54 may be configured such that the third signal includes the host's heart rate, respiratory rate, or blood pressure.

[0065] In Example 56, the subject of any one or any combination of Examples 42-55 may be configured so that the processor detects exercise based on changes in heart rate, respiratory rate, or blood pressure.

[0066] In Example 57, the subject of any one or any combination of Examples 42 to 56 may be configured such that the processor confirms an elevated body temperature indicated by a second signal, at least in part, based on motion detection.

[0067] In Example 58, the subject of any one or any combination of Examples 42 to 56 may be configured such that the processor reduces, gradually reduces, limits, or pauses temperature compensation in response to motion detection.

[0068] In Example 59, the subject of any one or any combination of Examples 42 to 58 may be configured such that the third signal includes a signal from an optical sensor configured to detect host blood parameters.

[0069] In Example 60, the subject of Example 59 may further include an optical sensor, the optical sensor comprising a light source and a photodetector configured to detect blood flow velocity or red blood cell count in a host region beneath the optical sensor.

[0070] An example of the subject (e.g., a system, device, or method) ("Example 61") may include determining a pattern from temperature data, receiving a glucose signal from a continuous glucose sensor, the glucose signal indicating a glucose concentration level, and temperature compensating a continuous glucose sensor by determining a temperature-compensated glucose concentration level based at least in part on the sensor glucose signal and pattern.

[0071] In Example 62, the subject of Example 61 may be configured such that determining the pattern includes determining the pattern of temperature fluctuations, and the method includes compensating for glucose concentration levels according to the pattern.

[0072] In Example 63, the subject of Example 61 or 62 may further include receiving a temperature parameter, comparing the temperature parameter with a pattern, and determining a temperature-compensated glucose concentration level based at least in part on the comparison.

[0073] In Example 64, the subject of Example 63 may be configured such that the pattern includes a temperature pattern correlated with a physiological cycle.

[0074] In Example 65, the subject of Example 63 or 64 may be configured to include determining whether the reliability of the temperature parameter is high based on comparison with a pattern, and, when it is determined that the reliability of the temperature parameter is high, temperature-compensating the glucose concentration level using the temperature parameter.

[0075] In Example 66, the subject matter of any one or any combination of Examples 63-65 may be configured such that the method includes determining the degree of compensation based at least in part on a comparison of a temperature parameter with a pattern. For example, the degree of compensation may be based on a defined range or confidence interval.

[0076] In Example 67, the subject of any one or any combination of Examples 61-66 may be configured such that determining a pattern includes determining a condition or state, and determining a temperature-compensated glucose concentration level is at least partially based on the determined condition or state.

[0077] In Example 68, the subject of Example 67 may be configured such that determining a condition or state includes applying a temperature parameter to a state model.

[0078] In Example 69, the subject of Example 67 or 68 may be configured such that determining a condition or state involves applying one or more of the following to a state model: glucose concentration level, carbohydrate sensitivity, time, activity, heart rate, respiratory rate, posture, insulin delivery amount, meal time, or meal amount.

[0079] In Example 70, the subject of any one or any combination of Examples 67-69 may be configured such that determining a condition or state includes determining a motion condition or state, and the method includes adjusting a temperature-compensated model based on the motion condition or state.

[0080] An example of the subject (e.g., a system, device, or method) ("Example 71") 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 indicating a temperature parameter, and a processor that receives the glucose signal and the temperature signal and determines a temperature-compensated glucose concentration level at least in part based on a pattern determined from the glucose signal and the temperature signal.

[0081] In Example 72, the subject of Example 71 may be configured such that the processor determines a temperature parameter based on a temperature signal, compares the temperature parameter with a pattern, and determines a temperature-compensated glucose concentration level based at least partially on the comparison.

[0082] In Example 73, the subject of Example 71 or 72 may be configured such that the processor determines whether the reliability of the temperature parameter is high based on a comparison with a pattern, and if it is determined that the reliability of the temperature parameter is high, it uses the temperature parameter to temperature-compensate for the glucose concentration level.

[0083] In Example 74, the subject of Example 72 or 73 may be configured such that the processor determines the degree of compensation based at least in part on a comparison of temperature parameters and patterns.

[0084] In Example 75, the subject of any one or any combination of Examples 71-74 may be configured such that the pattern includes a state model, and the processor determines a temperature-compensated glucose concentration level at least in part based on applying a temperature parameter to the state model.

[0085] In Example 76, the subject of Example 75 may be configured so that the processor determines the temperature-compensated glucose concentration level by additionally applying one or more of the following to the state model: glucose concentration level, carbohydrate sensitivity, time, activity, heart rate, respiratory rate, posture, insulin delivery amount, meal time, or meal amount.

[0086] In Example 77, the subject of Example 75 or 76 may be configured such that the processor determines the motion conditions or state and adjusts the temperature compensation model at least partially based on the motion conditions or state.

[0087] In Example 78, the subject of any one or any combination of Examples 71-77 may further include a memory circuit containing executable instructions that determine a pattern from a temperature signal and determine a temperature-compensated glucose concentration level based on the pattern, and the processor is configured to retrieve the instructions from memory and execute the instructions.

[0088] In Example 79, the subject matter of any one or any combination of Examples 71-78 may be configured such that the processor receives pattern information from a remote system via a communication circuit.

[0089] In Example 80, the subject of Example 79 may be configured such that the remote system receives temperature parameter information based on a temperature signal and determines a pattern from the temperature parameter information.

[0090] An example of a subject (e.g., a method, system, or device) ("Example 81") may include determining a first value from a first signal indicating a temperature parameter of a component of a continuous glucose sensor system, receiving a glucose sensor signal indicating a glucose concentration level, comparing the first value with a reference value, and determining a temperature-compensated glucose level based on the glucose sensor signal and the comparison between the first signal and the reference value.

[0091] In Example 82, the subject of Example 81 may be configured such that the method includes determining the temperature difference from a reference condition or state based on the variation of a first value from a reference value, without calibrating the temperature with respect to a reference value.

[0092] In Example 83, the subject of Example 81 or 82 may further include determining a reference value from the first signal.

[0093] In Example 84, the subject of Example 83 may be configured such that the continuous glucose sensor system includes a glucose sensor that can be inserted into a host, and a reference value is determined within a specified period after insertion of the glucose sensor into the host.

[0094] In Example 85, the subject of Example 83 or 84 is that the continuous glucose sensor system may include a glucose sensor that can be inserted into a host, and the reference value may be determined within a specified period after the glucose sensor has been activated.

[0095] In Example 86, the subject of any one or any combination of Examples 83 to 85 may be configured such that the reference value is determined during the manufacturing process.

[0096] In Example 87, the subject matter of any one or any combination of Examples 83 to 86 may be configured such that the method includes determining a reference value during a first period and determining a first value during a second period, the second period occurring after the first period. The reference value may be, for example, a long-term average, and the first value may be a short-term average.

[0097] In Example 88, the subject of Example 87 may further include updating the reference value based on one or more temperature signal values ​​obtained in a third period following a second period.

[0098] In Example 89, the subject of any one or any combination of Examples 83 to 88 may be configured such that determining a reference value includes determining the average of a plurality of sample values ​​obtained from a first signal.

[0099] In Example 90, the subject of any one or any combination of Examples 81 to 89 may be configured such that the temperature-compensated glucose level is determined at least in part on a temperature-dependent sensitivity value that varies based on a deviation of a first value from a reference value.

[0100] An example of the subject (e.g., a system, device, or method) ("Example 91") includes 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 indicating a temperature parameter, and a processor, which can determine a temperature-compensated glucose level based on the glucose signal and the deviation of the first signal from a reference value.

[0101] In Example 92, the subject of Example 91 is configured such that the processor determines the deviation of the first signal from a reference value without determining the temperature corresponding to the reference value.

[0102] In Example 93, the subject of Example 91 or 92 may be configured such that the processor determines a reference value based on the first signal.

[0103] In Example 94, the subject of Example 93 is configured such that the processor determines a reference value based on a plurality of sample values ​​obtained from a first signal during a first period.

[0104] In Example 95, the subject of Example 93 or 94 may be configured such that the processor determines a reference value based on a plurality of sample values ​​obtained from the first signal during a specified period after the activation or insertion of the glucose sensor.

[0105] In Example 96, the subject of any one or any combination of Examples 93 to 95 may be configured such that the processor iteratively updates a reference value.

[0106] In Example 97, the subject of any one or any combination of Examples 91 to 96 may be configured such that the processor determines the reference value as the average of multiple sample values ​​obtained from the first signal during a specified period.

[0107] In Example 98, the subject of any one or any combination of Examples 91 to 97 may be configured such that the processor determines a temperature-compensated glucose level based on a glucose signal and a temperature-dependent sensitivity value that varies based on a deviation from a reference value.

[0108] In Example 99, the subject of any one or any combination of Examples 91 to 98 may be configured such that the processor determines a temperature-compensated glucose concentration level based on a model, and the model may be configured such that a glucose sensor value determined from a glucose signal and a sample value based on a first signal are applied to the model.

[0109] In Example 100, the subject of any one or any combination of Examples 91 to 100 may further include a memory circuit and executable instructions stored on the memory circuit for determining a temperature-compensated glucose concentration level based on a glucose signal and a deviation of a first signal from a reference value.

[0110] An example of the subject (e.g., a method, system, or device) (Example 101) may include receiving a glucose signal indicating a glucose concentration level, receiving a temperature signal indicating a temperature parameter, detecting a condition or state, and determining a temperature-compensated glucose concentration level based at least in part on the glucose signal, the temperature signal, and the detected condition or state.

[0111] In Example 102, the subject of Example 101 may be configured such that the conditions or state include a high rate of change in the glucose signal, and temperature compensation is reduced or suspended during the period when the glucose signal is receiving a high rate of change.

[0112] In Example 103, the subject of Example 101 or 102 may be configured such that the conditions or state include a sudden change in the temperature signal.

[0113] In Example 104, the subject of Example 103 may be configured such that temperature compensation is reduced or paused in response to the detection of a sudden change in temperature.

[0114] In Example 105, the subject of Example 103 or 104 may be configured such that determining the temperature-compensated glucose concentration level includes using a previous temperature signal value instead of a temperature signal value associated with a sudden change in temperature.

[0115] In Example 106, the subject of any one or any combination of Examples 103 to 105 may be configured such that determining a temperature-compensated glucose concentration level includes determining an extrapolated temperature signal value based on a previous temperature signal value, and using the extrapolated temperature signal value instead of the temperature signal value associated with a sudden change in temperature.

[0116] In Example 107, the subject of Example 106 may be configured so that a delay model is invoked in response to the detection of a sudden change in temperature, and the delay model specifies a delay period to be used when determining the temperature-compensated glucose level.

[0117] In Example 108, the subject of any one or any combination of Examples 101 to 107 may be configured such that the condition or state is the presence of radiant heat on a continuous glucose monitoring system.

[0118] In Example 109, the subject of any one or any combination of Examples 101-108 may be configured such that the condition or state is thermal, and temperature compensation is reduced or suspended in response to the detection of thermal.

[0119] In Example 110, the subject of Example 109 may be configured such that the conditions or states include motion.

[0120] In Example 111, the subject of Example 110 may be configured such that the method includes reducing, gradually decreasing, limiting, or pausing temperature compensation when motion is detected.

[0121] In Example 112, the subject of any one or any combination of Examples 101 to 111 may be configured such that the method includes using a linear model to determine temperature-compensated glucose concentration levels.

[0122] In Example 113, the subject of Example 112 may further include receiving a blood glucose calibration value, and the temperature compensation gain and offset are updated when the blood glucose calibration value is received.

[0123] In Example 114, the subject of any one or any combination of Examples 101-113 may be configured such that the method includes using a time-series model to determine temperature-compensated glucose concentration levels.

[0124] In Example 115, the subject matter of any one or any combination of Examples 101 to 114 may be configured such that the method includes using partial differential equations to determine temperature-compensated glucose concentration levels.

[0125] In Example 116, the subject matter of any one or any combination of Examples 101 to 115 may be configured such that the method includes using a probabilistic model to determine temperature-compensated glucose concentration levels.

[0126] In Example 117, the subject matter of any one or any combination of Examples 101 to 116 may be configured such that the method includes using a state model to determine a temperature-compensated glucose concentration level.

[0127] In Example 118, the subject of any one or any combination of Examples 101 to 117 may be configured such that the conditions or states include a body mass index (BMI) value.

[0128] In Example 119, the subject matter of any one or any combination of Examples 101-118 may be configured such that the method includes determining a long-term average using a temperature signal, and the temperature-compensated glucose concentration level is determined using the long-term average.

[0129] In Example 120, the subject of any one or any combination of Examples 101 to 119 may be configured such that a glucose signal indicating a condition or state is received from a continuous glucose sensor, and the condition or state is pressure on the continuous glucose sensor.

[0130] In Example 121, the subject of Example 120 may be configured such that compression is detected at least in part based on a rapid decrease in the glucose signal.

[0131] In Example 122, the subject of Example 120 or 121 may be configured such that the condition or state is compression during sleep.

[0132] In Example 123, the subject of any one or any combination of Examples 101 to 122 may be configured such that the condition or state is sleep.

[0133] In Example 124, in the subject matter of Example 123, sleep is detected using one or more of the following: temperature, posture, activity, and heart rate, and the method includes applying a specified glucose alert trigger based on the detected sleep.

[0134] In Example 125, the subject of any one or any combination of Examples 101-124 may further include delivering insulin therapy, the therapy being determined at least in part on temperature-compensated glucose levels.

[0135] An example of the subject matter (e.g., a system, device, or method) ("Example 126") 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 indicating a temperature parameter, and a processor configured to determine a compensated glucose concentration level based on the glucose signal, the temperature signal, and the detected condition or state.

[0136] In Example 127, the subject of Example 126 is that the conditions or state include a high rate of change in the glucose signal, and the processor may be configured to reduce, pause, gradually decrease, or limit temperature compensation during periods of high rate of change in the glucose signal.

[0137] In Example 128, the subject of Example 126 or 127 may be configured such that the conditions or state include a sudden change in the temperature signal, and the processor may be configured to reduce, pause, gradually decrease, or limit the temperature compensation in response to the detection of a sudden change in temperature.

[0138] In Example 129, the subject of any one or any combination of Examples 126-128 may be configured such that the conditions or states include motion, and when motion is detected, the processor may be configured to reduce, gradually decrease, limit, or pause temperature compensation.

[0139] In Example 130, the subject of any one or any combination of Examples 126 to 129 may further include a second temperature sensor circuit configured to detect radiant heat on a continuous glucose monitoring system, wherein the detected condition or state includes radiant heat detected by the second temperature sensor circuit.

[0140] An example of the subject (e.g., a device, system, or method) ("Example 131") may include an elongated portion having a distal end configured for intracellular 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.

[0141] In Example 132, the subject of Example 131 may be configured such that the temperature sensor includes a thermistor.

[0142] In Example 133, the subject of Example 131 or 132 may be configured such that the temperature sensor includes a temperature-variable resistor coating.

[0143] In Example 134, the subject of any one or any combination of Examples 131 to 133 may be configured such that the temperature sensor includes a thermocouple.

[0144] In Example 135, the subject of Example 134 may 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 joined to the first wire to form a thermocouple.

[0145] In Example 136, the subject of Example 135 may be configured such that the first wire is tantalum or a tantalum alloy, and the second wire is platinum or a platinum alloy.

[0146] In Example 137, the subject of Example 135 or 136 may further include a transmitter coupled to a glucose sensor, wherein a first electrical contact on the transmitter is coupled to a first wire, and a second electrical contact on the transmitter is coupled to a second wire.

[0147] An example of a subject (e.g., a method, system, or device) ("Example 138") may include receiving a calibration value of a temperature signal, receiving a temperature signal from a temperature sensor indicating a temperature parameter, receiving a glucose signal from a continuous glucose sensor indicating a glucose concentration level, and determining a temperature-compensated glucose concentration level based at least in part on the glucose signal, the temperature signal, and the calibration value.

[0148] In Example 139, the subject of Example 138 may be configured such that receiving a calibration value for a temperature signal includes obtaining the calibration during a manufacturing step having a known temperature.

[0149] In Example 140, the subject of Example 138 or 139 may be configured such that receiving a calibration value for the temperature signal includes acquiring the temperature during a specified period after insertion of a continuous glucose sensor into the host.

[0150] An example of the subject (e.g., a method, system, or device) ("Example 141") may include receiving a temperature signal indicating the temperature of components of a continuous glucose sensor on a host, and determining the anatomical location of the continuous glucose sensor on the host, at least in part, based on the received temperature signal.

[0151] In Example 142, the subject of Example 141 may be configured such that the anatomical location is determined at least partially based on the perceived temperature.

[0152] In Example 143, the subject of Example 141 or 142 may be configured such that the anatomical location is determined at least in part on the variability of the temperature signal.

[0153] An example of the subject (e.g., a method, system, or device) ("Example 144") may include receiving a temperature signal indicating a temperature parameter from a temperature sensor on a continuous glucose monitor, and determining from the temperature signal that the continuous glucose monitor has been restarted.

[0154] In Example 145, the subject of Example 144 may be configured such that determining from the temperature signal that the continuous glucose monitor has been restarted includes comparing a first temperature signal value before the sensor started with a second temperature signal value after the sensor started, and declaring that the continuous glucose monitor has been restarted when the comparison meets similar conditions.

[0155] In Example 146, the subject of Example 144 or 145 may be configured such that the similar conditions are within a temperature range.

[0156] An example of the subject (e.g., a system, device, or method) ("Example 147") 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 indicating a temperature parameter; a thermal deflector configured to deflect heat from the temperature sensor circuit; and a processor configured to determine a compensated glucose concentration level based at least in part on the glucose signal and the temperature signal.

[0157] An example of the subject (e.g., a system, device, or method) ("Example 148") may include: a glucose sensor circuit configured to generate a glucose signal representing a host glucose concentration level; a first temperature sensor circuit configured to generate a first temperature signal indicating a first temperature parameter in proximity to the host; a second temperature sensor circuit configured to generate a second temperature signal indicating a second temperature parameter; and a processor 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.

[0158] In Example 149, the subject of Example 148 is configured such that the processor determines a compensated glucose concentration level based in part on the temperature gradient between the first temperature sensor circuit and the second temperature sensor circuit.

[0159] In Example 150, the subject of Example 148 or 149 may be configured such that the processor determines the compensated glucose concentration level based in part on an estimate of the heat flux between the first temperature sensor circuit and the second temperature sensor circuit.

[0160] In Example 151, the subject of any one or any combination of Examples 148 to 150 may be configured such that the second temperature circuit generates a temperature signal indicating the ambient temperature.

[0161] In Example 152, the subject of any one or any combination of Examples 148 to 151 may be configured such that the processor generates a temperature signal indicating the temperature of a transmitter coupled to a glucose sensor circuit.

[0162] An example of the subject matter (e.g., a method, device, or system) ("Example 153") may include receiving a glucose signal from a glucose sensor representing the host glucose concentration level, receiving a first temperature signal indicating a first temperature parameter adjacent to the host or glucose sensor, receiving a second temperature signal indicating 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.

[0163] In Example 154, the subject of Example 153 may be configured such that a first temperature signal is received from a first temperature sensor coupled to a glucose sensor, and a second temperature signal is received from a second temperature sensor coupled to a glucose sensor.

[0164] In Example 155, the subject of Example 154 may be configured such that the compensated glucose concentration level is determined at least in part on the temperature gradient between the first temperature sensor and the second temperature sensor.

[0165] In Example 156, the subject of Example 154 or 155 may be configured such that the compensated glucose concentration level is determined at least in part on the heat flux between the first temperature sensor and the second temperature sensor.

[0166] In Example 157, the subject of any one or any combination of Examples 154 to 156 may further include detecting an increase in a first temperature signal and a decrease in a second temperature signal, and adjusting a temperature compensation model based on the detected increase and decrease.

[0167] In Example 158, the subject of Example 157 may be configured such that the method includes detecting motion (e.g., outdoor motion or convective cooling motion) at least in part based on detected rises and falls, and adjusting or applying a temperature compensation model based on the detection of motion.

[0168] In Example 159, the subject of any one or any combination of Examples 154 to 158 may further include determining, at least in part, that a temperature change is due to radiant heat or ambient heat, and adjusting or applying a temperature compensation model based on that determination.

[0169] An example of a subject (e.g., a method, system, or device) ("Example 160") 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 regarding the glucose concentration level, the model compensating for multiple temperature-dependent effects on the glucose sensor signal.

[0170] In Example 161, the subject of Example 161 may be configured such that the output is a compensated glucose concentration level.

[0171] In Example 162, the subject of Example 161 may further include delivering the therapy based on a compensated glucose concentration value.

[0172] In Example 163, the subject of Example 161 is that the model may be configured to compensate for two or more of the following: sensor sensitivity, local glucose level, compartment bias, and non-enzymatic bias. In some examples, the model may compensate for three or more of the following: sensor sensitivity, local glucose level, compartment bias, and non-enzymatic bias. In some examples, the model may consider an additional temperature-dependent factor in addition to sensor sensitivity, local glucose level, compartment bias, and non-enzymatic bias.

[0173] An example of the subject (e.g., a method, system, or device) (Example 164) may include determining an estimated analyte level by determining a first value indicating the conductance of a sensor component; determining a second value indicating the conductance of the sensor component; receiving a signal representing the estimated analyte level of a host; and determining a compensated estimated analyte level at least in part on a comparison of the second value and the first value. The first and second values ​​may be, for example, electrical conductance, electrical resistance, or electrical impedance.

[0174] In Example 165, the subject of Example 164 may be configured such that determining the first value includes determining the mean conductance.

[0175] In Example 166, the subject of Example 164 or Example 165 may optionally include determining a first estimated subcutaneous temperature correlated in time with a first value, and determining a second estimated subcutaneous temperature correlated in time with a second value, wherein the second estimated subcutaneous temperature is determined at least in part based on a comparison between the second value and the first value.

[0176] In Example 167, the subject of Example 166 may optionally include determining a third estimated subcutaneous temperature that is time-correlated with a second value, determining whether a condition is met based on a comparison between 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 met.

[0177] In Example 168, the subject of Example 167 may optionally include triggering a reset, which includes determining a subsequent estimated subcutaneous temperature based on a third estimated temperature and a second value, or based on a third conductance value and a fourth estimated subcutaneous temperature that is time-correlated with the third conductance value.

[0178] In Example 169, the subject of any one or any combination of Examples 164 to 168 may optionally include compensating for drift in conductance values.

[0179] In Example 170, the subject of any one or any combination of Examples 164-169 may be optionally configured to include applying a filter to compensate for drift.

[0180] An example of the subject (e.g., a method, system, or device) (Example 171) may include determining a first value representing the conductance of a sensor component at a first time; determining a second value representing the conductance 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.

[0181] An example of the subject (e.g., a method, system, or device) (Example 172) may include: 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 for generating compensated temperature values; and determining an estimated analyte value based at least in part on the compensated temperature values.

[0182] In Example 173, the subject of Example 172 may be configured such that the first data includes at least one of uncompensated temperature values ​​from a system temperature sensor or raw temperature sensor data.

[0183] In Example 174, the subject of either Example 172 or 173 may be configured to further include the trained temperature compensation model returning a first temperature sensor parameter in response to first data, and generating a compensated temperature value based at least in part on the first temperature sensor parameter.

[0184] In Example 175, one or more of the themes from Examples 172 to 174 may be configured such that the trained temperature compensation model returns a system temperature sensor offset and a system temperature sensor gradient, receives raw sensor data from a system temperature sensor, and generates a compensated temperature value based at least partially on the raw sensor data, the system temperature sensor offset, and the system temperature sensor gradient.

[0185] An example of a subject (e.g., a method, system, or device) (Example 176) may include an analyte sensor, a system temperature sensor, and a control circuit. The control circuit may be configured to perform operations including accessing first data from the system temperature sensor of the analyte sensor system, applying the first data to a trained temperature compensation model, the trained temperature compensation model being for generating compensated temperature values, and determining an estimated analyte value based at least in part on the compensated temperature values.

[0186] In Example 177, the subject of Example 176 may be configured such that the first data includes at least one of either an uncompensated temperature value from a system temperature sensor or raw temperature sensor data.

[0187] In Example 178, the subject of either Example 176 or 177 may be configured to further include the trained temperature compensation model returning a first temperature sensor parameter in response to first data, and generating a compensated temperature value based at least in part on the first temperature sensor parameter.

[0188] In Example 179, one or more of the themes from Examples 176 to 178 may be configured such that the trained temperature compensation model returns a system temperature sensor offset and a system temperature sensor gradient, receives raw sensor data from the system temperature sensor, and generates a compensated temperature value based at least partially on the raw sensor data, the system temperature sensor offset, and the system temperature sensor gradient.

[0189] In Example 180, one or more subjects from Examples 176 to 179 may further include an application-specific integrated circuit (ASIC) equipped with a system temperature sensor.

[0190] An example of a subject (e.g., a method, system, or device) (Example 181) may include determining a temperature-compensated glucose concentration level. Determining may include receiving a glucose sensor signal, receiving a temperature parameter signal, detecting motion conditions or states based at least in part on the glucose sensor signal or the temperature parameter signal, and correcting the temperature compensation applied to the glucose sensor signal.

[0191] In Example 182, the subject of Example 181 may include determining that the noise floor of the glucose sensor signal is greater than a first threshold.

[0192] In Example 183, the subject of either Example 181 or 182 may include determining that the noise floor of the temperature parameter signal is greater than a second threshold.

[0193] In Example 184, one or more of the themes from Examples 181 to 183 may include determining that the noise floor of the glucose sensor signal is greater than a first threshold and determining that the noise floor of the temperature parameter signal is greater than a second threshold.

[0194] In Example 185, one or more subjects from Examples 181 to 184 may be configured such that correcting temperature compensation includes applying a motion model to the temperature parameter signal to generate an evaluated temperature parameter signal, and using the evaluated temperature parameter to generate a temperature-compensated glucose concentration value.

[0195] In Example 186, one or more of the themes from Examples 181 to 185 may be configured such that detecting motion conditions or states includes determining whether the distribution of the rate of change of the temperature parameter signal satisfies a classifier.

[0196] In Example 187, one or more of the themes from Examples 181 to 186 may be configured such that detecting motion conditions or states includes determining that the distribution of the rate of change of the temperature parameter signal is below a threshold.

[0197] An example of the subject (e.g., a method, system, or device) (Example 188) may include a temperature-compensated glucose sensor system comprising: a glucose sensor configured to produce a first signal representing glucose concentration in a host; a temperature sensor configured to produce a second signal representing temperature; and a processor. The processor may be programmed to perform operations including detecting motion conditions or states based at least in part on the first or second signal, and modifying the temperature compensation applied to the first signal.

[0198] In Example 189, the subject of Example 188 may be configured to further include determining that the noise floor of the first signal is greater than a first threshold.

[0199] In Example 190, the subject of either Example 188 or 189 may be configured to further include determining that the noise floor of the second signal is greater than a second threshold.

[0200] In Example 191, one or more of the themes from Examples 188 to 190 may be configured such that the operation further includes determining that the noise floor of a first signal is greater than a first threshold, and determining that the noise floor of a second signal is greater than a second threshold.

[0201] In Example 192, one or more subjects from Examples 188 to 191 may be configured such that correcting temperature compensation includes applying a motion model to a second signal to generate an evaluated second signal, and using the evaluated second signal to generate a temperature-compensated glucose concentration value.

[0202] In Example 193, one or more of the themes from Examples 188 to 192 may be configured such that detecting a motion condition or state includes determining whether the distribution of the rate of change of a second signal satisfies a classifier.

[0203] In Example 194, one or more of the themes from Examples 188 to 193 may be configured such that detecting a motion condition or state includes determining that the distribution of the rate of change of a second signal is below a threshold.

[0204] An example of a subject (e.g., a method, system, or device) ("Example 195") may include a processor implementation method for measuring temperature in an analyte sensor system. The method may include, during a first sensor session, accessing a record of periodic temperatures stored in the analyte sensor system, determining a peak temperature from the record of periodic temperatures, and performing a response action based on the peak temperature.

[0205] In Example 196, the subject of Example 195 may include determining that the peak temperature exceeds a peak temperature threshold, and the response action includes terminating the first sensor session.

[0206] In Example 197, the subject of either Example 195 or 196 may include determining initial sensor session parameters based at least in part on peak temperature, receiving raw sensor data from the analyte sensor of the analyte sensor system, and generating estimated analyte values ​​using the initial session parameters and the raw sensor data.

[0207] In Example 198, one or more of the themes from Examples 195 to 197 may be configured such that the initial sensor session parameters include sensitivity or baseline.

[0208] In Example 199, one or more of the themes from Examples 195 to 198 may include, before a first sensor session, measuring a first temperature in the analyte sensor system, writing the first temperature to a periodic temperature record, waiting for one period, and measuring a second temperature in the analyte sensor system.

[0209] One example of the subject matter ("Example 200") may include a temperature-compensated analyte sensor system. The temperature-compensated analyte sensor system may comprise: an analyte sensor configured to generate a first signal representing an estimated analyte value in a host; a temperature sensor configured to generate a second signal representing temperature; and a processor. The processor may be programmed to perform operations during a first sensor session that include accessing a record of periodic temperatures stored in the analyte sensor system, determining a peak temperature from the record of periodic temperatures, and performing a response action based on the peak temperature.

[0210] In Example 201, the subject of Example 200 may be configured such that the operation further includes determining that the peak temperature exceeds a peak temperature threshold, and the response action includes terminating the first sensor session.

[0211] In Example 202, the subject of either Example 200 or 201 may be configured such that the operation further includes determining an initial sensor session parameter based at least in part on peak temperature, receiving raw sensor data from the analyte sensor of the analyte sensor system, and generating an estimated analyte value using the initial session parameter and the raw sensor data.

[0212] In Example 203, one or more subjects from Examples 200 to 202 may be configured such that the initial sensor session parameters include sensitivity or baseline.

[0213] In Example 204, one or more subjects from Examples 200 to 203 may be configured such that the operation further includes measuring a first temperature in the analyte sensor system before a first sensor session, writing the first temperature to a periodic temperature record, waiting for one period, and measuring a second temperature in the analyte sensor system.

[0214] An example of a subject (e.g., a method, system, or device) ("Example 205") may include a temperature-sensing analyte sensor system. The temperature-sensing analyte sensor system may comprise a diode, an electronic circuit, a sample-and-hold circuit, and a dual-slope integral analog-to-digital converter (ADC). The electronic circuit may be configured to perform an operation including applying a first current to the diode over a first period such that the voltage drop across the diode has a first voltage value when the first current is applied to the diode, and applying a second current to the diode over a second period after the first period such that the voltage drop across the diode has a second voltage value when the second current is applied to the diode. The sample-and-hold circuit may be configured to receive a first voltage value when the first voltage is applied to the diode and to produce an output indicating the first voltage. A dual-slope integrating analog-to-digital converter (ADC) may comprise a first input coupled to receive a first voltage value from the output of a sample-and-hold circuit, and a second input coupled to receive the voltage drop across a diode. The time it takes for the output of the dual-slope integrating ADC to decay from the first voltage value to the second voltage value may be proportional to the temperature in the diode.

[0215] In Example 206, the subject of Example 205 may further include a comparator coupled to compare the output of the sample-and-hold circuit with the output of the dual-slope integral analog-digital circuit.

[0216] In Example 207, the subject of either Example 205 or 206 may further include a digital counter. The operation may further include activating the digital counter at the peak of the output of the dual-slope integrating ADC and determining the value of the digital counter in response to the change in the output of the comparator.

[0217] In Example 208, one or more of the themes from Examples 205 to 207 may be configured such that the value of a digital counter indicating the time it takes for the output of a dual-slope integrating ADC to decay from a first voltage value to a second voltage value is proportional to the temperature in the diode.

[0218] In Example 209, one or more subjects from Examples 205 to 208 may further include an AND circuit configured to generate a logical AND between the output of a comparator and a clock signal, wherein the clock signal is low when a first current is applied to the diode.

[0219] In Example 210, one or more of the subjects from Examples 205 to 209 may be configured such that the diode includes a diode-connected transistor.

[0220] In Example 211, one or more subjects from Examples 205 to 210 may be configured such that the analyte sensor of the analyte sensor is inserted into the host skin and the diode is positioned in close proximity to the host skin.

[0221] In Example 212, one or more of the subjects from Examples 205 to 211 may further comprise a first constant current source supplying a first current and a second pulsed current source, the second current comprising the sum of the first current and the current supplied by the second pulsed current source when the second pulsed current source is turned on.

[0222] An example of the subject (e.g., a method, system, or device) ("Example 213") may include: applying a first current to a diode over a first period such that the voltage drop across the diode has a first voltage value when the first current is supplied to the diode; applying a second current to the diode after the first period, different from the first current, such that the voltage drop across the diode has a second voltage value when the second current is supplied to the diode; and supplying the first and second voltage values ​​to a dual-slope integral analog-to-digital converter (ADC) such that the time for the output of the dual-slope integral ADC to decay from the first voltage value to the second voltage value is proportional to the temperature in the diode.

[0223] In Example 214, the subject of Example 213 may include comparing the output of a sample-and-hold circuit with the output of a dual-slope integral analog-to-digital circuit in order to generate a comparator output.

[0224] In Example 215, the subject of either Example 213 or 214 may include activating a digital counter at the peak of the output of a dual-slope integrating ADC and determining the value of the digital counter in response to the change in the comparator output.

[0225] In Example 216, one or more of the themes from Examples 213 to 215 may be configured such that the value of a digital counter indicating the time it takes for the output of a dual-slope integral ADC to decay from a first voltage value to a second voltage value is proportional to the temperature in the diode.

[0226] In Example 217, one or more subjects from Examples 213 to 216 may include an AND circuit configured to generate a logical AND between the output of a comparator and a clock signal. The clock signal may be low when a first current is applied to the diode.

[0227] In Example 218, one or more of the subjects from Examples 213 to 217 may be configured such that the diode includes a diode-connected transistor.

[0228] In Example 219, one or more subjects from Examples 213 to 218 may be configured such that the analyte sensor of the analyte sensor is inserted into the host skin and the diode is positioned in close proximity to the host skin.

[0229] An example of a subject (e.g., a method, system, or device) ("Example 220") may include a method for determining glucose concentration levels. The method may include receiving a temperature sensor signal, receiving a glucose sensor signal from a glucose sensor inserted at an insertion site in a host, and applying the temperature sensor signal and the glucose sensor signal to a model describing the difference between the glucose concentration at the insertion site and the blood glucose concentration in the host in order to generate a compensated blood glucose concentration for the host.

[0230] In Example 221, the subject of Example 220 may include determining a model time parameter based at least partially on a temperature sensor signal, and determining a compensated blood glucose concentration based at least partially on the model time parameter.

[0231] In Example 222, the subject of either Example 220 or 221 may be configured such that the model time parameters are applied to glucose concentration at the insertion site and blood glucose concentration.

[0232] In Example 223, one or more subjects from Examples 220-222 may further include determining glucose consumption that describes the host. The compensated blood glucose concentration may be based at least in part on glucose consumption.

[0233] In Example 224, one or more of the themes from Examples 220 to 223 may further include determining glucose consumption using a constant cell layer glucose concentration.

[0234] In Example 225, one or more of the themes from Examples 220 to 224 may further include determining glucose consumption using variable cell layer glucose concentration.

[0235] In Example 226, one or more of the themes from Examples 220 to 225 may include determining glucose consumption using linearly fluctuating cell layer glucose concentrations.

[0236] An example of a subject (e.g., a method, system, or device) ("Example 227") may include a temperature-compensated glucose sensor system. The temperature-compensated glucose sensor system may comprise a glucose sensor and sensor electronics. The sensor electronics may be configured to perform operations including receiving a temperature sensor signal, receiving a glucose sensor signal from a glucose sensor inserted at an insertion site in a host, and applying the temperature sensor signal and the glucose sensor signal to a model describing the difference between the glucose concentration at the insertion site and the blood glucose concentration in the host in order to generate a compensated blood glucose concentration for the host.

[0237] In Example 228, the subject of Example 227 is configured such that the operation further includes determining a model time parameter based at least in part on a temperature sensor signal, and determining a compensated blood glucose concentration based at least in part on the model time parameter.

[0238] In Example 229, the subject of either Example 227 or 228 may be configured such that the model time parameters are applied to glucose concentration at the insertion site and blood glucose concentration.

[0239] In Example 230, one or more subjects from Examples 227-229 may be configured such that the operation further includes determining glucose consumption that describes the host, and the compensated blood glucose concentration is at least partially based on glucose consumption.

[0240] In Example 231, one or more subjects from Examples 227 to 230 may be configured such that the operation further includes determining glucose consumption using a constant cell layer glucose concentration.

[0241] In Example 232, one or more subjects from Examples 227-230 may be configured such that the operation further includes determining glucose consumption using variable cell layer glucose concentration.

[0242] In Example 233, one or more subjects from Examples 227 to 231 may be configured such that the operation further includes determining glucose consumption using linearly fluctuating cell layer glucose concentrations.

[0243] Example 234 is an analyte sensor system for generating an estimated analyte value, comprising an in vivo temperature sensor, an in vivo analyte sensor, and at least one processor, wherein the operation is programmed to include accessing a first sensor signal from the in vivo analyte sensor, accessing a first temperature signal from the in vivo temperature sensor, generating a first analyte sensor temperature based at least partially on the first temperature signal, determining that the first analyte sensor temperature satisfies a temperature condition, generating a first temperature-compensated sensitivity based at least partially on the first analyte sensor temperature, and generating a first estimated analyte value based at least partially on the first sensor signal and the first temperature-compensated sensitivity.

[0244] In Example 235, the subject of Example 234 optionally includes determining that the temperature of the first analyte sensor satisfies the temperature conditions, which includes determining that the temperature of the first analyte sensor is within a first temperature range.

[0245] In Example 236, the subject of either Example 234 or 235 optionally includes determining that the first analyte sensor temperature satisfies a temperature condition by generating a rate of temperature change using the first analyte sensor temperature and at least one previous analyte sensor temperature indicated by a previous temperature signal, and determining that the rate of temperature change satisfies a rate of change condition.

[0246] In Example 237, one or more subjects from Examples 234 to 236 optionally include an operation further comprising generating a first extracorporeal temperature using a first temperature signal and generating a first analyte sensor temperature using the first extracorporeal temperature.

[0247] In Example 238, the subject of Example 237 optionally includes determining that the first extracorporeal temperature indicated by the first temperature signal satisfies the temperature condition by determining that the difference between the first extracorporeal temperature and the first analyte sensor temperature is less than a threshold.

[0248] In Example 239, one or more subjects from Examples 234 to 238 optionally include an operation that further includes determining that the temperature of a second analyte sensor, indicated by a second temperature signal from an in vitro temperature sensor, does not meet the temperature conditions, and performing a response action in response to determining that the temperature of the second analyte sensor, indicated by the second temperature signal, does not meet the temperature conditions.

[0249] In Example 240, the subject matter of Example 239 optionally includes a response action that includes generating a second temperature-compensated sensitivity based at least in part on a default analyte sensor temperature, and generating a second estimated analyte value based at least in part on the second analyte sensor signal and the second temperature-compensated sensitivity.

[0250] In Example 241, the subject matter of either Example 239 or 240 optionally includes a response action that includes pausing the display of an analyte value on a display associated with the analyte sensor.

[0251] In Example 242, the subject matter of any one or more of Examples 239 - 241 optionally includes a response action that includes generating a second estimated analyte value based at least in part on the second analyte sensor signal and an uncompensated sensitivity.

[0252] In Example 243, the subject matter of any one or more of Examples 234 - 242 further includes an operation that includes receiving a second sensor signal from an in vivo analyte sensor, generating a second temperature-compensated sensitivity based at least in part on a first analyte sensor temperature, and generating a second estimated analyte value based at least in part on the second sensor signal and the second temperature-compensated sensitivity.

[0253] In Example 244, the subject matter of any one or more of Examples 234 - 243 further includes an operation that includes determining that a first temperature signal meets a temperature signal condition before generating a first temperature-compensated sensitivity.

[0254] In Example 245, the subject matter of Example 244 optionally includes that determining that a first temperature signal meets a temperature signal condition is performed by an ex vivo temperature sensor.

[0255] Example 246 is a method for generating an estimated analyte value, comprising: accessing a first sensor signal from an in vivo analyte sensor; accessing a first temperature signal from an in vivo temperature sensor; generating a first analyte sensor temperature based at least in part on the first temperature signal; determining that the first analyte sensor temperature satisfies a temperature condition; generating a first temperature-compensated sensitivity based at least in part on the first analyte sensor temperature; and generating a first estimated analyte value based at least in part on the first sensor signal and the first temperature-compensated sensitivity.

[0256] In Example 247, the subject of Example 246 optionally includes determining that the temperature of the first analyte sensor satisfies the temperature conditions, which includes determining that the temperature of the first analyte sensor is within a first temperature range.

[0257] In Example 248, the subject of either Example 246 or 247 optionally includes determining that the first analyte sensor temperature satisfies a temperature condition by generating a rate of temperature change using the first analyte sensor temperature and at least one previous analyte sensor temperature indicated by a previous temperature signal, and determining that the rate of temperature change satisfies a rate of change condition.

[0258] In Example 249, one or more subjects from Examples 246 to 248 optionally include generating a first in vitro temperature using a first temperature signal and generating a first analyte sensor temperature using the first in vitro temperature.

[0259] In Example 250, the subject of Example 249 optionally includes determining that the first extracorporeal temperature indicated by the first temperature signal satisfies the temperature condition by determining that the difference between the first extracorporeal temperature and the first analyte sensor temperature is less than a threshold.

[0260] In Example 251, one or more of the themes from Examples 246 to 250 optionally include determining that the temperature of a second analyte sensor, indicated by a second temperature signal, does not meet the temperature conditions, and performing a response action in response to the determination that the temperature of a second analyte sensor, indicated by a second temperature signal, does not meet the temperature conditions.

[0261] In Example 252, the subject of Example 251 optionally includes a response action comprising generating a second temperature-compensated sensitivity based at least in part on a default analyte sensor temperature, and generating a second estimated analyte value based at least in part on a second analyte sensor signal and the second temperature-compensated sensitivity.

[0262] In Example 253, the subject of either Example 251 or 252 optionally includes a response action which includes pausing the display of the analyte value on a display associated with the analyte sensor.

[0263] In Example 254, one or more subjects from Examples 251 to 253 optionally include a response action that includes generating a second estimated analyte value based at least in part on a second analyte sensor signal and an uncompensated temperature sensitivity.

[0264] In Example 255, one or more subjects from Examples 246 to 254 optionally include receiving a second sensor signal from an in vivo analyte sensor, generating a second temperature-compensated sensitivity at least partially based on the first analyte sensor temperature, and generating a second estimated analyte value at least partially based on the second sensor signal and the second temperature-compensated sensitivity.

[0265] In Example 256, one or more themes from Examples 246 to 255 optionally include determining that a first temperature signal satisfies a temperature signal condition before generating a first temperature-compensated sensitivity.

[0266] In Example 257, the subject of Example 256 optionally includes determining whether the first temperature signal satisfies the temperature signal conditions by using an in vitro temperature sensor.

[0267] Example 258 is an analyte sensor system for generating an estimated analyte value, comprising an in vitro temperature sensor, an in vitro analyte sensor, and at least one processor, wherein the operation includes accessing a first sensor signal from the in vitro analyte sensor, accessing a first temperature signal from the in vitro temperature sensor, and selecting a first temperature compensation model from a set of temperature compensation models, wherein the selection is at least partially based on the first temperature signal; generating a first analyte sensor temperature at least partially based on the first temperature signal and the first temperature compensation model; generating a first temperature-compensated sensitivity at least partially based on the first analyte sensor temperature; and generating a first estimated analyte value at least partially based on the first sensor signal and the first temperature-compensated sensitivity.

[0268] In Example 259, the subject of Example 258 optionally includes a set of temperature compensation models, each including a piecewise linear model, wherein the first temperature compensation model corresponds to a first segment of the piecewise linear model over a first temperature range, and the second temperature compensation model corresponds to a second segment of the piecewise linear model over a second temperature range different from the first temperature range.

[0269] In Example 260, the subject of either Example 258 or 259 optionally includes an operation that further includes determining the rate of change of temperature of an in vivo analyte sensor using a first temperature signal, wherein the first temperature compensation model corresponds to the rate of change of temperature.

[0270] In Example 261, any one or more of the themes of Examples 258 - 260 further includes an operation of optionally generating a difference between the in vitro temperature indicated by an in vitro temperature sensor and a first analyte sensor temperature, and the first temperature compensation model corresponds to the difference.

[0271] In Example 262, any one or more of the themes of Examples 258 - 261 optionally includes that the first temperature compensation model has a first linear gain, and the set of temperature compensation models also includes a second temperature compensation model having a second linear gain different from the first linear gain.

[0272] In Example 263, any one or more of the themes of Examples 258 - 262 optionally includes that the set of temperature compensation models includes a first temperature compensation model and a second temperature compensation model, and at least one of the first temperature compensation model or the second temperature compensation model is a constant.

[0273] In Example 264, any one or more of the themes of Examples 258 - 263 further includes accessing a second sensor signal from an in vivo analyte sensor, accessing a second temperature signal from an in vitro temperature sensor, generating a second analyte sensor temperature based at least in part on the second temperature signal, determining that the second analyte sensor temperature does not meet a temperature condition, and performing a response action in response to determining that the second analyte sensor temperature does not meet the temperature condition.

[0274] In Example 265, the theme of Example 264 optionally includes a response action that includes generating a second temperature - compensated sensitivity based at least in part on a default analyte sensor temperature, and generating a second estimated analyte value based at least in part on the second sensor signal and the second temperature - compensated sensitivity.

[0275] In Example 266, the subject of either Example 264 or 265 optionally includes a response action which includes pausing the display of the analyte value on a display associated with the analyte sensor.

[0276] In Example 267, one or more subjects from Examples 264 to 266 optionally include a response action that generates a second estimated analyte value based at least in part on a second sensor signal and a temperature-uncompensated sensitivity.

[0277] In Example 268, one or more subjects from Examples 258 to 267 optionally include an operation that further comprises receiving a second sensor signal from an in vivo analyte sensor, generating a second temperature-compensated sensitivity at least partially based on the first analyte sensor temperature, and generating a second estimated analyte value at least partially based on the second sensor signal and the second temperature-compensated sensitivity.

[0278] In Example 269, one or more of the themes from Examples 258 to 268 optionally include an operation that further includes determining whether a first temperature signal satisfies a temperature signal condition before generating a first temperature-compensated sensitivity.

[0279] In Example 270, the subject of Example 269 optionally includes the determination that the first temperature signal satisfies the temperature signal conditions being performed by an in vitro temperature sensor.

[0280] Example 271 is a method for generating an estimated analyte value, comprising: accessing a first sensor signal from an in vivo analyte sensor; accessing a first temperature signal from an in vivo temperature sensor; selecting a first temperature compensation model from a set of temperature compensation models, wherein the selection is at least partially based on the first temperature signal; generating a first analyte sensor temperature at least partially based on the first temperature signal and the first temperature compensation model; generating a first temperature-compensated sensitivity at least partially based on the first analyte sensor temperature; and generating a first estimated analyte value at least partially based on the first sensor signal and the first temperature-compensated sensitivity.

[0281] In Example 272, the subject of Example 271 optionally includes a set of temperature compensation models, each including a piecewise linear model, where the first temperature compensation model corresponds to a first segment of the piecewise linear model over a first temperature range, and the second temperature compensation model corresponds to a second segment of the piecewise linear model over a second temperature range different from the first temperature range.

[0282] In Example 273, the subject of either Example 271 or 272 optionally includes determining the rate of change of temperature of an in vivo analyte sensor using a first temperature signal, and the first temperature compensation model corresponds to the rate of change of temperature.

[0283] In Example 274, one or more of the themes from Examples 271 to 273 optionally include generating a difference between the extracorporeal temperature indicated by an extracorporeal temperature sensor and the temperature of a first analyte sensor, wherein the first temperature compensation model corresponds to the difference.

[0284] In Example 275, any one or more themes from Examples 271 to 274 optionally include a first temperature compensation model having a first linear gain, and a set of temperature compensation models also including a second temperature compensation model having a second linear gain different from the first linear gain.

[0285] In Example 276, any one or more themes from Examples 271 to 275 optionally include a set of temperature compensation models comprising a first temperature compensation model and a second temperature compensation model, wherein at least one of the first or second temperature compensation models is a constant.

[0286] In Example 277, one or more subjects from Examples 271 to 276 optionally include accessing a second sensor signal from an in vivo analyte sensor, accessing a second temperature signal from an in vitro temperature sensor, generating a second analyte sensor temperature based at least partially on the second temperature signal, determining that the second analyte sensor temperature does not meet the temperature conditions, and performing a response action in response to the determination that the second analyte sensor temperature does not meet the temperature conditions.

[0287] In Example 278, the subject of Example 277 optionally includes a response action comprising generating a second temperature-compensated sensitivity based at least in part on a default analyte sensor temperature, and generating a second estimated analyte value based at least in part on a second sensor signal and the second temperature-compensated sensitivity.

[0288] In Example 279, the subject of either Example 277 or 278 optionally includes a response action which includes pausing the display of the analyte value on a display associated with the analyte sensor.

[0289] In Example 280, one or more subjects from Examples 277 to 279 optionally include a response action that generates a second estimated analyte value based at least in part on a second sensor signal and a temperature-uncompensated sensitivity.

[0290] In Example 281, one or more subjects from Examples 271 to 280 optionally include receiving a second sensor signal from an in vivo analyte sensor, generating a second temperature-compensated sensitivity based at least partially on the first analyte sensor temperature, and generating a second estimated analyte value based at least partially on the second sensor signal and the second temperature-compensated sensitivity.

[0291] In Example 282, one or more of the themes from Examples 271 to 281 optionally include determining that a first temperature signal satisfies a temperature signal range before generating a first temperature-compensated sensitivity.

[0292] In Example 283, the subject of Example 282 optionally includes the determination by an in vitro temperature sensor that the first temperature signal satisfies the temperature signal range.

[0293] Example 284 is a glucose sensor system for generating an estimated glucose value, comprising an in vivo temperature sensor, an in vivo analyte sensor, and at least one processor, and programmed to perform operations including: accessing a first sensor signal from the in vivo glucose sensor; accessing a first temperature signal from the in vivo temperature sensor; generating a first glucose sensor temperature at least partially based on the first temperature signal; generating a first temperature-compensated sensitivity at least partially based on the first glucose sensor temperature; generating a first temperature-compensated non-glucose signal at least partially based on the first glucose sensor temperature; and generating a first estimated glucose value at least partially based on the first sensor signal, the first temperature-compensated sensitivity, and the first temperature-compensated non-glucose signal.

[0294] In Example 285, the subject of Example 284 includes, optionally, an operation that further includes accessing a second sensor signal from an in vivo glucose sensor; accessing a second temperature signal from an extra vivo temperature sensor; generating a second glucose sensor temperature based at least in part on the second temperature signal; determining that the second glucose sensor temperature does not meet the temperature conditions; generating a second temperature-compensated non-glucose signal based at least in part on the default temperature; and generating a second estimated glucose value based at least in part on the second sensor signal and the second temperature-compensated non-glucose signal.

[0295] In Example 286, the subject of either Example 284 or 285 optionally includes generating a first temperature-compensated non-glucose signal by determining the difference between a first glucose sensor temperature and a reference glucose sensor temperature, and applying a non-glucose compensation coefficient to the difference.

[0296] In Example 287, one or more of the themes from Examples 284 to 286 optionally include an operation that further includes determining whether the first glucose sensor temperature satisfies a temperature condition before generating the first temperature-compensated sensitivity.

[0297] In Example 288, the subject of Example 287 optionally includes determining that the temperature of the first glucose sensor satisfies the temperature condition, which includes determining that the temperature of the first glucose sensor is within a first temperature range.

[0298] In Example 289, the subject of either Example 287 or 288 optionally includes determining that the first glucose sensor temperature satisfies a temperature condition by generating a rate of temperature change using the first glucose sensor temperature and at least one previous glucose sensor temperature indicated by a previous temperature signal, and determining that the rate of temperature change does not exceed a rate of change condition.

[0299] In Example 290, one or more subjects from Examples 287 to 289 optionally include an operation further comprising generating a first extracorporeal temperature using a first temperature signal and generating a first glucose sensor temperature using the first extracorporeal temperature.

[0300] In Example 291, the subject of Example 290 optionally includes determining that the first extracorporeal temperature indicated by the first temperature signal satisfies the temperature condition, which includes determining that the difference between the first extracorporeal temperature and the first glucose sensor temperature is less than a threshold.

[0301] In Example 292, one or more subjects from Examples 284 to 291 optionally include an operation that further includes accessing a second sensor signal from an in vivo glucose sensor, accessing a second temperature signal from an extra vivo temperature sensor, generating a second glucose sensor temperature based at least partially on the second temperature signal, determining that the second glucose sensor temperature does not meet a temperature condition, and performing a response action in response to determining that the second glucose sensor temperature exceeds a temperature condition.

[0302] In Example 293, the subject of Example 292 optionally includes a response action comprising generating a second temperature-compensated sensitivity based at least in part on a default glucose sensor temperature, and generating a second estimated glucose value based at least in part on a second sensor signal and the second temperature-compensated sensitivity.

[0303] In Example 294, the subject of either Example 292 or 293 optionally includes a response action which includes pausing the display of glucose concentration on a display associated with a glucose sensor.

[0304] In Example 295, one or more subjects from Examples 292 to 294 optionally include a response action that generates a second estimated glucose value based at least in part on a second sensor signal and an uncompensated temperature sensitivity.

[0305] In Example 296, one or more subjects from Examples 284 to 295 optionally include an operation that further comprises receiving a second sensor signal from an in vivo glucose sensor, generating a second temperature-compensated sensitivity at least partially based on the temperature of the first glucose sensor, and generating a second estimated glucose value at least partially based on the second sensor signal and the second temperature-compensated sensitivity.

[0306] In Example 297, one or more subjects from Examples 284 to 296 optionally include an operation that further includes determining whether a first temperature signal satisfies a temperature signal condition before generating a first temperature-compensated sensitivity.

[0307] In Example 298, the subject of Example 297 optionally includes determining whether the first temperature signal satisfies the temperature signal conditions by using an in vitro temperature sensor.

[0308] Example 299 is a method for generating an estimated glucose value, comprising: accessing a first sensor signal from an in vivo glucose sensor; accessing a first temperature signal from an in vivo temperature sensor; generating a first glucose sensor temperature at least partially based on the first temperature signal; generating a first temperature-compensated sensitivity at least partially based on the first glucose sensor temperature; generating a first temperature-compensated non-glucose signal at least partially based on the first glucose sensor temperature; and generating a first estimated glucose value at least partially based on the first sensor signal, the first temperature-compensated sensitivity, and the first temperature-compensated non-glucose signal.

[0309] In Example 300, the subject of Example 299 optionally includes accessing a second sensor signal from an in vivo glucose sensor, accessing a second temperature signal from an extra vivo temperature sensor, generating a second glucose sensor temperature based at least partially on the second temperature signal, determining that the second glucose sensor temperature does not meet the temperature conditions, generating a second temperature-compensated non-glucose signal based at least partially on the default temperature, and generating a second estimated glucose value based at least partially on the second sensor signal and the second temperature-compensated non-glucose signal.

[0310] In Example 301, the subject of either Example 299 or 300 optionally includes generating a first temperature-compensated non-glucose signal by determining the difference between a first glucose sensor temperature and a reference glucose sensor temperature, and applying a non-glucose compensation coefficient to the difference.

[0311] In Example 302, one or more themes from Examples 299 to 301 optionally include determining that the first glucose sensor temperature satisfies a temperature condition before generating a first temperature-compensated sensitivity.

[0312] In Example 303, the subject of Example 302 optionally includes determining that the temperature of the first glucose sensor satisfies the temperature condition, which includes determining that the temperature of the first glucose sensor is within a first temperature range.

[0313] In Example 304, the subject of either Example 302 or 303 optionally includes determining that the first glucose sensor temperature satisfies a temperature condition by generating a rate of temperature change using the first glucose sensor temperature and at least one previous glucose sensor temperature indicated by a previous temperature signal, and determining that the rate of temperature change does not exceed a rate of change condition.

[0314] In Example 305, one or more themes from Examples 302 to 304 optionally include generating a first in vitro temperature using a first temperature signal and generating a first glucose sensor temperature using the first in vitro temperature.

[0315] In Example 306, the subject of Example 305 optionally includes determining that the temperature indicated by the first temperature signal satisfies the temperature condition by determining that the difference between the first in vivo temperature and the first glucose sensor temperature is less than a threshold.

[0316] In Example 307, one or more subjects from Examples 299 to 306 optionally include accessing a second sensor signal from an in vivo glucose sensor, accessing a second temperature signal from an extra vivo temperature sensor, generating a second glucose sensor temperature based at least partially on the second temperature signal, determining that the second glucose sensor temperature does not meet a temperature condition, and performing a response action in response to determining that the second glucose sensor temperature exceeds a temperature condition.

[0317] In Example 308, the subject of Example 307 optionally includes a response action comprising generating a second temperature-compensated sensitivity based at least in part on a default glucose sensor temperature, and generating a second estimated glucose value based at least in part on a second sensor signal and the second temperature-compensated sensitivity.

[0318] In Example 309, the subject of either Example 307 or 308 optionally includes a response action which includes pausing the display of glucose concentration on a display associated with a glucose sensor.

[0319] In Example 310, one or more subjects from Examples 307 to 309 optionally include a response action that generates a second estimated glucose value based at least in part on a second sensor signal and a temperature-uncompensated sensitivity.

[0320] In Example 311, one or more subjects from Examples 299 to 310 optionally include receiving a second sensor signal from an in vivo glucose sensor, generating a second temperature-compensated sensitivity based at least partially on the temperature of the first glucose sensor, and generating a second estimated glucose value based at least partially on the second sensor signal and the second temperature-compensated sensitivity.

[0321] In Example 312, one or more themes from Examples 299 to 311 optionally include determining that a first temperature signal satisfies a temperature signal condition before generating a first temperature-compensated sensitivity.

[0322] In Example 313, the subject of Example 312 optionally includes determining whether the first temperature signal satisfies the temperature signal conditions by an in vitro temperature sensor.

[0323] An example of a subject (e.g., a system or apparatus) (e.g., "Example 314") may be any combination of one or more parts of Examples 1 to 313 or one or more parts of Examples 1 to 313 to include "means for" performing any part of any one or more of the functions or methods of Examples 1 to 313, or "machine-readable media" (e.g., collective machine-readable media, non-temporary machine-readable media, etc.) that, when executed by a machine, causes a machine to perform any one or more parts of the functions or methods of Examples 1 to 313.

[0324] 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 description of this disclosure. The modes for carrying out the invention are included to provide further information about this patent application. Other aspects of this disclosure will be apparent to those skilled in the art from reading and understanding the following modes for carrying out the invention and from looking at the drawings which constitute part thereof, and these drawings should not be interpreted as being restrictive. [Brief explanation of the drawing]

[0325] In drawings that are not necessarily drawn to scale, similar numbers may represent the same components in different drawings. Similar numbers with different letter suffixes may represent different instances of the same components. The drawings are illustrative, not limiting, but illustrate the various embodiments considered in this document. [Figure 1] This is a diagram illustrating an exemplary analyte sensor system that may include a temperature sensor and in which a temperature compensation method may be implemented. [Figure 2A] This is a schematic diagram of an example of an analyte sensor system. [Figure 2B] This is a schematic diagram of an exemplary sensor electronic component of an analyte sensor system. [Figure 2C] This is a schematic diagram of an exemplary analyte sensor system engaged with host tissue. [Figure 2D] This is a schematic diagram of an exemplary analyte sensor system engaged with host tissue. [Figure 3] This is a schematic diagram of the temperature sensor located on the distal portion of the analyte sensor. [Figure 4] This is a schematic diagram of an exemplary temperature sensor on the proximal portion of an analyte sensor. [Figure 5A] This is a schematic diagram of another exemplary temperature sensor on the proximal portion of the analyte sensor. [Figure 5B] Figure 5A is a magnified view of a portion of the temperature sensor shown. [Figure 6] This is a flowchart illustrating an exemplary method for determining temperature-compensated glucose concentration levels using a delay parameter. [Figure 7] This is a flowchart illustrating an exemplary method for determining temperature-compensated glucose concentration levels based on evaluated (e.g., verified) temperature values. [Figure 8] This is a schematic diagram of an exemplary method for temperature compensating a continuous glucose sensor, which includes determining a pattern from temperature information. [Figure 9] This is a flowchart illustrating an exemplary method for temperature-compensating a continuous glucose monitoring system, at least partially based on detected conditions or states. [Figure 10] This is a schematic diagram of a method for temperature compensating a continuous glucose sensor system using a reference temperature value. [Figure 11] This is a flowchart illustrating an exemplary continuous glucose sensor temperature compensation method. [Figure 12] This is a flowchart illustrating an exemplary method of temperature compensation using two temperature sensors. [Figure 13] This is a flowchart illustrating an exemplary method for determining when continuous glucose (or other analyte) monitoring has been restarted. [Figure 14] This is a flowchart illustrating an exemplary method for determining the anatomical location of a sensor. [Figure 15A] This shows the output of the glucose sensor plotted against time. [Figure 15B] This shows the output of the temperature sensor plotted against time. [Figure 15C] This shows the superposition of temperature over glucose sensor output. A clear correlation is observed here. [Figure 15D] This shows the superposition of temperature over glucose sensor output. The correlation is not clear here. [Figure 16] This graph shows temperature versus time plots for sensors on the host's abdomen and on the host's arm. [Figure 17] This is a plot of the standard deviation versus mean temperature over the first 24 hours for several sensor devices. [Figure 18A] This is a temperature versus time plot, where the sensor electronics package was removed from the sensor over a period of 1 minute. [Figure 18B] This is a temperature versus time plot, where the sensor electronics package was removed from the sensor over a period of 5 minutes. [Figure 19] This is a schematic diagram of an exemplary model that can be used to determine an output from two or more inputs that may be received at different points in time. [Figure 20A] This is a flowchart illustrating an exemplary method for determining a compensated glucose concentration value using a model. [Figure 20B] This is a flowchart illustrating another exemplary method for determining compensated glucose concentration values ​​using a model. [Figure 21] This is a graph showing temperature and impedance plotted against time. [Figure 22] This is a flowchart illustrating an exemplary method of temperature compensation using conductance or impedance. [Figure 23] This is a flowchart illustrating an exemplary method for determining estimated subcutaneous temperature using conductance or impedance. [Figure 24] This is a flowchart illustrating an exemplary method for training a temperature compensation model. [Figure 25] This is a flowchart illustrating an exemplary method for utilizing a trained temperature compensation model. [Figure 26] This is a flowchart illustrating an exemplary method for detecting motion conditions or states. [Figure 27] This graph shows a first change distribution function representing a host in a resting state (e.g., not exercising) and a second change distribution function representing a host in an exercised state. [Figure 28] This is a flowchart illustrating an exemplary method for detecting motion conditions or states using the distribution of the rate of change in a temperature parameter signal sample. [Figure 29]This is a flowchart illustrating an exemplary method for recording temperature in an analyte sensor system during shipment. [Figure 30] This is a flowchart illustrating an exemplary method for initiating a sensor session having an analyte sensor session that includes recording periodic temperature measurements obtained from the transport and / or storage of an analyte sensor system. [Figure 31] This is a diagram of an exemplary circuit configuration that may be implemented in an analyte sensor system to measure temperature using a diode. [Figure 32] This is a flowchart illustrating a method for measuring temperature in an analyte sensor system using a diode. [Figure 33] An example of a sensor insertion site is shown, illustrating the cell layer between the sensor insertion site and the host's capillary region. [Figure 34] This figure shows an example of a process flow that may be performed in an analyte sensor system that uses temperature compensation to respond to exceptions. [Figure 35] This is a block diagram illustrating a temperature model. [Figure 36] A chart illustrating an exemplary relationship between extracorporeal temperature TTx and in vivo sensor temperature TWE is shown, indicating the range of in vivo sensor temperature TWE that triggers an exception. [Figure 37] A version of the chart in Figure 36 is shown, which includes regions corresponding to situations where the in vivo temperature TTx exceeds the upper limit and / or falls below the lower limit. [Figure 38] This flowchart shows an example of a process flow that can be performed by an analyte sensor system to determine the in vivo sensor temperature from extra vivo temperature measurements, taking model parameters into consideration. [Figure 39] This flowchart shows an example of detecting an exception based on the determined in vivo sensor temperature (TWE). [Figure 40] A chart illustrating an exemplary relationship between the external temperature TTx and the in vivo sensor temperature TWE is shown. [Figure 41]This flowchart shows an example of a process flow that may be performed by an analyte sensor system to use multiple temperature models across different ranges of the in vivo sensor temperature (TWE). [Figure 42] This flowchart shows another example of process flow 4200, which can be performed by the analyte sensor system to use multiple temperature models across different ranges of the in vivo sensor temperature (TWE). [Figure 43] This flowchart shows an example of a process flow that may be performed by an analyte sensor system, such as a glucose sensor system, to determine a temperature-compensated estimated glucose value. [Figure 44] This flowchart shows an example of a process flow that may be performed by an analyte sensor system to upsample a signal from an in vitro temperature sensor. [Figure 45] This is a schematic diagram of another exemplary analyte sensor system engaged with host tissue. [Figure 46] This is a cross-sectional view of the exemplary configuration of Figure 45 along line AA. [Modes for carrying out the invention]

[0326] The accuracy of a glucose sensor is important to patients, caregivers, and clinicians, as estimated glucose values ​​obtained from a glucose sensor may be used to determine treatment or evaluate treatment effectiveness. Several factors can affect the accuracy of a glucose sensor. One factor is temperature. The inventors recognize that, among other things, measures can be taken to compensate for the effect of temperature on the glucose sensor, thereby improving the performance of the sensor system by improving the accuracy of the estimated glucose level, and thereby reducing the mean absolute relative deviation (MARD) of the sensor system. The MARD value over the effective or indicated range of glucose levels is a common way to describe the precision and accuracy of glucose measurement by a glucose sensing system. MARD is the result of a mathematical calculation that measures the mean difference between the estimated glucose value produced by the glucose sensor and the reference measurement. A lower MARD indicates a more accurate device.

[0327] definition To facilitate understanding of the various examples, some additional terms are defined below.

[0328] As used herein, the term “about” is a broad term, and its ordinary and customary meaning is given to those skilled in the art (and is not limited to any special or customized meaning), and when associated with any number or range, it means, but is not limited to, the understanding that the quantity or condition it modifies may vary somewhat beyond the stated quantity, insofar as the functionality of the embodiment is achieved.

[0329] As used herein, the term "A / D converter" is a broad term, its ordinary and conventional meaning given to those skilled in the art (and not limited to any special or customized meaning), and refers to, but is not limited to, hardware and / or software that converts an analog signal to a corresponding digital signal.

[0330] As used herein, the term “analyte” is a broad term, given its ordinary and customary meaning to those skilled in the art (and not limited to any special or customized meaning), and refers to, but is not limited to, a substance or chemical component in a biological fluid (e.g., blood, interstitial fluid, cerebrospinal fluid, lymph, or urine) that can be analyzed. Analytes may include naturally occurring substances, artificial substances, metabolites, and / or reaction products. In some embodiments, the analyte for measurement by the sensor heads, devices, and methods disclosed herein is glucose. However, other analytes are similarly intended, including lactate; bilirubin; ketones; carbon dioxide; sodium; potassium; acarboxyprothrombin; acylcarnitine; adenine phosphoribosyltransferase; adenosine deaminase; albumin; α-fetoprotein; amino acid profile (arginine (Krebs cycle), histidine / urocanic acid, homocysteine, phenylalanine / tyrosine, tryptophan); andrenostenedione; antipyrine; arabinitol enantiomer; arginase; benzoylecgonine (cocaine); biotinidase; biopterin; c-reactive protein; carnitine; carnosinase; CD4; ceruloplasmin; chenodeoxycholic acid; chloroquine; cholesterol; cholinesterase; conjugated 1-β-hydroxycholic acid Cortisol; Creatine kinase; Creatine kinase MM isoenzyme; Cyclosporine A; d-Penicillamine; Deethylchloroquine; Dehydroepiandrosterone sulfate; DNA (acetylated polymorphism, alcohol dehydrogenase, α1-antitrypsin, cystic fibrosis, Duchenne / Becker muscular dystrophy, analyte-6-phosphate dehydrogenase, abnormal hemoglobinopathy, A, S, C, E, D-Punjab, β-thalassemia, hepatitis B virus, HCMV, HIV-1, HTLV-1, Leber's hereditary optic neuropathy, MCAD, RNA, PKU, Plasmodium vivax, sex differentiation, 21-deoxycortisol); Desbutylhalofantrin; Dihydropteridine reductase; Diphtheria / tetanus antitoxin; Erythrocyte arginase; Erythrocyte protoporphyrin; Esterase D; Fatty acids / acylglycine;Free β-human chorionic gonadotropin; free erythrocyte porphyrin; free thyroxine (FT4); free triiodothyronine (free tri-iodothyronine, FT3); fumarylacetase; galactose / gal-1-phosphate; galactose-1-phosphate uridyltransferase; gentamicin; analyte-6-phosphate dehydrogenase; glutathione; glutathione peroxidase; glycocholic acid; glycosylated hemoglobin; halofantrin; hemoglobin variant; hexosaminidase A; human erythrocyte carbonic anhydrase I; 17α-hydroxyprogesterone; hypoxanthine phosphoribosyltransferase; immunoreactive trypsin; lead; lipoprotein ((a), B / A-1, β); lysozyme; mefloquine; netylmycin; phenobarbiton; phenytoin; phytanic acid / pristanic acid; progesterone; prolactin; prolidase; purine nucleoside phosphorylase; quinine; reverse triiodothyronine Tri-iodothyronine, rT3); selenium; serum pancreatic lipase; shisomycin; somatomedin C; specific antibodies (adenovirus, antinuclear antibody, antizeta antibody, arbovirus, pseudorabies virus, dengue virus, guinea pig, tapeworm, amoeba histolytica, enterovirus, giardiasis, Helicobacter pylori, hepatitis B virus, herpesvirus, HIV-1, IgE (atopic disease), influenza virus, Donovan leishmania, leptospirosis, measles / mumps / rubella, Mycoplasma leprae, mycoplasma pneumoniae, myoglobin, trichomoniasis, parainfluenza) Viruses, malaria parasites, poliovirus, Pseudomonas aeruginosa, respiratory syncytial virus, rickettsia (tsutsugyu typhus), Schistosomiasis mansoni, Toxoplasma, Treponema pallidum, Trypanosoma cruzi / langeri, vesicular stomatitis virus, Wuchereria bancrofti, yellow fever virus); specific antigens (hepatitis B virus, HIV-1); succinylacetone; sulfadoxine; theophylline; thyrotropin (TSH); thyroxine (T4); thyroxine-binding globulin; trace elements; transferrin; UDP-galactose-4-epimerase; urea; uroporphyrinogen I synthase; vitamin A; white blood cells;and zinc protoporphyrin. Salts, sugars, proteins, fats, vitamins, and hormones naturally present in blood or interstitial fluid may also constitute the analyte in certain embodiments. Analytes may be naturally present in biological fluids, such as metabolites, hormones, antigens, antibodies, etc. Alternatively, analytes may be introduced into the body, such as contrast agents for imaging, radioisotopes, chemical agents, fluorocarbon-based synthetic blood, or drugs or pharmaceutical compositions, including insulin; ethanol; cannabis (marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide, amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine (crack cocaine); stimulants (amphetamine, methamphetamine, Ritalin, Silulto, Preludin, Didrex, Prestate, Boranil, Sandrex, Pregin); antidepressants (barbiturates, methacaron, Valium) Examples of analytes include, but are not limited to, tranquilizers such as Librium, Miltown, Serax, Equanil, and Tranxine; hallucinogens (phencyclidine, lysergic acid, mescaline, peyote, psilocybin); narcotics (heroin, codeine, morphine, opium, meperidin, Percocet, Percodan, Tasionex, fentanyl, Dalvon, Talwin, Romotil); synthetic narcotics (fentanyl, meperidin, amphetamine, methamphetamine, and analogs of phencyclidine, e.g., ecstasy); anabolic steroids; and nicotine. Metabolites of drugs and pharmaceutical compositions are also intended analytes. For example, analytes of neurochemicals and other chemicals produced in the body, such as ascorbic acid, uric acid, dopamine, norepinephrine, 3-methoxytyramine (3MT), 3,4-dihydroxyphenylacetic acid (DOPAC), homovanillic acid (HVA), 5-hydroxytryptamine (5HT), and 5-hydroxyindoleacetic acid (FHIAA), can also be analyzed.

[0331] As used herein, the term “baseline” is a broad term whose ordinary and conventional meaning is given to those skilled in the art (and is not limited to any special or customized meaning), referring to, but not limited to, components of the analyte sensor signal that are not related to the estimated analyte value. In one example of a glucose sensor, the baseline is substantially composed of signal contributions from factors other than glucose (e.g., interfering species, non-reactive related hydrogen peroxide, or other electroactive species with oxidation potentials overlapping with hydrogen peroxide). In some embodiments, calibration may 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 0 or about 0. This may be, for example, a result of a baseline subtraction electrode or a low bias potential setting. Consequently, in these embodiments, calibration may be defined by solving the equation y = mx.

[0332] As used herein, the term “biological sample” is a broad term, its ordinary and conventional meaning given to those skilled in the art (and not limited to any 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, for example, blood, interstitial fluid, cerebrospinal fluid, saliva, urine, tears, sweat, or similar fluids.

[0333] As used herein, the term “calibration” is a broad term whose ordinary and conventional meaning is given to those skilled in the art (and is not limited to any special or customized meaning), and refers to, but is not limited to, the process of determining the grade of a sensor that provides quantitative measurements (e.g., estimated analyte values). For example, calibration may be updated or recalibrated over time to account for changes associated with the sensor, such as changes in sensor sensitivity and sensor background. In addition, sensor calibration may involve automatic self-calibration, i.e., calibration that does not use a reference analyte value after the time of use.

[0334] As used herein, the term “co-analyte” is a broad term whose ordinary and customary meaning is given to those skilled in the art (and is not limited to any special or customized meaning), and refers to, but is not limited to, molecules required in an enzymatic reaction that react with the analyte and the enzyme to form a particular product under measurement. 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.

[0335] As used herein, the term “comprising” is synonymous with “including,” “containing,” or “characterized by,” and is comprehensive or open-ended, not excluding additional unlisted elements or methods.

[0336] As used herein, the term “computer” is a broad term, meaning a machine that can be programmed to manipulate data, with its ordinary and conventional meaning being evident to those skilled in the art (and not limited to any special or customized meaning).

[0337] As used herein, the terms “continuous analyte sensor” and “continuous glucose sensor” are broad terms, and their ordinary and customary meanings are shown to those skilled in the art (and are not limited to any special or customized meanings), and refer to, but are not limited to, a device that continuously or sustainably measures the concentration of an analyte / glucose and / or calibrates the device (for example, by continuously or sustainably adjusting or determining the sensitivity and background of the sensor) at time intervals ranging from fractions of a second to, for example, one, two, or five minutes or more.

[0338] As used herein, the term “continuous glucose sensing” is a broad term whose ordinary and conventional meaning is evident to those skilled in the art (and is not limited to any special or customized meaning), and refers to, but is not limited to, a period during which plasma glucose concentration is monitored continuously or in a continuous manner, for example, at time intervals ranging from less than one second to, for example, one, two, or five minutes or more.

[0339] As used herein, the term “count” is a broad term whose ordinary and conventional meaning is given to those skilled in the art (and not limited to any special or customized meaning), referring to, but not limited to, a unit of measurement of a digital signal. For example, the raw data stream measured in a count is directly related to the voltage (e.g., converted by an A / D converter) that is directly related to the current from the working electrode.

[0340] As used herein, the term “distal” is a broad term, given to those skilled in the art its ordinary and customary meaning (and not limited to any special or customized meaning), referring to, but not limited to, space relatively far from a reference point such as an origin or attachment point.

[0341] As used herein, the term “domain” is a broad term, given to those skilled in the art its ordinary and customary meaning (and not limited to any special or customized meaning), and refers to, but is not limited to, a layer, a uniform or non-uniform gradient (e.g., an anisotropic region of a film), a region of a film that may be a functional aspect of a material, or a region of a film provided as part of a film.

[0342] As used herein, the term “conductor” is a broad term, given its ordinary and customary meaning to those skilled in the art (and not limited to any special or customized meaning), and refers to, but is not limited to, a material containing mobile charges. When a potential difference is applied to separate points on a conductor, the mobile charges within the conductor are forced to move, and an electric current appears between these points according to Ohm's law.

[0343] As used herein, the term “electrical conductance” is a broad term, given to those skilled in the art its ordinary and customary meaning (and not limited to any special or customized meaning), referring to, but not limited to, the tendency of a material to behave as a conductor. In some embodiments, the term refers to a sufficient amount of electrical conductance (e.g., a material property) to provide the desired function (electrical conductivity).

[0344] As used herein, the terms “electrochemically reactive surface” and “electroactive surface” are broad terms, given to those skilled in the art their ordinary and customary meanings (and not limited to any special or customized meanings), referring to, but not limited to, the surface of an electrode on which an electrochemical reaction occurs. In one embodiment, the working electrode measures hydrogen peroxide (H2O2) which generates a measurable electron current.

[0345] As used herein, the term “electrode” is a broad term, given its ordinary and customary meaning to those skilled in the art (and not limited to any special or customized meaning), referring to, but not limited to, a conductor through which electricity flows when it enters or leaves something, such as a battery or electrical device. In one embodiment, the electrode is a metal part of a sensor (e.g., an electrochemically reactive surface) that is exposed to the extracellular environment to detect an analyte. In some embodiments, the term electrode includes a conductive wire or trace that electrically connects the electrochemically reactive surface to a connector (for connecting the sensor to an electronic device) or to an electronic device.

[0346] As used herein, the term “elongated conductor” is a broad term, given to those skilled in the art its ordinary and customary meaning (and not limited to any special or customized meaning), and refers to, but not limited to, an elongated object formed on at least a portion of a conductive material, including any number of coatings that may be formed on the elongated object. For example, “elongated conductor” may mean a bare elongated conductive core (e.g., a metal wire) or an elongated conductive core coated with one, two, three, four, five, or six or more layers of a material, each of which may or may not be conductive.

[0347] As used herein, the term “enzyme” is a broad term, given its ordinary and customary meaning to those skilled in the art (and not limited to any special or customized meaning), and refers to, but is not limited to, proteins or protein-based molecules that facilitate chemical reactions occurring in living organisms. Enzymes can act as catalysts for single reactions that convert reactants (also referred herein as analytes) into specific products. In one embodiment of a glucose oxidase-based glucose sensor, an enzyme, glucose oxidase (GOX), is provided to react with glucose (analyte) and oxygen to form hydrogen peroxide.

[0348] As used herein, the term “filtering” is a broad term whose ordinary and conventional meaning is given to those skilled in the art (and not limited to any special or customized meaning), and refers to, but is not limited to, any modification of a data set that makes the data set smoother or more continuous, removing or mitigating outliers, for example, by performing a moving average on the raw data stream.

[0349] As used herein, the term “function” is a broad term, given its ordinary and customary meaning to those skilled in the art (and not limited to any special or customized meaning), and refers to, but is not limited to, an action or use for which something is suitable or an action or use for which something is designed.

[0350] As used herein, the term "GOx" is a broad term, given to those skilled in the art its ordinary and customary meaning (and not limited to any special or customized meaning), and refers to, but is not limited to, the enzyme glucose oxidase (e.g., GOx is an abbreviation).

[0351] As used herein, the term “host” is a broad term, its ordinary and conventional meaning as shown to those skilled in the art (and not limited to any special or customized meaning), and refers to animals, including humans.

[0352] As used herein, the term “inactive enzyme” is a broad term, given its usual and customary meaning to those skilled in the art (and not limited to any special or customized meaning), and refers to, but is not limited to, an enzyme that has been inactivated (e.g., by denaturation) and is substantially devoid of enzymatic activity (e.g., glucose oxidase, GOx). Enzymes can be inactivated using various techniques known in the art (e.g., heating, freeze-thaw cycles, denaturation in organic solvents, acids or bases, cross-linking, genetic alteration of enzymatically important amino acids, etc.). In some embodiments, a solution containing an active enzyme can be applied to a sensor, and the applied enzyme is subsequently inactivated by heating or treatment with an inactivating solvent.

[0353] As used herein, the terms “insulating properties,” “electrical insulator,” and “insulator” are broad terms whose ordinary and conventional meanings are shown to those skilled in the art (and are not limited to any special or customized meanings), referring to, but not limited to, the tendency of a material to have no movable charge in order to prevent the transfer of charge between two points. In one embodiment, an electrical insulating material may be placed between two conductive materials to prevent the transfer of electricity between them. In some embodiments, these terms refer to a sufficient amount of insulating properties (e.g., of a material) to provide the required function (electrical insulation). The terms “insulator” and “non-conductive material” may be used interchangeably herein.

[0354] As used herein, the term “in vivo portion” is a broad term whose ordinary and customary meaning is given to those skilled in the art (and is not limited to any special or customized meaning), and refers to, but is not limited to, any portion of a device that is implanted or inserted into a host. In one embodiment, the in vivo portion of a transdermal sensor is the portion of the sensor that is inserted through the host’s skin and resides within the host.

[0355] As used herein, the term “membrane system” is a broad term, given to those skilled in the art its ordinary and customary meaning (and not limited to any special or customized meaning), and refers to, but is not limited to, a permeable or semipermeable membrane that may comprise two or more domains, is typically composed of materials several microns or more in thickness, may be permeable to oxygen, and optionally may be permeable to glucose. In one example, a membrane system may include an immobilized glucose oxidase enzyme that allows an electrochemical reaction to occur in order to measure the concentration of glucose.

[0356] As used herein, the term “operably connected” is a broad term whose ordinary and conventional meaning is given to those skilled in the art (and not limited to any special or customized meaning), referring to, but not limited to, one or more components linked to another component in a manner that enables the transmission of signals between those components. For example, one or more electrodes may be used to detect the amount of an analyte in a sample and convert that information into a signal, which can then be transmitted to an electronic circuit. In this example, the electrodes are “operably connected” to the electronic circuit. These terms are broad enough to include wired and wireless connectivity.

[0357] As used herein, the term “potentiostat” is a broad term whose ordinary and customary meaning is given to those skilled in the art (and not limited to any special or customized meaning), referring to an electrical system that applies a preset potential between the working electrode and the reference electrode of a two- or three-electrode battery and measures the current flowing through the working electrode. A potentiostat forces any current that needs to flow between the working electrode and the counter electrode to maintain a desired potential, as long as the required cell voltage and current do not exceed the compliance limits of the potentiostat.

[0358] As used herein, the terms “processor module” and “microprocessor” are broad terms, their ordinary and customary meanings given to those skilled in the art (and not limited to any special or customized meanings), and refer to, but not limited to, a computer system, state machine, processor, or similar device designed to perform arithmetic and logical operations using logic circuits that process in response to basic instructions that drive the computer.

[0359] As used herein, the term “proximal” is a broad term, given its ordinary and customary meaning to those skilled in the art (and not limited to any special or customized meaning), referring to the vicinity of a reference point such as an origin or attachment point.

[0360] As used herein, the terms “raw data stream” and “data stream” are broad terms whose ordinary and conventional meanings are evident to those skilled in the art (and not limited to any special or customized meanings), referring to, but not limited to, analog or digital signals directly related to estimated analyte values ​​measured by an analyte sensor. For example, a raw data stream is digital data of counts converted by an A / D converter from an analog signal (e.g., voltage or amperes) representing an estimated analyte value. The terms broadly encompass data points at multiple time intervals from a substantially continuous analyte sensor, which may include individual measurements taken at time intervals ranging from a fraction of a second to, for example, 1, 2, or 5 minutes or more.

[0361] As used herein, the term "RAM" is a broad term, given to those skilled in the art its ordinary and customary meaning (and not limited to any special or customized meaning), referring to, but not limited to, a data storage device in which the order of access to different locations does not affect the speed of access. RAM is so broad as to include, for example, SRAM, which is static random-access memory that holds data bits in its memory as long as power is supplied.

[0362] As used herein, the term "ROM" is a broad term, its ordinary and conventional meaning given to those skilled in the art (and not limited to any special or customized meaning), referring to, but not limited to, read-only memory, a type of data storage device manufactured with fixed content. ROM is so broad as to include, for example, EEPROM, which is electrically erasable and programmable read-only memory (ROM).

[0363] As used herein, the terms “reference analyte value” and “reference data” are broad terms whose ordinary and customary meanings are given to those skilled in the art (and are not limited to any 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, including one or more reference data points. In some embodiments, reference glucose values ​​are obtained, for example, from a self-monitored blood glucose (SMBG) test (e.g., from a finger or forearm blood test) or a YSI (Yellow Springs Instruments) test.

[0364] As used herein, the term “regression” is a broad term whose ordinary and conventional meaning is given to those skilled in the art (and not limited to any special or customized meaning), referring to finding a line from which a set of data has the smallest measurement (e.g., deviation). Regressions can be linear, nonlinear, first-order, second-order, etc. An example of regression is least-squares regression.

[0365] As used herein, the term “sensing region” is a broad term whose ordinary and conventional meaning is given to those skilled in the art (and is not limited to any special or customized meaning), and refers to, but is not limited to, an area of ​​a monitoring device responsible for detecting a particular analyte. In one embodiment, the sensing region generally comprises a non-conductive body, at least one electrode, a reference electrode, and optionally a counter electrode, which pass through and are fixed within the body, forming an electroactive surface at one location on the body and an electrical connection at another location on the body, and a film system attached to the body and covering the electroactive surface.

[0366] As used herein, the terms “sensitivity” or “sensor sensitivity” are broad terms whose ordinary and conventional meanings are given to those skilled in the art (and are not limited to any special or customized meanings) and refer to, but are not limited to, the amount of signal produced by a measured analyte or a measured species (e.g., H2O2) associated with a measured analyte (e.g., glucose). For example, in one embodiment, the sensor has a sensitivity of about 1 to about 300 picoamperes of current for every 1 mg / dL of glucose analyte.

[0367] As used herein, the terms “sensitivity profile” and “sensitivity curve” are broad terms whose ordinary and conventional meanings are given to those skilled in the art (and are not limited to any special or customized meanings), and refer to, but are not limited to, expressions of changes in sensitivity over time.

[0368] As used herein, the terms “sensor analyte value” and “sensor data” are broad terms whose ordinary and customary meanings are evident to those skilled in the art (and are not limited to any special or customized meanings), and refer to, but are not limited to, data received from a continuous analyte sensor, including one or more spatiotemporal sensor data points.

[0369] As used herein, the terms “sensor electronics” and “electronic circuit” are broad terms whose ordinary and customary meanings are evident to those skilled in the art (and not limited to any special or customized meanings), and refer to, but are not limited to, components of a device configured to process data (e.g., hardware and / or software). 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. Patents 4,757,022, 5,497,772, and 4,787,398 describe suitable electronic circuits that may be used with devices of a particular embodiment.

[0370] As used herein, the terms “sensor environment” and “sensor operating environment” are broad terms whose ordinary and customary meanings are evident to those skilled in the art (and are not limited to any special or customized meanings), referring to the biological environment in which the sensor operates.

[0371] As used herein, the terms “substantial” and “substantially” are broad terms whose ordinary and customary meanings are evident to those skilled in the art (and not limited to any special or customized meanings), and primarily refer to, but not necessarily, all of, the designated terms.

[0372] As used herein, the term “thermal conductivity” is a broad term, given to those skilled in the art its ordinary and customary meaning (and not limited to any special or customized meaning), and refers to, but is not limited to, the amount of heat transferred per unit area due to a unit temperature gradient per unit time under steady conditions in a direction perpendicular to the surface.

[0373] As used herein, the term “thermal coefficient” is a broad term whose ordinary and conventional meaning is given to those skilled in the art (and is not limited to any special or customized meaning), referring to, but not limited to, the change in resistance of a material at various temperatures.

[0374] As used herein, the term “thermally conductive material” is a broad term, given to those skilled in the art its ordinary and customary meaning (and not limited to any special or customized meaning), and refers to, but is not limited to, materials exhibiting high thermal conductivity.

[0375] As used herein, “thermocouple” is a broad term, given its ordinary and customary meaning to those skilled in the art (and not limited to any special or customized meaning), and refers to, but is not limited to, a device comprising two different conductors (e.g., metal alloys) that produce a voltage between the respective ends of two conductors that is proportional to the temperature difference between them.

[0376] overview Some analyte sensors measure the concentration of substances in the body (e.g., glucose) (e.g., measuring glucose concentration in blood or interstitial fluid at a subcutaneous location). The output of an analyte sensor can be affected by temperature. The temperature of the subcutaneous region of the body where the sensor may be located can vary from person to person and can vary over time within an individual. For example, subcutaneous temperature can be affected by changes in body temperature (such as fever or periodic fluctuations) as well as changes in ambient temperature. For example, exposure to hot or cold water, warm clothing, exposure to cold weather, and sunlight can alter the host's subcutaneous temperature. When such conditions or states are present, temperature fluctuations can cause inaccuracies in estimating glucose concentration levels. The accuracy and precision of the estimated glucose value can be improved by compensating for temperature fluctuations at the sensing site or within the sensor when the sensor is worn by the host.

[0377] The performance of analyte sensor systems can be improved by compensating for these temperature effects. For example, temperature compensation can increase sensory accuracy or decrease MARD. However, temperature compensation presents implementation challenges because it can be difficult to know the actual temperature at the sensing site or how much compensation is needed, and the temperatures of the host body and various system components can fluctuate relative to each other and change over time.

[0378] In some cases, temperature compensation can be applied to the sensitivity value used to convert the signal from the sensor into an estimated analyte value (e.g., sensitivity changes by 3% for every 1°C deviation from a reference temperature (e.g., 35°C)). In some cases, temperature compensation can be applied directly to the estimated glucose value. In some cases, a compensated glucose value instead of sensor sensitivity may produce a more accurate value. For example, in addition to variations in enzyme sensitivity, other effects may affect glucose concentration levels or sensor responses. Additional temperature effects may include local glucose concentration variations (as opposed to whole-body glucose levels), compartment bias (differences in glucose concentration between interstitial fluid and blood), and non-enzyme sensor bias (e.g., electrochemical baseline signals not produced by glucose / enzyme interactions). Models may be developed to account for some or all of these additional factors, which may provide a more accurate estimate of glucose concentration levels.

[0379] Figure 1 shows an exemplary system 100 in which exemplary temperature compensation systems, devices, and methods may be implemented. System 100 may include a continuous analyte sensor system 8, which includes a sensor electronic device 12 and a continuous analyte sensor 10. System 100 may include a drug delivery pump 2 (which may be communicatively coupled to the continuous analyte sensor system, for example, to enable closed-loop therapy), and other devices and / or sensors such as a glucose meter 4, which may be communicatively coupled to the continuous analyte sensor system 8. The continuous analyte sensor 10 may be physically coupled to the sensor electronic device 12, which may be removably mounted to the sensor electronic device 12, or which may be integrated with the sensor electronic device 12 (for example, which may be non-removably mounted). The sensor electronic device 12, the drug delivery pump 2, and / or the glucose meter 4 may be coupled to one or more devices such as display devices 14, 16, 18, and / or 20.

[0380] In some exemplary implementations, system 100 may include a cloud-based analyte processor 490 configured to analyze analyte data (and / or other relevant data from other patients) provided via network 406 (e.g., via wired, wireless, or a combination thereof) from a sensor system 8 and other devices such as display devices 14-20 associated with the host (also referred to as subject or patient) to generate a report that provides high-level information, such as statistics on the analyte measured over a specific time frame. A complete consideration of using a cloud-based analyte processing system can be found in U.S. Patent Application Publication 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 implementations, one or more steps of a temperature compensation algorithm may be performed in the cloud.

[0381] In some exemplary implementations, the sensor electronics 12 may include electronic circuits 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, although these algorithms may be provided in other ways. The sensor electronics 12 may include hardware, firmware, software, or a combination thereof for providing measurement of analyte values ​​via a continuous analyte sensor such as a continuous glucose sensor. Exemplary implementations of the sensor electronics 12 are described further below with respect to Figure 2B. In one implementation, a temperature compensation method may be implemented by the sensor electronics 12.

[0382] As described above, the sensor electronic device 12 can be coupled (for example, wirelessly) with one or more devices such as display devices 14, 16, 18, and / or 20. The display devices 14, 16, 18, and / or 20 can be configured to display (and / or warn) information such as sensor information transmitted by the sensor electronic device 12 for display on the display devices 14, 16, 18, and / or 20.

[0383] The display device may include a relatively small display device 14. In some exemplary implementations, the relatively small display device 14 may be part of a key fob, wristwatch, belt, necklace, pendant, jewelry, adhesive patch, pager, plastic card (e.g., credit card), identification (ID) card, etc. This small display device 14 may include a relatively small display area (e.g., smaller than the large display device 16) and may be configured to display certain types of displayable sensor information, such as numbers and arrows, or color codes. 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 US-B port or wireless communication transceiver) for uploading data to another device.

[0384] In some exemplary implementations, the relatively large handheld display device 16 may include a handheld receiver device, a palmtop computer, and / or equivalent. This large display device may include a relatively larger display area (e.g., larger than the small display device 14) and may be configured to display information such as a graphic 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.

[0385] 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 device configured to display at least information (e.g., drug delivery information, discrete self-monitoring glucose readings, heart rate monitor, calorie intake monitor, etc.).

[0386] Each display device may be connected to network 406 via a wired or wireless connection (e.g., cellular, Bluetooth®, Wi-Fi, MICS, ZigBee®) and may include a processor and memory circuitry for storing and processing information. In some examples, a temperature compensation method may be carried out at least partially by one or more of the display devices.

[0387] In some exemplary implementations, the continuous analyte sensor 10 may include a sensor for detecting and / or measuring the analyte, and the continuous analyte sensor 10 may be configured to continuously detect and / or measure the analyte as a non-invasive device, a subcutaneous device, a transdermal device, and / or an intravascular device. In some exemplary implementations, the continuous analyte sensor 10 can analyze multiple intermittent blood samples, but other analytes can be used as well.

[0388] In some exemplary implementations, 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 enzyme, chemical, physical, electrochemical, spectrophotometric, optical rotation, calorimetry, ion electrophoresis, radiometric analysis, and immunochemistry. In implementations where the continuous analyte sensor 10 includes a glucose sensor, the glucose sensor may include any device capable of measuring glucose concentration and may provide data such as a data stream indicating glucose concentration in a host using a variety of techniques for measuring glucose, including invasive, minimally invasive, and non-invasive sensing techniques (e.g., fluorescence monitoring). The data stream may be sensor data (raw data and / or filtered data) and may be converted into a calibrated data stream used to provide glucose values ​​to a host such as a user, patient, or caregiver (e.g., parent, relative, guardian, teacher, doctor, nurse, or any other individual interested in the host's health). Furthermore, the continuous analyte sensor 10 can be implanted as at least one of the following types of sensors: an implantable glucose sensor, a transcutaneous glucose sensor implanted intravascularly or extravascularly in the host, a subcutaneous sensor, a refillable subcutaneous sensor, or an intravascular sensor.

[0389] The disclosure herein refers to several implementations including a continuous analyte sensor 10 equipped with a glucose sensor, but the continuous analyte sensor 10 may also include other types of analyte sensors. Furthermore, while some implementations may refer to the glucose sensor as an embedded glucose sensor, other types of devices capable of detecting glucose concentration and providing an output signal representing glucose concentration may also be used. Furthermore, the description herein refers to glucose as the analyte being measured, processed, etc., but other analytes, such as ketone bodies (e.g., acetone, acetoacetic acid, and β-hydroxybutyrate, lactate esters, etc.), glucagon, acetyl coenzyme A, triglycerides, fatty acids, intermediates in the citric acid cycle, choline, insulin, cortisol, testosterone, etc., may also be used.

[0390] Figure 2A is a schematic diagram of an exemplary analyte sensor system 250, which may be, for example, system 8 shown in Figure 1. 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 memory 256. The processor may receive a glucose sensor signal from the glucose sensor 252 indicating a glucose concentration level and a temperature sensor signal from the temperature sensor 254 indicating a temperature parameter (e.g., absolute temperature or relative temperature, or temperature gradient). 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 blood pressure monitor (e.g., to measure the pressure of the sensor on the host).

[0391] The processor 251 may determine a temperature-compensated glucose concentration level (or other estimated analyte value) based on the glucose sensor signal, the temperature sensor signal, and optionally on one or more signals from additional sensors 258. The processor 251 may determine a specific temperature-compensated sensitivity value (e.g., a temperature-based analyte sensor sensitivity value) or a compensated estimated glucose value. The signal from the temperature sensor 254 may be used as an approximation of the temperature at the analyte sensor, or the signal from the temperature sensor 254 may be processed (e.g., using the methods described in detail below) to determine the estimated analyte temperature sensor based on the signal from the temperature sensor 254. In some examples, the processor may retrieve instructions or information from memory 256 to determine the temperature-compensated glucose concentration level. For example, the processor may 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 examples, the processor may retrieve executable instructions from memory 256 (or a separate memory that can be operably coupled to or integrated with the processor). In some examples, the processor may include, or be part of, an application-specific integrated circuit (ASIC) that can be configured to determine temperature-compensated glucose concentration levels. In various examples, one or more of the methods described herein or illustrated in Figures 6 to 14 may be performed by the processor 251 or the temperature-compensated glucose sensor, either alone or in conjunction with other processors or devices, such as the device illustrated in Figure 5.

[0392] Figure 2B shows a more detailed diagram of an exemplary sensor electronic device 12. The sensor electronic device may be part of a system of devices such as those shown in Figure 1. The sensor electronic device 12 may include, for example, a sensor electronic device configured to process sensor information such as sensor data and generate converted sensor data and displayable sensor information via a 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 values), raw sensor data, calibrated sensor data (e.g., one or more calibrated analyte values), rate of change information, trend information, acceleration / deceleration information, sensor diagnostic information, location information, alarm / alert information, calibration information which can be determined by a calibration algorithm, sensor data smoothing and / or filtering algorithms, etc.

[0393] The sensor electronic equipment 12 may include a first temperature sensor 240. In some examples, the signal from the temperature sensor 240 may be used for temperature compensation, for example, to compensate for the effect of temperature on the analyte sensor. In some examples, the sensor electronic equipment 12 may include an optional second temperature sensor 242. The signals from the first temperature sensor 240 and the second temperature sensor 242 may be used to determine the heat flux or temperature gradient. In some examples, the temperature sensors 240 and 242 act as backups for each other. For example, if the temperature sensor 240 fails, the sensor electronic equipment 12 may continue to operate using the temperature signal from the temperature sensor 242. If the temperature sensor 242 fails, the sensor electronic equipment 12 may continue to operate using the temperature signal from the temperature sensor 240.

[0394] In some embodiments, the processor module 214 may be configured to perform a substantial, if not all, portion, of the data processing, including data processing related to factory calibration or temperature compensation. Factory calibration may be a calibration of a continuous analyte sensor that can achieve a high level of accuracy without relying on (or with reduced reliance on) reference data from a reference analyte monitor (e.g., a blood glucose meter). The processor module 214 may be integrated with the sensor electronics 12 and / or located remotely, such as on one or more of the devices 14, 16, 18, and / or 20, and / or the cloud 490. In some embodiments, the processor module 214 may include a plurality of smaller subcomponents or submodules. For example, the processor module 214 may include an alarm module (not shown) or a predictive module (not shown), or any other suitable module that can be used to efficiently process data. If the processor module 214 consists of multiple submodules, the submodules may be located within the processor module 214, including within the sensor electronics 12 or other related devices (e.g., 14, 16, 18, 20, and / or 490). For example, in some embodiments, the processor module 214 may be located at least partially within the cloud-based analyte processor 490 or elsewhere within the network 406.

[0395] In some exemplary implementations, the processor module 214 may be configured to calibrate sensor data, and the data storage memory 220 may store the calibrated sensor data points as converted sensor data. Furthermore, in some exemplary implementations, the processor module 214 may be configured to receive calibration information wirelessly from a display device such as devices 14, 16, 18, and / or 20, enabling calibration of sensor data from sensor 12. Furthermore, the processor module 214 may be configured to perform additional algorithmic processing on 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 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.

[0396] In some exemplary implementations, some or all of the sensor electronics 12 may be incorporated to include an ASIC 205, which may be coupled to a user interface 222 via a wired or wireless connection. For example, the ASIC 205 may include a potentiostat 210, a telemetry module 232 for transmitting data from the sensor electronics 12 to one or more devices such as 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). Figure 2B shows the ASIC 205, but other types of circuitry may be used similarly, 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 circuits, digital circuits, or a combination thereof. In addition, ASIC205 may consist of only a subset (one or more) of the devices, and devices 210, 214, 216, 218, 220, 232, 240, and 242 may each be included within the ASIC, provided as separate components, or integrated as a separate ASIC (for example, as a second or third ASIC).

[0397] In the example shown in Figure 2B, the potentiostat 210 can be coupled to a continuous analyte sensor 10, such as a glucose sensor, via a first input port for sensor data, to generate sensor data from the analyte. The potentiostat 210 can also provide a voltage via the data line 212 to an analyte sensor, such as the continuous analyte sensor 10 (shown in Figure 5) or the sensor shown in Figures 2C, 3, 4, 5A, or 5B, to bias the sensor for measuring a value (e.g., current) indicating the concentration of the analyte within the host (also referred to as the analog portion of the sensor). The potentiostat 210 may have one or more channels, depending on the number of working electrodes in the continuous analyte sensor 10.

[0398] In some exemplary implementations, the potentiostat 210 may include a resistor that converts current values ​​from the sensor 10 into voltage values, and in some exemplary implementations, a current-frequency converter (not shown) may also be configured to continuously integrate the measured current values ​​from the sensor 10 using, for example, a charge counting device. In some exemplary implementations, an analog-to-digital converter (not shown) may digitize the analog signal from the sensor 10 into a so-called "count" to enable processing by the processor module 214. The resulting count may be directly related to the current measured by the potentiostat 210, which may be directly related to an analyte level, such as glucose levels in the host.

[0399] The telemetry module 232 may be operably connected to the processor module 214 and may provide hardware, firmware, and / or software that enables wireless communication between the sensor electronics 12 and one or more other devices such as a display device, a processor, or a network access device. Various wireless techniques that may be implemented in the telemetry module 232 include Bluetooth, Bluetooth Low-Energy, ANT, ANT+, ZigBee, IEEE 802.11, IEEE 802.16, cellular wireless access techniques, radio frequency (RF), infrared (IR), paging network communication, magnetic induction, satellite data communication, spread spectrum communication, frequency hopping communication, and short-range wireless communication. In some exemplary implementations, the telemetry module 232 may include a Bluetooth chip, but the Bluetooth technique may also be implemented in combination with the telemetry module 232 and the processor module 214.

[0400] The processor module 214 can control the processing performed by the sensor electronic equipment 12. For example, the processor module 214 may be configured to process data from the sensor (e.g., counts), filter the data, calibrate the data, perform fail-safe checks, and so on.

[0401] In some exemplary implementations, the processor module 214 may include a digital filter, such as an infinite impulse response (IIR) filter or a finite impulse response (FIR) filter. This digital filter can smooth the raw data stream received from the sensor 10. Generally, the digital filter is programmed to filter data sampled at a predetermined time interval (also referred to as the sample rate). In some exemplary implementations, the potentiostat 210 is configured to measure the analyte (e.g., glucose) at discrete time intervals, and these time intervals determine the sampling rate of the digital filter. In some exemplary implementations, the potentiostat 210 may be configured to measure the analyte continuously using, for example, a current-frequency converter. In these current-frequency converter implementations, the processor module 214 may be programmed to request a digital value from the integrator of the current-frequency converter at a predetermined time interval (acquisition time). These digital values ​​obtained from the integrator by the processor module 214 can be averaged over the acquisition time due to the continuity of the current measurements. Therefore, the acquisition time can be determined by the sampling rate of the digital filter.

[0402] The processor module 214 may further include a data generator (not shown) configured to generate data packages for transmission to devices such as display devices 14, 16, 18, and / or 20. Furthermore, the processor module 214 may generate data packets for transmission to these external sources via the telemetry module 232. In some exemplary implementations, the data packages may be customizable for each display device as described above and / or may include any available data such as temperature information or temperature-related information, temperature compensation data, accelerometer data, motion data, position data, timestamps, displayable sensor information, converted sensor data, identification codes for sensors and / or sensor electronic devices 12, raw data, filtered data, calibrated data, rate of change information, trend information, error detection or correction, temperature information, or any combination thereof.

[0403] The processor module 214 may also include program memory 216 and other memories 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. Furthermore, the battery 234 may be further coupled to a battery charger and / or regulator 236 to supply power to the sensor electronics 12 and / or charge the battery 234.

[0404] The program memory 216 may be implemented as a semi-static memory for storing data such as identifiers of the coupled sensor 10 (e.g., sensor identifiers (IDs)) and code (also called 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 perform data stream or count processing, filtering, calibration methods, fail-safe checks, etc.

[0405] Memory 218 may also be used to store information. For example, a processor module 214 containing memory 218 may be used as the system's cache memory, and temporary storage may be provided for recent sensor data received from the sensor. In some exemplary implementations, memory may 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, and flash memory.

[0406] The data storage memory 220 may be coupled to the processor module 214 and may be configured to store various sensor information. In some exemplary implementations, the data storage memory 220 stores continuous analyte sensor data for one day or more. For example, the data storage memory may store continuous analyte sensor data for 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, and / or 30 days (or more) received from the sensor 10. The stored sensor information may include one or more of the following: temperature information or temperature-related information, temperature-compensated data, timestamp, raw sensor data (one or more raw analyte values), calibrated data, filtered data, converted sensor data, and / or any other displayable sensor information, calibration information (e.g., previous calibration information such as that obtained from a reference BG value and / or factory calibration), sensor diagnostic information, temperature information, etc.

[0407] 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, and a backlight (not shown). Components comprising such a user interface 222 may provide controls for interconnecting with a user (e.g., a host). One or more buttons 224 may enable, for example, toggles, menu selections, option selections, status selections, yes / no responses to on-screen questions, an "off" function (e.g., for alarms), an "acceptance" function (e.g., for alarms), a reset, and so on. The display 226 may provide the user with, for example, visual data output. The audio transducer 230 (e.g., a speaker) may provide audible signals in response to triggers for specific alerts, such as current hyperglycemic and hypoglycemic states and / or predicted hyperglycemic and hypoglycemic states. In some exemplary implementations, an audible signal may be distinguished by tone, volume, duty cycle, pattern, duration, etc. In some exemplary implementations, an audible signal may be muted (e.g., acknowledged or turned off) by pressing one or more buttons 224 on the sensor electronic device 12 and / or by transmitting a signal to the sensor electronic device 12 using a button or selection on a display device (e.g., a key fob, mobile phone, etc.).

[0408] While audible and vibration alarms are described with respect to Figure 2B, other alarm mechanisms can be used in a similar manner. For example, in some exemplary implementations, a tactile alarm is provided which includes a poking mechanism configured to "poke" or physically touch the patient in response to one or more alarm conditions or states.

[0409] The battery 234 is operably connected to the processor module 214 (and possibly other components of the sensor electronics 12) and can supply the necessary power to the sensor electronics 12. In some exemplary implementations, the battery may be a manganese dioxide lithium battery, but any battery of appropriate size and power (e.g., AAA battery, nickel-cadmium battery, zinc-carbon battery, alkaline battery, lithium battery, nickel-metal hydride battery, lithium-ion battery, zinc-air battery, zinc-mercury oxide battery, silver-zinc battery, or sealed battery) may be used. In some exemplary implementations, the battery may be rechargeable. In some exemplary implementations, multiple batteries may be used to power the system. In yet another implementation, the receiver may be powered transcutaneously, for example, via inductive coupling.

[0410] The battery charger and / or regulator 236 may be configured to receive energy from an internal and / or external charger. In some exemplary implementations, the battery regulator (or balancer) 236 regulates the recharging process by releasing excess charging current to ensure that all batteries in the sensor electronics 12 are fully charged without overcharging other batteries. In some exemplary implementations, the battery 234 (or multiple batteries) may be configured to be charged via an inductive charging pad and / or a wireless charging pad, but any other charging and / or power mechanism may also be used.

[0411] One or more communication ports 238, also called 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 electronic device 12. The communication ports may include, for example, serial (e.g., Universal Serial Bus or "USB") communication ports, which may enable communication with another computer system (e.g., a PC, a personal digital assistant or "PDA", a server, etc.). In some exemplary implementations, the sensor electronic device 12 may be capable of transmitting historical data to a PC or other computing device for retrospective analysis by patients and / or HCPs. As another example of data transmission, factory information may be transmitted from the sensor to the algorithm or from a cloud data source to the algorithm.

[0412] One or more communication ports 238 may further include a second input port from which calibration data may be received, and an output port that may be used to transmit calibrated data or data to be calibrated to a receiver or mobile device. While the ports may be physically separated, it will be understood that in alternative implementations, a single communication port may provide the functionality of both the second input and output ports.

[0413] In some continuous analyte sensor systems, the on-skin portion of the sensor electronics may be simplified to minimize the complexity and / or size of the on-skin electronics, providing only raw data, calibrated data, 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 (e.g., via a processor module 214) may be implemented to perform prospective algorithms used to generate transformed sensor data and / or displayable sensor information, including algorithms such as, for example, evaluating the clinical acceptability of criteria and / or sensor data, evaluating calibration data for the best calibration based on inclusion criteria, evaluating the quality of calibration, comparing estimated analyte values ​​with time-dependent measured analyte values, analyzing fluctuations in estimated analyte values, evaluating the stability of the sensor and / or sensor data, detecting signal artifacts (noise), replacing signal artifacts, determining the rate of change and / or trend of sensor data, performing dynamic and intelligent analyte value estimation, performing diagnostics on the sensor and / or sensor data, setting operating modes, and evaluating the data for anomalies.

[0414] Although separate data storage devices and program memory are shown in Figure 2B, various configurations can be used similarly. For example, one or more memories may be used to provide storage space to support the data processing and storage requirements of the sensor electronic device 12.

[0415] In one preferred embodiment, the analyte sensor may be an implantable glucose sensor, as described with reference to U.S. Patent No. 6,001,067 and U.S. Patent Publication No. 2005 / 0027463(A1). In another preferred embodiment, the analyte sensor may be a transcutaneous glucose sensor, as described with reference to U.S. Patent Publication No. 2006 / 0020187(A1). In yet another embodiment, the sensor may be configured to be implanted intravascularly or extracorporeally in the host, as described in U.S. Patent Publication No. 2007 / 0027385(A1), U.S. Patent Publication No. 2008 / 0119703(A1) (now abandoned), U.S. Patent Publication No. 2008 / 0108942(A1) (now abandoned), and U.S. Patent No. 7,828,728. In one alternative embodiment, the continuous glucose sensor may include a transcutaneous sensor, for example, as described in U.S. Patent No. 6,565,509 by Say et al. In another alternative embodiment, the continuous glucose sensor may include a subcutaneous sensor, for example, as described with reference to U.S. Patent No. 6,579,690 by Bonnecaze et al., or U.S. Patent No. 6,484,046 by Say et al. In another alternative embodiment, the continuous glucose sensor may include a refillable subcutaneous sensor, for example, as described with reference to U.S. Patent No. 6,512,939 by Colvin et al. In another alternative embodiment, the continuous glucose sensor may include an intravascular sensor, for example, as described with reference to U.S. Patent No. 6,477,395 by Schulman et al. In yet another alternative embodiment, the continuous glucose sensor may include an intravascular sensor, for example, as described with reference to U.S. Patent No. 6,424,847 by Mastrototaro et al.

[0416] Figure 2C is a schematic diagram of an exemplary analyte sensor system 8 showing an analyte sensor 10 inserted into the subcutaneous layer 264 through the epidermis 260 and dermis 262, such that the distal end 280 of the analyte sensor 10 is located in the subcutaneous layer. In a human host, the epidermal layer 260 may typically be about 0.01 cm thick, the dermal layer 262 may typically be about 0.2 cm thick, and the subcutaneous layer may be substantially thicker, for example, 1 cm to 1.5 cm. The working portion 282 (e.g., working electrode) of the analyte sensor 10 may be located at or near the distal end 280 of the analyte sensor at a depth of about 0.5 cm. The working portion 282 may include, for example, a coating on a conductive portion 286 (e.g., a conductive core). The working portion 282 may be configured to generate a voltage proportional to the glucose concentration (for example, the working portion may be part of a glucose sensor available from Dexcom, Inc.). In some examples, the temperature sensor 284 may be located at or near the distal end 280 of the analyte sensor. The temperature sensor 284 may be used to compensate for temperature fluctuations using one or more of the various techniques described below. In addition, empirical measurements (discussed below and shown in Figure 21) indicate that the conductance of the analyte sensor may be strongly dependent on temperature. In some examples, this relationship between conductance and temperature may be used to estimate subcutaneous temperature, which may be used in a temperature compensation model or other methods. In other examples, the relationship between conductance and temperature may be applied directly (e.g., without using an estimated temperature) to compensate for temperature fluctuations.

[0417] The analyte sensor can be coupled to a base 274 which can be coupled to a housing 266. The housing can accommodate some or all of the components shown in Figure 2A or the sensor electronics 12 shown in Figure 2B.

[0418] In some examples, the housing may include a heat shield 272 on its top surface (and optionally on one or more additional sides) to reflect heat from the housing, thereby reducing, for example, the effects of sunlight on the sensor 10.

[0419] In some examples, the sensor electronic device 12 may include a first temperature sensor 268 near the bottom of the housing 266 and a second temperature sensor 270 near the top of the housing. Circuitry such as the processor 251 or processor module 214 may be configured, at least in part, to determine a compensated glucose concentration level based on the glucose signal, the first temperature signal, and the second temperature signal.

[0420] In some examples, the temperature gradient or heat flux can be determined from signals received from the first temperature sensor 268 and the second temperature sensor 270 (for example, by the processor 251 or processor module 214). 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 in a temperature compensation algorithm or model. In another example, the sensor may be exposed to low temperatures, in which case the second temperature sensor 270 may indicate a lower temperature than the first temperature sensor. In yet another example, the system may be immersed in cold water, in which case the first temperature sensor 268 and the second temperature sensor may initially show a gradient but rapidly transition to nearly equal temperature values. This information can be used directly in temperature compensation based on the relationship between one or more of the temperature sensors 268 and 270 and the temperature in the analyte sensor 10, or the temperature information can be used indirectly as an indicator 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 this indicator can be applied to a model.

[0421] Figure 2D is a schematic diagram of another exemplary configuration of the analyte sensor system 8 engaged with host tissue. In the example of Figure 2D, the temperature sensor 281 is positioned on the base 274 in contact with the epidermis 260 of the host skin. For example, the temperature sensor 281 may be incorporated into an adhesive pad for fixing the base 274 to the host skin.

[0422] Figure 3 is 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 the glucose concentration level of a host substance (e.g., an interstation fluid). The signal may be conducted through one or more elongated members 304, 306, which may be wires (e.g., platinum or tantalum or alloys thereof). The sensor signal may be communicated to sensor electronics for processing. The analyte sensor 10 may also include a temperature sensor 308, which may be located in or near the analyte sensor region 302. In one example, the temperature sensor 308 may be a thermocouple capable of producing a voltage proportional to the temperature difference between, for example, a junction 310 of conductors 304, 306 and a second junction (not shown) which may be located at the proximal end of the conductors (e.g., outside the host). To form a working thermocouple, the conductors 304, 306 may be formed from different materials. For example, one of the conductors 304, 306 may be platinum, and the other may be tantalum. The signal generated by the thermocouple can be communicated to sensor electronics for processing (i.e., for use in compensating glucose sensor values ​​for temperature).

[0423] In another example, the temperature sensor 308 could be a thermistor. The resistance of the thermistor can be measured using conductors 304, 306 and communicated to the sensor electronics for processing.

[0424] In one example, a sequential method may be used to measure glucose concentration levels and temperature using a pair of conductors (for example, a platinum conductor and a tantalum conductor as described above, which may be 304 and 306 in Figure 3). For example, the estimated analyte value may be measured by applying a voltage (e.g., 0.6 volts) across the conductors, and then the temperature measurement may be obtained by measuring the open-circuit potential across the conductors, or by applying a low voltage input across the conductors and measuring the current (for example, to determine the resistance of a thermistor and thereby determine the temperature parameter).

[0425] In another example, the temperature sensor may be positioned at the proximal end of the sensor wire, and the sensor wire may have high thermal conductivity so that the temperature measurement at the proximal end approximates the temperature near the analyte sensor (i.e., the distal end). In various examples, such approximate temperature measurements may be used for temperature compensation.

[0426] Figure 4 is a schematic diagram of an exemplary proximal portion 401 and electrical contact portion 402 of the analyte sensor 10, the electrical contact portion which may be part of, for example, a sensor electronic device or a transmitter (such as a transmitter manufactured by Dexcom and configured to be coupled to a base portion containing a subcutaneous glucose sensor). The proximal portion 402 of the analyte sensor may include a first conductor 404 and a second conductor 406, the first conductor 404 and the second conductor 406 may have distal ends (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 coupled to the thermistor and configured to be coupled to a third contact 414 on the electrical contact portion. The temperature-sensitive resistance of a thermistor can be used to compensate for the effects of temperature on a glucose sensor.

[0427] Figure 5A shows a configuration similar to that in Figure 4, but the thermistor in Figure 4 is replaced by a temperature-sensitive coating 508. Figure 5B is a magnified view of a possible temperature-sensitive coating 508 on the conductor 406. The conductive element 510 may be configured to couple with the coating or connected to the coating, or configured to couple with a third contact 414, so that the resistance of the coating can be measured by applying a voltage or driving a current across the contacts 412, 414.

[0428] The system may use learned or defined relationships between an input (e.g., a temperature sensor signal, or one or more other sensor signals) and an analyte level to compensate for the effect of temperature on an analyte sensor (e.g., a glucose sensor) and provide an estimate (e.g., an estimated glucose value) that is less affected by temperature fluctuations. These relationships may be defined, for example, by theoretical models, or determined from bench data, clinical trial data, or a combination thereof.

[0429] Various methods, models, or algorithms can be applied or combined to compensate for temperature signal fluctuations caused by temperature changes. For example, a system may compensate for long-term trends or averages, or for short-term (e.g., real-time) changes, or a combination thereof.

[0430] In some cases, a linear relationship between temperature and the glucose sensor signal may be determined and used to approximate the relationship between temperature and the glucose sensor signal and to compensate for the effect of temperature, as given by, for example, equation [1] below.

[0431]

number

[0432] In some cases, the sensor's sensitivity M(t) to the analyte (glucose) concentration can be compensated for temperature effects by determining the temperature-compensated sensitivity M(t)comp based on the programmed (e.g., factory-calibrated) sensitivity M(t)pro and the percentage sensitivity change (Z) per degree Celsius. The temperature difference (ΔT) is given by, for example, the subcutaneous temperature at time t, T, as shown in equation [2] below. subcu (t) and the reference temperature, T subcu,reference It can be determined as the difference between [the two conditions].

[0433]

number

[0434] Reference temperature, T subcu,reference This could be, for example, an average or a predetermined subcutaneous temperature value. The compensated analyte sensitivity can be determined by solving equations such as the following [3].

[0435]

number

[0436] The value of Z can be determined from bench tests for a particular sensor configuration. In some examples, Z can be modeled as a function of time Z(t), where t can be measured from the start of the sensor session. Equation [3] gives M(t) comp Solving for this, we obtain, for example, a compensated analyte sensitivity as given by equation [4].

[0437]

number

[0438] Temperature-compensated analyte sensitivity, M(t) comp For example, this can be used to convert raw analyte sensor data into estimated glucose values ​​using the following equation [5].

[0439]

number

[0440] In some cases, the offset can be determined for a particular analyte sensor design configuration, as is routinely done with existing commercially available sensors. In other cases, multiple blood glucose measurements (or, in the case of other analytes, biological samples) may be acquired (e.g., via a user interface) and used to determine the offset for a particular sensor.

[0441] In some cases, the temperature compared to the baseline is the long-term average temperature. In some cases, this is to account for differences in body temperature between hosts (patients). In other cases, real-time compensation for temperature can compensate for temperature-based sensor fluctuations that may be caused by, for example, hot water (e.g., shower), cold water (e.g., swimming), air conditioning, sunlight, heat fluctuations during sleep (e.g., trapped heat from a warm blanket), or exposure to other high or low temperature environments. In some cases, long-term compensation methods and real-time compensation methods can be combined.

[0442] In some cases where the temperature sensor is not subcutaneous, a delay parameter may also be used (in conjunction with a linear model, or a more complex model such as those described below) to compensate for the delay between the detection of temperature changes in the temperature sensor and the actual temperature changes in the analyte sensor. Various exemplary methods for determining subcutaneous temperature based on signals from non-subcutaneous sensors are provided below.

[0443] In some systems, devices, or methods, subcutaneous temperature or other in vivo temperature may be determined (e.g., estimated) using temperature signals from non-subcutaneous temperature sensors, such as temperature sensors in sensor electronic devices of external devices (e.g., transmitters) that may be coupled to subcutaneous analytes (e.g., glucose) sensors. One or more of a variety of methods may be used to determine subcutaneous temperature from temperature signals received from non-subcutaneous temperature sensors.

[0444] In some examples, a linear relationship between non-subcutaneous temperature values ​​and subcutaneous temperature values ​​may be used to approximate subcutaneous temperature. In some examples, a delay parameter may also be used (in conjunction with a linear model, or a more complex model such as those described below) to compensate for the delay between the detection of temperature changes in the temperature sensor and the actual temperature changes in the analyte sensor. In some examples, a nonlinear relationship (e.g., a quadratic equation or a higher-order polynomial or other relationship) may be determined and used to compensate for temperature, and may optionally include a delay parameter. In some examples, the relationship may be determined by solving a differential equation (e.g., a heat transfer relationship) to determine temperature compensation. For example, the sensor system may solve the differential equation whenever an analyte value is needed (e.g., every 5 or 15 minutes) to provide a temperature-compensated analyte value. In another example, a filter based on a differential equation or a given relationship may be applied to compensate for temperature.

[0445] In some cases, subcutaneous temperature or other in vivo temperatures can be determined from non-subcutaneous temperatures using a linear model. The linear model may be developed from, for example, a biothermal model (e.g., Penne's biothermal equation), known host tissue parameters (e.g., typical heat transfer parameters for human skin and subcutaneous tissue), and sensor electronics parameters (e.g., transmitter parameters), the sensor electronics parameters may be determined, for example, by bench testing. Tissue parameters may include, for example, thermal conductivity or heat flux across the tissue.

[0446] Subcutaneous temperature (Tsubcu) can be determined from the measured texternal temperature using a linear equation such as the following equation [6].

[0447]

number

[0448] In the example of equation [6], the gain / gradient (a) and offset (b) may be determined, for example, using empirical data, theoretical data, or model data, or a combination thereof.

[0449] In some cases, when the analyte temperature sensitivity is known, the gain and offset for the above formula can be determined or updated based on the analyte calibration value (e.g., blood glucose level). In other words, if the glucose sensitivity is highly reliable, the temperature can be estimated based on the blood glucose level from a fingertip puncture and the signal received from the glucose sensor. The system can calculate the actual analyte sensitivity using the input glucose value, then determine the subcutaneous temperature from the actual 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 temperature sensor values ​​during calibration (e.g., from a temperature sensor in an external sensor electronic device) to ensure that the updated temperature sensor signal is used when determining the sensitivity, gain, and offset. In some examples, instead of using new gain and offset, weighted average or stochastic models may be used to ensure that the gain and offset are not overly influenced by independent factors that may alter sensitivity, such as a period of inaccuracy after the initial placement of the sensor (e.g., the "dip-and-recovery" phenomenon, where the sensor signal produces a low signal (dip) during an initial "warm-up" period, followed by more accurate (recovered) readings).

[0450] In some examples, the system may account for the delay between the time a temperature change is recorded in a non-subcutaneous temperature sensor and the time when the temperature change actually occurs in a subcutaneous analyte (glucose) sensor. If the analyte sensing system includes a subcutaneous temperature sensor, direct subcutaneous temperature measurements may be used for temperature compensation. However, if the system relies on a non-subcutaneous (e.g., external) temperature sensor, the accuracy of the temperature compensation method may be improved by considering the delayed temperature change in the subcutaneous glucose sensor.

[0451] For example, the linear model described above assumes that subcutaneous temperature matches the external temperature (e.g., sensor electronics or transmitter temperature), but that skin tissue heats and cools much more slowly than the transmitter, and therefore there is a delay between the time the external sensor records the temperature change and the time the change occurs at the subcutaneous location. For example, when a person walks from a cold, air-conditioned room to a warmer place, the external sensor records the temperature change quickly, but it takes much longer for the subcutaneous temperature to rise. In another example, when the host and sensor are immersed in cold water (e.g., a pool, sea, or lake with a temperature lower than the ambient temperature), the temperature drop is first detected by the sensor in the external sensor electronics (e.g., inside a CGM transmitter), and after some time, the temperature at the subcutaneous sensor drops due to heat loss through the sensor or host tissue. The accuracy of subcutaneous temperature estimation can be improved by incorporating this delay to reflect this reality.

[0452] In some cases, delay may be considered in recording temperature changes in a non-subcutaneous temperature sensor, based on other temperature information or a model or estimate of the time delay for a non-subcutaneous sensor to record temperature changes. For example, when an ambient temperature change occurs, especially if the sensor is embedded in a sensor electronic housing where heat must be conducted to record the temperature change, the time required for a non-subcutaneous temperature sensor to record the change may be relatively short (e.g., 1 minute). After some time (e.g., 6 minutes), a subcutaneous temperature change may be observed, and the net delay is the difference between the two readings (e.g., 5 minutes).

[0453] In some examples, a fixed delay may be used. For example, a temperature compensation method may assume a delay period d and compensate for the effect of temperature using the temperature from the previous period based on the assumed delay (e.g., using the temperature at time td). In other examples, compensation may be performed using both the temperature at the current time (t) and the temperature from the previous period based on the assumed delay (e.g., using the temperature at time td). In yet another example, compensation may be performed using multiple temperature measurements from different periods associated with the assumed delay (e.g., using both the temperature at t-d1 and the temperature at t-d2). In some examples, the delay may 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 1 hour (e.g., 30 minutes). In some examples, the delay may be determined based on known information about the host, such as mean body temperature or body mass index.

[0454] In some cases, a variable delay period may be used. In some cases, the variable delay period may be based, for example, at least in part on the variation between the detected temperature and the baseline. In other cases, the delay may be based, at least in part on the difference or rate of change between the detected temperature and the previously detected temperature (for example, a longer delay may be used when a larger temperature change is observed, as it takes longer for the heat transfer process to complete bringing the subcutaneous temperature to a steady state). In some cases, the delay may be implemented only when the temperature change meets a condition, for example, when a sudden temperature change exceeding a threshold occurs (e.g., a change greater than 5°C or 10°C).

[0455] In some cases, the variable delay may be based on a temperature gradient, e.g., the difference between the perceived temperature and the determined subcutaneous temperature. In some cases, the variable delay may be obtained 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, biological processes (e.g., heat transfer via blood flow) may also be taken into account.

[0456] Delays can be calculated or used in various other exemplary methods, such as partial differential equation models, polynomial models, state models, time series models, and models with subgroups or conditions.

[0457] Figure 6 is a flowchart of an exemplary method 600 for determining a temperature-compensated glucose concentration level using a delay parameter. Method 600 may include, in operation 602, receiving a temperature signal indicating a temperature parameter of an external component. The temperature parameter may be, for example, temperature, temperature change, or temperature offset. Detecting the temperature signal may include, for example, measuring the temperature parameter of a component of a wearable glucose sensor. Method 600 may include, in operation 604, receiving a glucose signal indicating an in vivo glucose concentration level. Receiving the glucose signal may include, for example, receiving a glucose signal from a wearable glucose sensor.

[0458] Method 600 may include determining a compensated glucose concentration level based on a glucose signal, a temperature signal, and a delay parameter in operation 606. In some examples, the temperature-compensated sensor sensitivity value may be determined based on the temperature signal and the delay parameter, and the estimated glucose value may be determined using the sensor sensitivity value and the glucose signal. In some examples, a model or neural network may be used to determine the estimated glucose value based (at least partially) on the glucose signal, temperature, and the delay parameter.

[0459] In various examples, the delay parameter may be constant or variable based on information about temperature or the host or other factors, as described above. In some examples, the temperature parameter may be detected at a first time, and the glucose concentration level may be detected at a second time after the first time. The delay parameter may include a delay period between the first time and the second time, taking into account the delay between the first temperature change in the external components and the second temperature change near the glucose sensor. In some examples, determining the compensated glucose concentration level may include executing an instruction 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 may also 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. In some examples, temperature-compensated glucose concentration levels, estimated subcutaneous temperature, or delay parameters (or any combination thereof) can be determined using linear models (e.g., first-order equations), nonlinear models, partial differential equation models, time series models, linear or nonlinear models with subgroups, or any other techniques described herein.

[0460] The method may further include adjusting the delay period in operation 608 based on the rate of temperature change or temperature gradient (or other factors or techniques as described above), or based on detected conditions or states. In some examples, detected conditions or states may include sudden changes in temperature, location, or motion conditions, states, or sessions (e.g., using an accelerometer).

[0461] Optionally, the method may further include, in operation 610, delivering the therapy based at least partially on the compensated glucose concentration level.

[0462] In some cases, subcutaneous temperature can be determined from non-subcutaneous temperature sensor signals using a partial differential equation (PDE) model. The PDE approach to temperature compensation can make the system more accurate, for example, by considering the fact that the rate of temperature change in external electronic devices (e.g., CGM transmitters) is higher than the rate of temperature change in subcutaneous tissue or fluids. Subcutaneous tissue and body fluid temperatures can change more slowly in part, as the body acts as a heat sink. The use of the PDE model may be particularly advantageous in cases of rapid temperature changes.

[0463] In one example, the sensor electronics, subcutaneous sensor, and skin layer can be treated as a multilayer model. The sensor and skin layer (epidermis 260, dermis 262, and subcutaneous tissue 264) are shown in Figure 2C. In one example, the multilayer structure can be considered as a one-dimensional (1D) system, where 1D space is the depth relative to the skin surface. The temperature distribution in space and time can be described by thermal equations such as Penne's biothermal equation, which is described by equation [7] below.

[0464]

number

[0465] The variables and parameters from equation [7] are described by Table 1 below.

[0466] [Table 1]

[0467] The thermal conductivity of each layer in a 1D model can be determined or estimated. For example, the thermal conductivity of each skin layer can be determined by empirical or theoretical methods. The thermal conductivity of sensor electronics (including batteries and epoxy adhesives) can also be determined. Exemplary values ​​for the thermal conductivity of different layers in the sensor electronics, subcutaneous sensors, and skin layer models are given in Table 2 below.

[0468] [Table 2]

[0469] The outer boundary condition (BC) of the exemplary PDE in equation [7] may be set to a time-varying temperature, such as one measured by a non-subcutaneous temperature sensor, while the inner BC may be set to a constant core body temperature.

[0470] Based on these assumptions, equation [7] or a similar PDE can be solved, and as a result, the temperature at the sensor (e.g., at the working electrode of the sensor) can be estimated. In some cases, equation [7] or a similar PDE can be solved whenever a temperature value is needed. For some values, a lookup table can be developed by solving equation [7] or a similar PDE over a reasonable range of values. The lookup table can be referred to in order to determine an approximate subcutaneous temperature.

[0471] In some cases, a linear correlation between the temperature at a subcutaneous sensor and the temperature at an external sensor can be determined from the PDE model. In some cases, the PDE model can be used to capture transient processes of temperature changes and time lags by performing spatiotemporal filtering.

[0472] The estimated temperature at a subcutaneous sensor can be used to compensate for changes in sensitivity at the subcutaneous sensor. For example, the temperature at the electrochemically reactive 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.

[0473] In some cases, time-series models may 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 cases, temperature-compensated sensitivity may be determined directly, i.e., without estimating subcutaneous temperature.

[0474] For example, a quartic polynomial can be used as a model. For instance, the model given by equation [8] below can be used.

[0475]

number

[0476] [Table 3]

[0477] Model parameters can be determined from empirical datasets, for example, using curve fitting or optimization techniques. After the model parameters have been determined, the model can be used to compensate for temperature variations. For example, the compensated sensitivity can be determined using the following equation [9].

[0478]

number

[0479] In some examples, model parameters may be updated when calibration entries (e.g., based on blood glucose meter data) are available. For example, a time-series model may be converted to a recursive version of the model, and thus the model may be updated in real time when fingertip puncture measurements are available. The values ​​of constants may be determined based on population data and patient-specific data. The values ​​may be, for example, as follows: p1: ~0.0004334, p2: 0.04955, p3: ~2.035, p4: 36.7, p5: ~259.7

[0480] A fourth-degree polynomial is provided as an example, but polynomials of degree 3 or 5 or higher can be used. A higher degree polynomial can lead to greater accuracy in compensation, but may require more time, input data, or processing power to determine and update the model parameters.

[0481] Algorithms or models may be used to determine temperature-compensated analyte sensor values ​​(e.g., glucose concentration levels). In some examples, neural networks, state models (e.g., Hidden Markovs), probabilistic models, or other models may be used to develop temperature-compensated models. Models may be trained for specific subjects (e.g., patients) based on data from subjects, and the models may be used to determine compensated estimated glucose values. In some examples, models may be trained from data from a population of patients (e.g., clinical trial data), and the models may be used for a population of patients. In some examples, the same model may be used for almost or all patients (subject to exclusion criteria). In some examples, patients may be matched against models developed from similar patient populations (e.g., based on mean body temperature, age, sex, BMI, or other factors). Inputs to the model may include temperature measurements, time, sensor sensitivity, estimated glucose values, insulin sensitivity, accelerometer data (e.g., to detect activity or posture), heart rate, respiratory rate, dietary status, food intake or type, insulin onboard or insulin delivery amount or pattern, body mass index (BMI), or other factors. The output from the model may include sensor sensitivity, local glucose level, compartment bias value, and non-enzymatic bias level (any of these may be combined to determine the glucose concentration level), or the model may output a compensated glucose / analyte concentration value. Model-based methods may be particularly effective because the effects of various temperatures (e.g., sensor sensitivity, local glucose level, compartment bias value, non-enzymatic bias level) may be linear, nonlinear, or dynamic (e.g., dependent on a combination of both time and temperature).

[0482] Temperature compensation systems may consider long-term averaged temperature or trends. For example, long-term averaging may be used to compensate for temperature fluctuations. Long-term averaging may, for example, consider fluctuations in body temperature or skin temperature between the host and a reference value. In some examples, long-term averaging methods may be used in conjunction with one or more short-term (e.g., real-time) temperature compensation methods described below.

[0483] An individual's average subcutaneous temperature can be determined and updated in several different ways. For example, subcutaneous temperature can be determined as an average over the entire sensor session (e.g., mean or median) or as an average over a rolling window (e.g., the previous 12 or 24 hours). In some cases, subcutaneous temperature can be updated at intervals, for example, every 6, 9, 12, 18, or 24 hours. In some cases, subcutaneous temperature can be determined as a weighted average, with more recent values ​​(e.g., the previous 6, 12, or 24 hours) being weighted more heavily, and past intervals being weighted less heavily.

[0484] In one example, the temperature sensor may first be calibrated against an initial baseline value (e.g., 35°C) that could represent the average temperature of the population. During the learning period, the temperature sensor may determine the actual temperature of the host. The learning period may be chosen to be long enough (e.g., 6–12 hours) to exclude temperature deviations (e.g., so that the mean is not determined during high / low temperature events such as showers). The learned mean may be used to compensate for host temperatures that differ from the population mean. For example, if the population is assumed to have an operating temperature of 35.0°C, but the temperature detected from a particular host averages 35.5°C, a 1 / 2 degree variation may be used to compensate for the analyte value. In some examples, the initial mean may be determined (e.g., on the first day), and the assumed mean may be updated with subsequent temperature measurements (e.g., using the average temperature on the second day, or the average temperature over two days). Other time windows may also be used, as described above.

[0485] If the system has a subcutaneous temperature sensor, a series of temperature measurements may be obtained from the subcutaneous temperature sensor and used to determine the long-term average. In other examples, the subcutaneous temperature may be determined based on the sensed non-subcutaneous temperature (e.g., based on a linear relationship or a higher-level relationship) using one of the various methods described below. After the subcutaneous temperature (Tsubcu,ind) for an individual is established, the temperature-corrected analyte sensitivity may be determined based on the deviation from the reference temperature (Tsubcu,reference) using, for example, the formula provided above.

[0486] In some examples, the rate of change of the estimated glucose value or the rate of change of the signal from the glucose sensor may be used as input to determine temperature compensation. For example, when the rate of change meets a condition (e.g., exceeds a specified value), temperature compensation may be paused or shifted to a different model. In some examples, when the rate of change of the estimated glucose value meets a condition, an exception that can be addressed may be triggered, for example, as described herein with respect to Figure 34.

[0487] For some subcutaneous glucose sensors, the glucose concentration level determined by the subcutaneous sensor reflects a time delay relative to blood glucose levels due to the physiological delay in changes in interstitial fluid glucose levels compared to changes in blood (e.g., it may take several minutes for changes in blood glucose levels to be reflected in the interstitial fluid measured by the subcutaneous glucose sensor). The delay can also be introduced by the periodicity of sensor readings (e.g., if sensor readings are taken every 5 minutes, the estimated glucose level may be 4+ minutes old at some point in the cycle). In the case of time lag errors present in the system during periods of rapid glucose change, temperature compensation may be performed for inaccurate (old) estimates of glucose. In some cases, temperature compensation for old glucose levels may worsen the estimate, and therefore it may be useful to pause or modify temperature compensation during periods of high rate of change. For example, when glucose concentration levels are rapidly decreasing (e.g., due to strenuous exercise), the estimate from the subcutaneous temperature sensor may "lag" behind the blood glucose concentration level (e.g., as determined by a blood glucose meter), and therefore the subcutaneous sensor may show a higher estimated glucose value than the blood glucose level. This discrepancy can be exacerbated if temperature compensation increases the estimated blood glucose concentration level from the subcutaneous sensor. This can be avoided by pausing temperature compensation or shifting to a different model. In some cases, when high rate of change conditions are met, temperature compensation may only be applied when it increases the rate of change (e.g., to avoid exacerbation of the discrepancy caused by physiological delay).

[0488] In some examples, the deviation of the analyte sensor output from the temperature sensor output may be used to evaluate the signal from the temperature sensor. These correlations or deviations may be used to establish confidence in the temperature signal, the analyte sensor signal, or both. Temperature and glucose concentration levels may be expected to correlate during the time that glucose levels meet stability conditions. Stability conditions may be determined, for example, based on the rate of change of glucose concentration levels. In some examples, stability conditions may include several auxiliary conditions, such as short-term and long-term conditions. For example, glucose levels may be considered stable when the rate of change and / or the average rate of change over a specified period meets the conditions (e.g., not increasing or decreasing by more than 1 mg / dL per minute and / or not increasing or decreasing by more than 15 mg / dL in 15 minutes). Glucose levels may be considered moderately stable (e.g., showing a moderate rate of increase or decrease) if the rate of change and / or average rate of change or the specified period meets the conditions (e.g., glucose levels rise (or fall) by 1–2 mg / dL per minute and / or rise (or fall) by 15–30 mg / dL in 15 minutes).

[0489] As illustrated in Figures 15A and 15C, the slopes of the temperature and glucose curves should correlate when glucose levels are stable (or, in some cases, moderately stable). This is because changes in the glucose curve reflect temperature-induced fluctuations in the output of the analyte sensor. Figure 15A shows the output of the glucose sensor plotted against time. The gain in mg / dL is relatively high, showing the fluctuation of the slope over periods of relative glucose stability. Figure 15B shows the output of the temperature sensor plotted against time. Figure 15C shows the superposition of temperature over glucose sensor output (i.e., a combination of Figures 15A and 15B). The analyte sensor output correlates with the temperature sensor output. The analyte sensor has an upward value (positive slope) when the temperature sensor is rising, the analyte sensor has a downward value (negative slope) when the temperature sensor output is falling, and the analyte sensor value is flat when the temperature sensor output is flat. The reliability of the temperature signal can be inferred from this correlation. In contrast, Figure 15D shows an example where the temperature sensor output (dotted line) does not correlate well with the glucose sensor output during periods of relatively stable glucose values, suggesting that the temperature sensor output may be unreliable.

[0490] In various cases, when the reliability of the temperature sensor output is low, temperature compensation may be paused, reduced, or modified, or other information (e.g., motion detection, as described below) may be used or requested to improve the accuracy of temperature compensation. For example, if the reliability of the temperature signal is below a threshold, the analyte sensor may set an exception flag, which may be dealt with as described herein with respect to Figure 34.

[0491] Figure 7 is a flowchart of an exemplary method 700 for determining a temperature-compensated glucose concentration level based on an evaluated (e.g., verified) temperature value. Method 700 may include receiving a glucose sensor signal in operation 702. For example, the glucose sensor signal may be received from a continuous glucose monitor (CGM).

[0492] Method 700 may include receiving a temperature parameter signal in operation 704. Receiving a temperature parameter signal may include, for example, receiving a signal indicating temperature, temperature change, or temperature offset.

[0493] Method 700 may include receiving a third sensor signal in operation 706. Receiving a third sensor signal may include, for example, receiving a heart rate signal, receiving a blood pressure signal, receiving an activity signal or accelerometer signal (for example, to detect exercise), or receiving a location signal (for example, to infer proximity to a high-temperature or low-temperature environment such as a swimming pool, beach, or air conditioning system). In some examples, receiving a third sensor signal may include receiving temperature information from an ambient temperature sensor. In some examples, receiving a third sensor signal may include receiving information from a wearable device such as a wristwatch. In some examples, receiving a third sensor signal may include receiving temperature information from a physiological temperature sensor, which may be incorporated into, for example, a wristwatch or other wearable device. In some examples, the third signal may include a heart rate signal, a respiration signal, a blood pressure signal, or an activity signal, and exercise conditions or states may be detected from an increase in the heart rate signal, respiration signal, blood pressure signal, or activity signal.

[0494] Method 700 may include, in operation 708, evaluating a temperature parameter signal using a third sensor signal to generate an evaluated temperature parameter signal. In some examples, evaluating a temperature parameter signal may include determining presence at a location with known temperature characteristics. For example, a low-temperature or high-temperature signal may be confirmed by a location signal indicating presence at a location with known ambient temperature characteristics (e.g., a hot or cold environment), such as a swimming pool, beach, air conditioning, or area with known weather characteristics, which may be determined by referring to, for example, a network resource (e.g., a website) or a stored lookup table. In some examples, the method may include determining presence at a location with a water-filled environment, such as a swimming pool or beach. In some examples, evaluating a temperature parameter signal may include determining that a change in the temperature parameter signal is consistent with an exercise session. For example, evaluating a temperature parameter signal may include determining that the temperature parameter signal is consistent with the generation of elevated body temperature due to exercise.

[0495] Method 700 may include determining a temperature-compensated glucose concentration level based on an evaluated temperature parameter signal and a glucose sensor signal in operation 710. In some examples, determining a temperature-compensated glucose concentration level may include applying the temperature parameter signal to an exercise model. In some examples, the method may include using an exercise model (e.g., an outdoor or convective cooling exercise model) when exercise 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 (e.g., in a sensor electronic device) may be paused when the detected temperature decreases but exercise is detected (e.g., when HR or activity increases). This is because, when a patient engages in strenuous exercise outdoors in a cold environment (e.g., running) (e.g., when convective cooling is provided by a fan, or when exercising outdoors in a cold climate environment), the subcutaneous temperature may stabilize or even increase during the exercise session.

[0496] Figure 8 is a schematic diagram of an exemplary method 800 for temperature compensation of a continuous glucose sensor, which includes determining a pattern from temperature information. Method 800 may include determining a pattern from temperature data in operation 802. In some examples, determining a pattern may include determining a pattern of temperature fluctuations, and the method may include compensating glucose concentration levels according to the pattern.

[0497] Method 800 may include, in operation 804, receiving a glucose signal from a continuous glucose sensor indicating a glucose concentration level.

[0498] Method 800 may include determining a temperature-compensated glucose concentration level based at least in part on a glucose signal and a pattern in operation 806. For example, the method may include receiving a temperature parameter, comparing the temperature parameter with a pattern, and determining a temperature-compensated glucose concentration level based at least in part on the comparison. In some examples, the pattern may include a temperature pattern that correlates with a physiological cycle, such as a circadian rhythm. In some examples, Method 800 may include determining whether the temperature parameter is highly reliable based on the comparison with the pattern, and, if the temperature parameter is determined to be highly reliable, using the temperature parameter to temperature-compensate the glucose concentration level.

[0499] In some cases, the degree of compensation can be determined at least partially based on a comparison of temperature parameters with patterns. For example, the degree of compensation may be based on a defined range or confidence interval.

[0500] In some examples, the pattern may be determined by determining a condition or state, and determining the temperature-compensated glucose concentration level may be based at least in part on the determined condition or state. For example, method 800 may further include receiving a temperature parameter, and determining the condition or state may include applying the temperature parameter to a state model. In some examples, determining the condition or state may include applying one or more of the following to a state model: glucose concentration level, carbohydrate sensitivity, time, activity, heart rate, respiratory rate, posture, insulin delivery amount, meal time, or meal volume. In some examples, determining the condition or state may include determining an exercise condition or state, and the method may include adjusting a temperature-compensated model based on the exercise condition or state.

[0501] In some cases, the model may be selected or modified based on the detected conditions or states. For example, a group of different linear models may be developed, and the model may be selected from the group based on the detected conditions or states, such as an analyte sensor or host. In some cases, the conditions or states may be determined using a state model.

[0502] In some examples, conditions or states may include location or geographical characteristics. Location may include, for example, geographic location parameters (e.g., longitude, latitude, or altitude), or a city, place, or point of interest (e.g., a beach or a mountain). In various examples, location information, geographical information, or physiological sensor information (e.g., activity or heart rate, as described below) may be collected from the host's smart device, such as a mobile phone, watch, or other wearable sensor.

[0503] In some examples, the conditions or state may include deviations of temperature readings from the mean. For example, the rolling mean temperature and rolling standard deviation may be determined from a series of temperature values, and a model (e.g., a linear model) may be used depending on whether the temperature is +1σ, -1σ, +2σ, -2σ, +3σ, or -3σ from the mean. In some examples, the rolling mean may be determined from a predetermined number of previous temperature values. In various examples, the current reading may be included in or excluded from the rolling mean. In some examples, the rolling mean may be exponentially weighted.

[0504] In some examples, the conditions or status may include patient demographics. For example, demographics may include sex (e.g., using different models for male vs. female hosts / patients), diagnosis (e.g., type 1 diabetes, type 2 diabetes, or non-diabetic), age (e.g., age, or youth, adolescent, adult, elderly), biological cycle (e.g., circadian rhythm or menstrual cycle), and medical condition (e.g., pregnancy, health / illness, or chronic disease).

[0505] In some examples, a condition or state may be determined from wearable or physiological sensors such as a heart rate sensor, accelerometer, blood pressure monitor, or temperature sensor. A condition or state may be determined based on one or more sensor inputs. In some examples, a condition or state may include an activity condition or state, which may be determined, for example, from a heart rate or accelerometer. In some examples, a condition or state may include a wake-sleep state, which may be determined from one or more physiological sensors (e.g., based on biorhythms) or a posture sensor (e.g., a 3-axis accelerometer). In some examples, a condition or state may include a compression state, which may be determined, for example, from a blood pressure sensor or temperature sensor or a combination thereof. For example, when a patient lies on a wearable glucose sensor, as may occur, for example, during sleep, the sensor may produce an inaccurate glucose sensor reading (e.g., "low compression" suggesting a lower glucose value). In some examples, each of these inputs or states may trigger a different temperature relationship (e.g., the application of a specific temperature compensation model).

[0506] In some cases, temperature compensation, or its application (or suspension), may be based at least partially on the rate of change of temperature (e.g., the condition or state may be the rate of change of temperature). Since externally (e.g., sensor electronics) detected temperatures can change much faster than subcutaneous temperature, it may be difficult to accurately predict subcutaneous temperature during periods of rapid temperature change. In some cases, temperature compensation may be suspended or reduced when the detected rate of change of temperature meets a condition (e.g., the condition is met) (e.g., the rate of change exceeds a specified value, the rate of change goes out of range, etc.). In other cases, a first model (e.g., a linear model) may be used when the first condition is met (e.g., the rate of change of temperature is below a specified value), and a second model (e.g., a linear delay model) may be used when the second condition is met (e.g., the rate of change is above a specified value).

[0507] In some examples, temperature compensation, or its application (or suspension), may be based on the magnitude of the temperature gradient, also referred to herein as heat flux. The temperature gradient may be determined (e.g., approximated) from the determined subcutaneous or other intracellular temperature (e.g., previous temperature determination) and the detected non-subcutaneous or extracellular temperature, e.g., the temperature sensed by a sensor electronic device. In one example, when a temperature gradient condition is met (e.g., a temperature gradient exceeding a threshold), the model may be adjusted to reflect, for example, that the external temperature changes faster than the subcutaneous temperature. In one example, the gain in a linear model (as described herein) may be reduced, which may have the effect of reducing the rate of change of the determined subcutaneous temperature in order to more accurately track the actual rate of temperature change. In another example, temperature compensation may be suspended when a temperature gradient or heat flux condition is met. In some examples, temperature compensation, or its application, may be based on the direction of the temperature gradient; for example, temperature compensation when the external temperature is higher than the subcutaneous temperature may differ from temperature compensation when the external temperature is lower than the determined subcutaneous temperature.

[0508] In some cases, the condition or state can be exercise. For example, one or more wearable sensors (e.g., accelerometer, heart rate sensor, respiratory sensor) can be used to determine whether a subject is performing any type of aerobic exercise (e.g., running, cycling, or metabolic conditioning). In one example, it may be assumed that the subject's core body temperature (and subcutaneous temperature) has risen, for example, from 37°C to 38°C. It may also be assumed that during mobile exercise such as running or cycling, the convection coefficient increases (e.g., by 10 times) due to the subject's exercise. An appropriate exercise model that takes these parameter changes into account may be applied. For example, to reflect the effect of exercise, the "gain" (gradient) of a linear model may be increased and the offset (constant) may be changed (e.g., basic linear model: Tsubcu = 0.395). * Texternal+22.346 is equivalent to Tsubcu=0.416 * We may shift to a linear aerobic exercise model like Texternal+22.178.

[0509] In some examples, the amount of temperature compensation applied can be limited when cold temperature and movement are detected. For example, during movement, the transmitter temperature may be lower than when the subject is at rest because, for example, the subject is outdoors, the sensor electronics are exposed to an increased convection due to movement, or the subject's skin becomes cold due to sweating. However, the subcutaneous temperature can rise due to increased heat generation, so a standard temperature compensation model (not considering the combination of movement / cold) can result in inaccuracies. This "cold movement" condition or state can be detected through, for example, a combination of temperature sensor input and accelerometer, heart rate, respiratory rate, or position input. When movement by the subject is detected, the temperature compensation can be modified for low temperature. For example, the temperature compensation can be paused, limited, or tapered, or an alternative compensation model can be applied. In one example, any temperature lower than a threshold during movement can be treated as the threshold for temperature compensation purposes (e.g., sensor temperature < 29 °C is replaced with 29 °C for temperature compensation purposes), or the temperature compensation can be limited by an algorithm. In another example, tapered compensation can be achieved by reducing the temperature sensitivity coefficient (Z) such that a smaller change is added to the subcutaneous temperature for a given detected sensor temperature (e.g., Mt,comp = Mt,pro * (Z) * (Tsubcu - Tsubcu,reference), or Mt,comp = Mt,pro * (Z) * (Tsubcu - Tsubcu,reference)+Mt,pro). For example, if the typical temperature sensitivity coefficient is 3.3%, during movement, the temperature sensitivity coefficient can be changed to 1.5% based on the detected condition or state.

[0510] In some cases, the compensation model may be selected or determined based at least in part on the individual's mean subcutaneous temperature. In one example, the mean subcutaneous temperature may be established during the first few hours of a session (e.g., during or after the warm-up period) or on the first day of the session. The long-term averaging methods described above may be used to determine compensation. In some examples, the mean subcutaneous temperature may be updated periodically or iteratively, for example, every 6 hours, every 12 hours, or every 24 hours.

[0511] In some cases, a determination may be made (e.g., using algorithms, models, or lookup tables) as to whether temperature compensation may increase the accuracy of estimated glucose values. In some patients or under certain conditions, temperature compensation may actually decrease accuracy. Identifying these patients or discriminants and pausing or withholding temperature compensation may improve sensor performance or reduce MARD. Discriminants may include, for example, host surface temperature or body temperature, BMI, sex, age, or any combination of any other conditions or states identified above.

[0512] Figure 9 is a flowchart of an exemplary method 900 for temperature-compensating a continuous glucose monitoring system based at least in part on detected conditions or states. Method 900 may include receiving a glucose signal indicating a glucose concentration level in operation 902.

[0513] Method 900 may include receiving a temperature signal indicating a temperature parameter in operation 904. Method 900 may include detecting a condition or state in operation 906. In some examples, the condition or state may include a high rate of change in the glucose signal, and temperature compensation may be reduced or paused during periods when the glucose signal is receiving a high rate of change. In some examples, the condition or state may include a body mass index (BMI) value. For example, a host with a high BMI may be assumed to warm up naturally or change temperature more slowly than a host with a low BMI. In some examples, the condition or state may include detected heat, and temperature compensation may be reduced, paused, limited, or halted in response to the detection of heat. In some examples, the detected condition or state may include the presence of radiant heat on a continuous glucose monitoring system. In some examples, the condition or state may include detected motion. The method may include, for example, reducing, halting, limiting, or pausing temperature compensation when a condition or state (e.g., motion) is detected.

[0514] In some examples, the glucose signal may be received from a continuous glucose sensor, and the condition or state may include pressure on the continuous glucose sensor. For example, pressure on the sensor may be detected based at least in part on a rapid decrease in the glucose signal. In some examples, the condition or state may include sleep. In some examples, the condition or state may include pressure during sleep. Sleep may be detected using, for example, one or more of temperature, posture, activity, and heart rate, and the method may include applying a specified glucose alert trigger based on the detected sleep.

[0515] Method 900 may include, in operation 908, determining a temperature-compensated glucose concentration level based at least in part on a glucose signal, a temperature signal, and a detected condition or state.

[0516] In some cases, the conditions or states may include sudden changes in the temperature signal. Temperature compensation may be reduced or paused in response to the detection of sudden temperature changes, for example. Sudden temperature changes are unlikely to occur at the analyte sensor site in a subcutaneous location, and temperature changes tend to occur more gradually because heat is conducted through the skin to or away from the sensor site. Therefore, when a sudden temperature change occurs at an external sensor, it may be appropriate to take response actions such as pausing temperature compensation for a period of time, or "gradually changing" the temperature compensation over a period of time to reflect the gradual temperature change at the sensor site.

[0517] After a sudden change in temperature or other rapid change or signal discontinuity is detected, one or more of the following techniques may be used to determine the temperature-compensated glucose level. In some cases, 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 cases, the temperature-compensated glucose concentration level may be determined using an extrapolated temperature signal value based on the previous temperature signal value, and using the extrapolated temperature signal value instead of the temperature signal value associated with the sudden change in temperature. In some cases, a delay model may be invoked in response to the detection of a sudden change in temperature. For example, the delay model may specify a delay period to be used when determining the temperature-compensated glucose level.

[0518] Based on the glucose signal, temperature signal, and detected conditions or states, one or more of the following techniques may be used to determine the temperature-compensated glucose concentration level. For example, a linear model may be used to determine the temperature-compensated glucose concentration level. In another example, a time-series model may be used to determine the temperature-compensated glucose concentration level. In some examples, partial differential equations may be used to determine the temperature-compensated glucose concentration level. In some examples, a stochastic model may be used to determine the temperature-compensated glucose concentration level. For example, a state model may be used to determine the temperature-compensated glucose concentration level.

[0519] In some examples, the method may involve using a temperature signal to determine a long-term average, and the temperature-compensated glucose concentration level may be determined using the long-term average.

[0520] In some examples, the method may further include receiving a blood glucose calibration value and updating the temperature compensation gain and offset when the blood glucose calibration value is received.

[0521] The method may further include delivering insulin therapy. Insulin therapy (e.g., via a pump or smart pen) may be determined at least partially based on temperature-compensated glucose levels.

[0522] In some cases, temperature compensation may be based at least partially on body mass index (BMI). In one example, height and weight may be received from the subject, for example, via the interface of a smartphone application. Temperature compensation parameters may be determined or adjusted based at least partially on BMI. In some cases, 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 specific distance from the subcutaneous layer (where the working electrodes are designed to be located during use) and the tissue at core body temperature. In a person with a high BMI, a thicker layer of adipose tissue (body fat) may increase the distance from the subcutaneous layer to the tissue at core body temperature, thereby resulting in a lower subcutaneous or skin surface temperature. In some cases, a group of models may be available, and the model (e.g., a PDE model with varying distance to core body temperature) and the model itself may be selected from the group based at least partially on the person's BMI. In some cases, BMI does not fully predict adipose tissue thickness, especially at the location of analyte sensors (e.g., CGM), so additional information may be used in addition to BMI to select a model.

[0523] In some cases, temperature compensation models are based at least partially on the subject's core body temperature. For example, since body temperature tends to correlate with BMI, mean body temperature can be estimated based on BMI.

[0524] Other physiological factors or influences, such as local glucose concentration fluctuations (as opposed to systemic glucose levels), compartment bias (differences in glucose concentration between interstitial fluid and blood), and non-enzyme sensor bias, may also be considered in determining compensated analyte values.

[0525] In some cases, sensor signals from an optical sensor having a light source and an optical sensor can be used as input for a temperature compensation method. For example, an optical sensor may be used to detect blood flow or perfusion in a subject's skin. An optical sensor having a light source and a photodetector 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 cases, the amount of exertion (e.g., exercise) can be determined by using blood perfusion information obtained from the optical sensor. For example, when running uphill or downhill, the number of steps may be roughly the same, but uphill running requires more exercise and results in higher blood perfusion. During downhill sections, blood perfusion is lower. Specific exercise detection can be used for more sophisticated temperature compensation algorithms used during exercise. In some cases, an optical sensor may detect less obvious exercise than an accelerometer (e.g., weight training) because the exercise involves less movement or slower movement. In some cases, optical sensors may be used in conjunction with accelerometers to detect motion conditions or states and momentum during movement.

[0526] In some cases, location information (e.g., global positioning sensor data or network connectivity) can be used as input to determine temperature compensation or the reliability of temperature measurements. For example, location can be used to establish the reliability of temperature measurements by comparing them to the temperature characteristics of the location. For example, activities associated with location (e.g., swimming, sunbathing, running, skiing) can establish reliability for low, high, or rapidly changing temperature measurements. In another example, meteorological characteristics at a location (e.g., ambient temperature) can establish reliability for temperature measurements. In yet another example, temperature measurements can be validated using location information correlated with circadian rhythms (e.g., typically sleeping at home location), or deviations from circadian rhythms can be validated by deviations from patterns in location information (e.g., if the subject is away from home, e.g., outdoors at night, camping, or in a location that may have different temperature characteristics, reliability for low nighttime temperatures can be established).

[0527] In some cases, fever detection (e.g., using sensors) or fever reporting (e.g., through an app on a smart device) can be used as input to determine temperature compensation. For example, temperature compensation may be paused during fever because a normal pattern may not apply. In other cases, the model may be modified or a different model may be applied to compensate for temperature changes caused by fever. In some cases, fever can be confirmed using other information. For example, the correlation of the rate of change of sensor outputs, illustrated in Figures 15A-15C, can be used to confirm detected fever. In another case, the patient may be queryed, for example, by a smart device, using a query about fever ("Do you have a fever?") or other events that may cause temperature changes ("Have you recently taken a bath?").

[0528] Figure 19 is a schematic diagram of an exemplary model that may be used to determine an output from two or more inputs. For example, the model may learn patterns or relationships from previous data and apply the learned patterns or relationships in determining the output. This may include, for example, learning from previous data obtained from a specific host or population or from one or more clinical trials.

[0529] In various examples, inputs may be received or sensed simultaneously or at different points in time. In some examples, two inputs (e.g., temperature and analyte sensor output) may be applied to the model. The model may also receive additional inputs such as time (e.g., received from a clock circuit) or sensitivity (e.g., factory-calibrated sensitivity). In one example, the model may include submodels 1902, 1904, and 1906. The submodels may consider temperature-dependent factors such as local glucose levels, compartment bias values, non-enzyme sensor bias levels, and sensor sensitivity. In one example, each model may define different relationships (e.g., linear, nonlinear) between the inputs and temperature-dependent factors. For example, model 1902 may be obtained based on a first nonlinear relationship, model 1904 on a second nonlinear relationship, and the output model may be based on a linear relationship. In various examples, the processor may retrieve model information or input data from a lookup table in memory, or store and retrieve past values ​​or states in memory, or retrieve a function or other aspect of the model from memory. The extracted information can be combined with recent or real-time information and applied to a model to generate an output that may be a compensated glucose concentration level, or the output can be used to determine the compensated glucose concentration level.

[0530] Figure 20A is a flowchart of an exemplary method 2000 for determining a compensated glucose concentration value using a model. Method 2000 may include receiving a temperature sensor signal in operation 2002. For example, the temperature sensor signal may be received from a subcutaneous temperature sensor adjacent to the analyte sensor, or the temperature sensor signal may be received from a non-subcutaneous sensor (e.g., an external sensor electronic device, e.g., on a CGM transmitter). In operation 2004, Method 2000 may include receiving an analyte sensor signal, such as a signal from a glucose sensor. In operation 2006, the temperature sensor signal and the glucose sensor signal may be applied to a 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 examples, multiple temperature sensor signals may be applied to the model. The signals may be processed or analyzed to determine a pattern (e.g., one or more linear or nonlinear trends). A defined or learned relationship between temperature and glucose sensor values ​​and a compensated glucose concentration value may be used to return a compensated glucose concentration value using the model. In operation 2008, the compensated glucose concentration value may optionally be displayed on the user device. In operation 2010, therapy may be delivered based at least partially on the compensated glucose concentration value. For example, the amount of insulin delivered via a pump may be controlled based at least partially on the compensated glucose concentration value. In some examples, a processor may determine the insulin dose, delivery time, or delivery rate (or any combination thereof) based at least partially on the glucose concentration value. In some examples, a pump may automatically deliver insulin, or the pump may suggest insulin time, rate, and dosage to the user. In other examples, a smart pen may receive the compensated glucose concentration value and determine the dose or delivery time, which may be displayed to the user, automatically loaded for delivery, or both.

[0531] In the example in Figure 20A, the model is trained to provide a compensated glucose concentration as its output. In other examples, as described herein, the model is trained to produce an output that includes one or more compensated characteristics of a glucose sensor. For example, temperature compensation may be applied to sensor characteristics to generate one or more compensated sensor characteristics, as described herein. Then, one or more compensated sensor characteristics may 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, etc.

[0532] Figure 20B is a flowchart of another exemplary method 2001 for determining a compensated glucose concentration value using a model. Method 2001 may include receiving a temperature sensor signal in operation 2012. For example, the temperature sensor signal may be received from a subcutaneous temperature sensor adjacent to the glucose sensor, or the temperature sensor signal may be received from a non-subcutaneous sensor (e.g., an external sensor electronic device, e.g., on a CGM transmitter). In operation 2014, Method 2001 may include receiving a glucose sensor signal, such as a signal from the glucose sensor. The glucose sensor signal received in operation 2014 may include, in some examples, a raw sensor signal related to the current at the working electrode of the glucose sensor, such as one or more counts related to the current at the working electrode of the glucose sensor. In some examples, this may include an estimated analyte value, such as glucose concentration, which is derived from the raw sensor signal. In some examples, the glucose sensor signal may include the raw sensor signal and the analyte value.

[0533] In operation 2016, temperature sensor signals and glucose sensor signals can be applied to a model. For example, temperature sensor signals and glucose sensor signals can be applied to a state model (e.g., a Hidden Markov model), a neural network model, or other suitable model. In some examples, 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). Defined or learned relationships between temperature and glucose sensor values ​​and one or more glucose sensor characteristics such as sensitivity and baseline can be used to return values ​​for one or more compensated glucose sensor characteristics.

[0534] In operation 2018, the compensated glucose sensor characteristics are used to generate a compensated glucose concentration. In operation 2020, the compensated glucose concentration value may optionally be displayed on the user device. In operation 2022, therapy may be delivered based at least partially on the compensated glucose concentration value. For example, the amount of insulin delivered via a pump may be controlled based at least partially on the compensated glucose concentration value. In some examples, a processor may determine the insulin dose, delivery time, or delivery rate (or any combination thereof) based at least partially on the glucose concentration value. In some examples, a pump may automatically deliver insulin, or the pump may suggest insulin time, rate, and dosage to the user. In other examples, a smart pen may receive the compensated glucose concentration value and determine the dose or delivery time, which may be displayed to the user, automatically loaded for delivery, or both.

[0535] In some cases, temperature compensation or estimated subcutaneous temperature may be based at least partially on the electrical conductance (or electrical resistance, the reciprocal of conductance) of the analyte sensor or a part thereof. For example, the measured conductance of the analyte sensor 10 or the conductive portion 286 of the analyte sensor shown in Figure 2C may be used for temperature compensation or estimation of subcutaneous temperature.

[0536] Empirical measurements (discussed below and shown in Figure 21) indicate that the conductance of an analyte sensor can be strongly dependent on temperature. In some cases, this relationship between conductance and temperature can be used to estimate subcutaneous temperature, which can then be used in temperature compensation models or other methods. In other cases, the relationship between conductance and temperature can be applied directly (e.g., without using estimated temperature) to compensate for subcutaneous temperature fluctuations.

[0537] Figure 21 plots sensor conductance 2103 and transmitter temperature 2105 against time. A strong correlation is observed between temperature and conductance. As the transmitter temperature increases, the sensor conductance 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 conductance.

[0538] The correlation between temperature and sensor conductance can be used to determine an estimate of the temperature at the working electrode temperature (for example, to determine the subcutaneous temperature in an analyte sensor). In various examples, the system or method may use a non-subcutaneous temperature (e.g., transmitter temperature), or the system or method may compensate without using a non-subcutaneous temperature (for example, as described above, the system may use an assumed reference temperature or a factory-calibrated temperature).

[0539] An initial estimate of the working electrode temperature may be made using one (or more) of various models (e.g., linear models, delay models, partial differential equation models, time series models). The initial estimate may also be based on a predetermined reference value or other methods described herein. This initial estimate may then be used to determine the adjusted temperature using one or more sensor conductance measurements. For example, as the conductance changes, a corresponding temperature change may be calculated, and this temperature change may be applied to the initial temperature estimate or reference temperature to determine the temperature at the time of the sensor conductance measurement (e.g., it may be added to or subtracted from the initial temperature estimate or reference temperature).

[0540] In various examples, conductance-based temperature compensation techniques may be combined with any of the examples described herein to determine an estimated subcutaneous temperature, or the estimated influence of subcutaneous temperature on the signal from an analyte sensor. For example, the estimated subcutaneous temperature (e.g., the temperature at the working electrode of the analyte sensor) may be determined from a non-subcutaneous temperature (e.g., transmitter temperature) measured at a first time, and the conductance of the analyte sensor or a portion thereof may be measured simultaneously with the non-subcutaneous temperature measurement. Subsequently, a second subcutaneous temperature may be estimated based on the difference between a subsequent conductance value (single point or average) and the conductance value (single point or average) from the first time.

[0541] The conductance value 2013 plotted in Figure 21 shows an upward drift over time. This drift component can be related to sensor sensitivity drift, as described in U.S. Patent Application Publication No. 2015 / 0351672, incorporated by reference.

[0542] In some examples, the system may implement one or more techniques to account for drift and avoid or reduce the effects of conductance drift on subcutaneous temperature estimates or compensated data. Such techniques to address drift may include, for example, resetting the temperature estimate (e.g., recalculating the estimated temperature and the conductance baseline used when compensating for future values), or compensation based on mean (e.g., compensation for moving baseline conductance based on long-term mean, weighted mean, or rolling window).

[0543] In various examples, subcutaneous temperature estimates or conductance baselines may be periodically refreshed. For example, a new subcutaneous temperature estimate (e.g., working electrode temperature) may be iteratively (e.g., periodically) refreshed (e.g., reset) by recalculating the estimate (e.g., using the techniques discussed above). Future analyte values ​​may be compensated for conductance values ​​(or mean) that are time-correlated (e.g., simultaneous) with the new subcutaneous temperature estimate. This refresh (reset) of conductance-based temperature estimates may eliminate or reduce the effects of drift components, resulting in more accurate temperature estimates.

[0544] In some cases, reset, refresh, or error statuses may be triggered based on the meeting of conditions. For example, the meeting of conditions may trigger exceptions and responses, as described herein with respect to Figure 34. Conditions may be based on comparative conductance-compensated temperature estimates, including subcutaneous temperature estimates determined in different ways (e.g., not based on conductance), such as transmitter temperature and newly calculated subcutaneous temperature estimates based on a linear model, a delay model, or other models considered herein. Conditions may be met, for example, when two values ​​differ by a larger amount than a set threshold. In some examples, the error status may be changed when the comparison meets an error condition (e.g., an error condition or state may be declared). In some examples, the conductance baseline may be reset (e.g., the baseline may be updated to a new value or mean), or a new temperature estimate may be correlated with a specific conductance value. In some examples, a stepwise approach may be applied such that a reset procedure may be applied when the difference exceeds a reset threshold, and an error condition may be applied when the difference exceeds an error threshold that is greater than the reset threshold (in which case a reset may or may not still occur). Resetting conductance-based temperature estimates in this way may remove or reduce the drift component visible in the conductance signal in Figure 21 (e.g., the conductance value drifts up over time).

[0545] In some cases, a digital high-pass filter may be applied to block low-frequency drift components from the conductance signal, allowing only temperature-related changes to pass through. Filter characteristics, such as the cutoff frequency, may be based on measured temperature data, preferably subcutaneous temperature measurement data (e.g., by frequency analysis such as Fourier decomposition).

[0546] While the above discussion focuses on conductance and resistance, it should be understood that temperature compensation or temperature estimation can, as an alternative, be based on other electrical conductivity characteristics (e.g., impedance or admittance), depending on the configuration of the analyte sensor system and the type of signal applied.

[0547] Figure 22 is a flowchart of an exemplary method 2200 for temperature compensation using conductance or impedance. In operation 2202, a first value representing the conductance of a sensor component at a first time is determined. In operation 2204, a second value representing the conductance of the sensor component thereafter is determined. In operation 2206, a signal representing the host analyte concentration is received. In operation 2208, a compensated analyte value is determined, at least in part, based on a comparison of the second value with the first value. In some examples, determining the first value may include determining the average conductance over a period of time adjacent to or including the first time. In some examples, the method may further 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, the second estimated subcutaneous temperature being determined, at least in part, based on a comparison of the second value with the first value.

[0548] In some examples, the method may include determining a third estimated subcutaneous temperature that is time-correlated with a second value, determining whether a condition is met based on a comparison of the third estimated subcutaneous temperature with the second estimated subcutaneous temperature, and setting an exception in response to the condition being met. In some examples, setting an exception may include setting a flag or other indicator that can be treated as an exception in the manner described herein with respect to Figure 34. For example, an exception may trigger a response action, such as triggering a reset of the analyte sensor system. Triggering a reset of the analyte sensor system may include determining a subsequent estimated subcutaneous temperature based on the third estimated temperature and the second value, or based on a third value indicating subsequent conductance and a fourth estimated subcutaneous temperature that is time-correlated with the third value.

[0549] In some examples, method 2200 may include compensating for drift in the conductance value by, for example, applying the methods described above or by applying a filter.

[0550] Figure 23 is a flowchart of an exemplary method 2300 for determining an estimated subcutaneous temperature using conductance or impedance. In operation 2302, a first value representing the conductance of a sensor component at a first time can be determined, for example, by the measured conductance or impedance of the sensor component. In operation 2304, a second value representing the subsequent conductance of the sensor component can be determined, for example, by making a second measurement to determine the conductance or impedance. In operation 2306, the estimated subcutaneous temperature can be determined at least in part on a comparison of the second value and the first value. As described above, the initial estimated temperature can be determined using non-subcutaneous temperature measurements, and the subsequent estimated subcutaneous temperature can be determined on a change in the value representing conductance. An error condition may be set or a reset may be triggered if the variation exceeds a threshold or otherwise the comparison meets an error condition or reset condition. In some examples, the error condition or reset condition may be treated as an exception when detected, in the manner described herein with respect to Figure 34. It should be understood that any of the estimated temperatures described herein can be used as input for any of the temperature compensation models described herein.

[0551] In some examples, a temperature sensor may be calibrated during a manufacturing step where the process temperature is known or controlled. For example, some sensor electronics packages that use adhesives or structural agents such as epoxy that can be cured at a known or controlled temperature. The temperature sensor may be calibrated during the curing step. In another example, a temperature sensor may be calibrated when an analyte sensor is calibrated. In yet another example, a temperature sensor may be calibrated during the initial period of wear. For example, the temperature sensor output during the initial period (e.g., the first one or two hours after the start of the analyte sensor) may be calibrated to a predetermined average (e.g., 37°C).

[0552] Figure 10 is a schematic diagram of method 1000 for temperature compensating a continuous glucose sensor system using a reference temperature value. The method may include, in operation 1002, determining a first value from a first signal indicating the temperature parameter of a component of the continuous glucose sensor system. The method may include, in operation 1004, receiving a glucose sensor signal indicating the glucose concentration level. The method may include, in operation 1006, comparing the first value with a reference value.

[0553] The method may include, in operation 1008, determining a temperature-compensated glucose level based on a glucose sensor signal and a comparison of the first signal with a reference value.

[0554] In some examples, the method may further include determining a reference value. For example, the reference value may be determined from a first signal. For example, a continuous glucose sensor system may include a glucose sensor that can be inserted into a host, and the reference value may be determined during a specific period after insertion of the glucose sensor into the host, or during a specific period after activation of the glucose sensor. In other examples, the reference value may be determined during the manufacturing process.

[0555] In some examples, the baseline value may be established during a first period, and the first value may be determined during a second period following the first period (for example, the baseline value may be established after sensor insertion, and subsequent sensor readings may be compensated in relation to the baseline value). In some examples, the baseline value may be a long-term average, and the first value may be a short-term average. In some examples, the baseline value may be updated based on subsequently received temperature values. For example, the baseline value may be updated based on one or more temperature signal values ​​acquired during a third period following the second period.

[0556] In some examples, the baseline value may be determined based on the average of multiple sample values ​​obtained from the first signal.

[0557] Figure 11 is a flowchart of an exemplary continuous glucose sensor temperature compensation method 1100. The method may include receiving a calibration value of a temperature signal in operation 1102. In some examples, the calibration value may be obtained during a manufacturing step with a known temperature. In some examples, the calibration value for the temperature signal may be obtained during a specified period after insertion of the continuous glucose sensor into the host. For example, the calibration value may be determined after a warm-up period, which may be, for example, a 2-hour period after sensor insertion or operation. For example, the calibration value may be determined during a subsequent period after the warm-up period (e.g., 2-4 hours after insertion). The method may include receiving a temperature signal from the temperature sensor indicating a temperature parameter in operation 1104. The method may include receiving a glucose signal from the continuous glucose sensor indicating a glucose concentration level in operation 1106. The method may include determining a temperature-compensated glucose concentration level in operation 1108, at least in part, based on the glucose signal, the temperature signal, and the calibration value.

[0558] In some cases, relative temperature fluctuations can be used for temperature compensation. For example, an uncalibrated temperature sensor or a temperature with low absolute accuracy can be used for temperature compensation by basing the compensation on the deviation from a reference, as opposed to knowledge of absolute temperature. This may include, for example, using an individualized dynamic reference temperature (e.g., a reference temperature determined for a particular sensor or session, which may be periodically refreshed or recalculated) and applying compensation using the deviation from that reference temperature.

[0559] In some cases, the temperature difference can be determined from a reference condition or state based on the variation of a first value from a reference value, without calibrating the temperature against a reference value. This can make it possible to compensate for the temperature difference from the reference value, for example, even when the absolute temperature cannot be determined. This may be useful to ensure accurate absolute temperature when the temperature sensor is not factory calibrated, or when using a sensor that has good relative accuracy or precision but low reliability absolute accuracy or precision. In some cases, the temperature-compensated glucose level can be determined at least in part based on a temperature-dependent sensitivity value that varies based on the deviation of a first value from a reference value.

[0560] In some cases, temperature compensation can be performed using temperature sensors with low absolute accuracy. For example, even if the sensor is not accurate in an absolute sense (e.g., a variation of ±3°C or 5°C in absolute temperature), it may be sufficiently accurate in a relative sense (e.g., accurately detecting that the sensor is 1°C warmer than the previous (reference) point in time). The use of these types of sensors may be advantageous because the sensors may be integrated into sensor electronics for other reasons (e.g., to detect overheating), and the calibration steps required may be simpler or less expensive.

[0561] In one example, the reference temperature may be obtained when a blood glucose level (e.g., a blood glucose meter using finger prick) is received. For example, once the blood glucose level is received, the glucose sensitivity may be determined (e.g., calculated) based on the signal from the analyte sensor (glucose sensor), and the signal from the temperature may be obtained (e.g., declared) as the reference temperature. The signal from the temperature sensor may then be used to determine the temperature difference from the reference temperature, and temperature compensation may be based on that difference. For example, the temperature may then be determined to be 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 the raw or processed signal from the temperature sensor, as opposed to a calculated temperature difference.

[0562] In various examples, the reference temperature may be determined during a specific period, for example, during the first two hours or the first 24 hours after the sensor session begins. In one example, the reference temperature may be the average (e.g., mean or median) temperature during a specified period. In some examples, the reference temperature may be used for the remainder of the session. In other examples, the reference temperature may be updated iteratively or periodically. For example, the reference may be updated every 24 hours, and the reference temperature may be used for the following 24 hours. In some examples, for the purpose of temperature compensation, the reference temperature may be assumed to be a specific value (e.g., 35°C, which may be assumed as the mean subcutaneous temperature of the general population of subjects). In some examples, the temperature sensor value at the time of calibration (during manufacturing or after insertion) may be used as the reference value.

[0563] Real-time temperature compensation can be determined using a real-time (or recent) temperature signal and a reference temperature value, using one of the compensation methods described herein (linear compensation, linear compensation with delay, polynomial compensation, etc.). In some examples, temperature compensation using relative temperature can achieve 75% (or more) of the MARD improvement achieved using a calibrated temperature sensor.

[0564] Indicators of movement or condition can be detected and used to determine temperature compensation. Movement may be detected based on, for example, temperature data, accelerometer data (e.g., for detecting walking or running), positional data (e.g., based on presence in a position associated with movement, or based on movement in a position associated with walking, running, or walking), and physiological data (e.g., respiration, heart rate, or skin surface condition).

[0565] In some examples, the method may include detecting an increase in a first temperature signal and a decrease in a second temperature signal, and adjusting a temperature compensation model based on the detected increases and decreases. In some examples, an exercise session (e.g., outdoor exercise or convective cooling exercise) may be detected at least in part based on the detected increases in the first signal and decreases in the second signal. For example, a decrease in the second signal may indicate the start of an exercise session in a low-temperature environment (e.g., outdoors on a cold day, or an exercise session in an actively cooled environment (e.g., near a fan)). A decrease in the temperature signal from an external second sensor (e.g., in sensor electronic equipment) may indicate a decrease in temperature in response to an outdoor ambient temperature being lower than an indoor ambient temperature, or a decrease in temperature in response to convective cooling (e.g., running or biking, or from a fan adjacent to a treadmill or other training space). For example, a rise in temperature (or steady temperature) in the first temperature signal, which may be received from an external sensor positioned closer to the body than the second sensor, or from a subcutaneous sensor (e.g., on or incorporated into a glucose sensor), may indicate that there is no decrease in temperature despite changes in ambient temperature, due to body warming or heat generation caused by exercise.

[0566] Figure 12 is a flowchart of an exemplary method 1200 of temperature compensation using two temperature sensors. Method 1200 may be implemented, for example, in the system shown in Figure 2C. In operation 1202, method 1200 may include receiving a glucose signal from a glucose sensor representing the host's glucose concentration level.

[0567] Method 1200 may include, in operation 1204, receiving a first temperature signal indicating a first temperature parameter in proximity to a host or glucose sensor. Method 1200 may include, in operation 1206, receiving a second temperature signal indicating a second temperature parameter. In some examples, the first temperature signal may be received from a first temperature sensor coupled to a glucose sensor, and the second temperature signal may be received from a second temperature sensor coupled to a glucose sensor.

[0568] Method 1200 may include determining a compensated glucose concentration level in operation 1208 based at least in part on a glucose signal, a first temperature signal, and a second temperature signal. In some examples, the compensated glucose concentration level may be determined based at least in part on a temperature gradient between the first and second temperature sensors, or at least in part on a heat flux between the first and second temperature sensors. In some examples, Method 1200 may include detecting an exercise session based on two temperature signals (e.g., based on the difference in detected temperatures) and compensating accordingly (e.g., by applying an exercise model).

[0569] In some examples, method 1200 may further include determining, at least in part, that a temperature change is due to radiant heat or ambient heat, and adjusting the temperature compensation model based on the determination. For example, if the second temperature signal is from a sensor near the outer surface of a 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, and a slower rate of change may indicate exposure to radiant heat. In some examples, the state model may include one or more of the following: radiant heat state, immersion state, motion state, ambient air temperature state, or ambient water temperature state, and the state model may be used for temperature compensation of the estimated glucose value.

[0570] Temperature sensors can be used for a variety of other purposes. In some cases, BMI can be estimated from temperature. For example, lower temperatures tend to correlate with higher BMI. Estimated BMI values ​​can be shared with other applications. For example, decision support systems may use BMI as input for a model or algorithm to determine guidance for a subject (e.g., glucose correction dose, exercise recommendations, or the amount or type of carbohydrates or food to eat).

[0571] In some cases, an alarm or alert may be triggered when a temperature sensor indicates a temperature that meets a specific condition. For example, an alarm or alert may be triggered when a temperature sensor indicates a temperature that meets a statistical condition (e.g., the temperature deviates by more than one standard deviation from the mean or baseline, or deviates by a specified number of standard deviations from the mean or baseline). For example, a potentially dangerous or hazardous condition or state for a patient (e.g., severe fever, heatstroke, hypothermia) may be detected using a subcutaneous temperature sensor or a temperature sensor in a sensor electronic device, and the condition or state may be communicated via an alarm or alert (e.g., via the target smart device or communicated to the caregiver's smart device via a wireless network or the internet). In other cases, a potentially overheated or excessively cold sensor or sensor electronic device may be detected. In some cases, a potentially faulty temperature sensor may be identified based on a temperature sensor signal that meets a specific condition (e.g., when the temperature sensor indicates a temperature within an unlikely range).

[0572] In various examples, temperature compensation as described herein may be used in conjunction with analyte sensors for measuring analytes other than glucose. Temperature compensation techniques may be used with analyte sensors for measuring any analyte, including the exemplary analytes described herein.

[0573] Furthermore, in some cases, temperature measured by a subcutaneous temperature sensor, or temperature measured using a temperature sensor in a sensor electronic device as described herein, may be used to determine the recommended insulin dosage. For example, the host body may utilize insulin differently depending on temperature. Temperature-related adjustments may be made to the host's insulin dose based on the measured temperature.

[0574] Sensor disconnection or reuse of a disposable sensor ("restart") can be detected, at least partially, on the basis of a temperature change or the absence of a temperature change. Some analyte-based sensor systems may consist of disposable (replaceable) sensor components and a reusable sensor electronics package, e.g., a CGM transmitter, which can be mechanically and electrically coupled to the disposable sensor components. The disposable sensor components may be designed to extend into the subcutaneous layer of the host and function for several days (e.g., 7, 10, or 14 days), after which the disposable sensor components are removed and replaced with new disposable sensor components. As described in detail in the discussion in Figure 1, the reusable transmitter may be wirelessly coupled to a control device (e.g., a smart device), which may include a user interface for inputting commands that may be sent to the transmitter. The user interface on the control device may allow stopping a sensor session and starting a new sensor session.

[0575] A sensor session can be programmed for a defined period (e.g., 7 days), after which the session expires (unless manually stopped via the user interface). After a sensor session expires or is stopped, a new session can be started via the user interface.

[0576] In some cases, a subject (e.g., a patient) may initiate a new sensor session without replacing the disposable sensor components; that is, the subject may "restart" the session using the same disposable components that were used before the session was terminated. For various reasons, detecting such restart events may be useful.

[0577] Sensor "restart" can be detected at least partially based on signals from the temperature sensor within the sensor electronics package (e.g., a CGM transmitter). For example, if a subject intends to reuse a disposable sensor component, the subject typically stops the sensor session and starts a new session without removing the transmitter from the disposable component. This "restart" scenario can be detected by the absence of a temperature signature associated with the removal of the transmitter from the sensor.

[0578] When a transmitter is removed from a host and reconnected to a new sensor, a temperature signature, including a temperature drop, is observable if the sensor electronics are away from the host for a sufficient period of time (e.g., 1 minute). Figure 18A is a temperature-versus-time plot, where the sensor electronics package (Dexcom CGM transmitter) was removed from the sensor (Dexcom glucose sensor) for 1 minute at 1:27 PM. A temperature drop 1802 is visible in the temperature plot. Figure 18B is a similar graph where the sensor electronics package was removed for 5 minutes at 3:34 PM. A larger temperature drop 1804 is visible in the temperature plot, and it takes longer than 30 minutes for the sensor to return to the steady-state temperature 1806 (approximately 33°C) detected before the change.

[0579] In various examples, disconnection events (e.g., removing a CGM transmitter from a sensor) can be identified based on the amount of temperature drop (e.g., 3°C or 5°C over a short period), the gradient of the drop, the consistency of the signal during the drop (lack of smoothness or variability), or a combination thereof.

[0580] Sensor restart can be identified by the absence of a disconnection event before or after the session stop or start. In some cases, a disconnection event can be determined from a combination of a temperature signature (e.g., a temperature drop) and other information such as the termination of a sensor session. For example, if a temperature signature associated with a disconnection occurs immediately after (or immediately before) the session ends, it can be inferred that the sensor electronics were removed from the disposable sensor. If a sensor session is stopped and started, but there is no temperature drop as described above and illustrated in Figures 18A and 18B, it can be inferred that the disposable sensor was reused. This is because changing to a new sensor requires removing the sensor electronics (CGM transmitter) from the sensor. In some cases, sensor removal can be determined from a combination of a temperature signature and accelerometer data (e.g., rapid or large movement that may occur during transmitter disconnection and is followed by a temperature drop) or other sensor data.

[0581] Figure 13 is a flowchart of an exemplary method 1300 for determining that a continuous glucose (or other analyte) monitor has been restarted. Method 1300 may include, in operation 1302, receiving a temperature signal from a temperature sensor on the continuous glucose monitor indicating a temperature parameter. Method 1300 may further include, in operation 1304, determining from the temperature signal that the continuous glucose monitor has been restarted. For example, as described above, a restart may be identified by the absence of a disconnection event in the temperature signature, along with optionally other sensor information. When it is detected that the continuous glucose monitor has been restarted, a response action may be performed, for example, as described herein with respect to Figure 34. For example, the sensor system may set a flag indicating that the continuous glucose monitor has been restarted. In some examples, when a session restart is detected, the sensor system may terminate the restarted session.

[0582] Restarting can also be detected using a subcutaneous temperature sensor. When the temperature sensor is on a subcutaneous analyte sensor, the temperature reading from the sensor is typically lower than body temperature (e.g., closer to ambient air temperature) when the sensor is first inserted, and the detected temperature can be expected to gradually rise to body temperature as the sensor absorbs heat from the body. In one example, determining from the temperature signal that a continuous glucose monitor has been restarted may involve comparing a first temperature signal value before sensor initiation with a second temperature signal value after sensor initiation, and declaring that the continuous glucose monitor has been restarted when the comparison meets similar conditions. Similar conditions may include a temperature range. For example, when a sensor is restarted (as opposed to replacement), the temperature at the subcutaneous sensor is typically unchanged or any change is gradual. When a sensor is replaced, a more significant temperature change may occur (e.g., a new sensor may show a different temperature than the old sensor).

[0583] In some cases, temperature information may be used to determine the anatomical location, or type of anatomical location, where a sensor is attached. For example, a sensor may be attached to the arm or abdomen. A sensor (or sensor electronic device) may experience lower temperatures when attached to the arm compared to the abdomen. This may be driven by, for example, the fact that the upper arm is further away from the torso, or the fact that the arm is more likely to be exposed to air, for example, when wearing short-sleeved clothing. A sensor attached to the arm may also experience more drastic temperature fluctuations, particularly during sleep (for example, when the arm is more likely to be outside the sheets or blankets than the abdomen, at least for part of the night). In some cases, the anatomical location may be determined based on the average (e.g., mean or median) temperature over a specified period (e.g., during the first 24 hours after attachment). For example, a sensor device location may be declared to be on 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, the abdominal sensor position may be detected based on a first standard deviation of temperature fluctuations during a specified period (e.g., nighttime or sleep period) that is below a specified amount (e.g., less than 1°C). In some examples, the abdominal position may be detected based on a combination of temperature and fluctuation conditions; for example, the abdominal position may be declared when the mean temperature exceeds a specified temperature threshold (e.g., 32°C) or when the first standard deviation of temperature fluctuations during a specified period is below a specified amount (e.g., less than 1°C).

[0584] Figure 16 is a graph showing temperature (y-axis) versus time (x-axis) for two sensors. The first plot 1602 (dotted line) shows data from a sensor placed on the abdomen. The second plot 1604 (solid line) shows data from a sensor placed on the arm. For the first four hours, the sensors are not attached to the host (e.g., not yet inserted), and the data from the sensors are roughly correlated. After four hours, the sensors are inserted into the host, and the temperature rises rapidly. After this transition, the variation between the first plot 1602 and the second plot 1604 is clear, with the second plot 1604 (corresponding to the arm-mounted sensor) showing lower temperatures and more volatile fluctuations.

[0585] Figure 17 shows a plot of standard deviation versus mean temperature over the first 24 hours for several dozen sensor devices. Using the method discussed above (SD > 1.0 and mean temperature < 32°C), sensor devices located on the arm were identified with high sensitivity (all but five arm-mounted sensors were identified in this way) and good specificity (only six abdominal-mounted sensors were identified as being on the arm according to the method). In some examples, the exemplary temperature method may be combined with information from other sensors (e.g., accelerometer data) to further enhance sensitivity and specificity. In some examples, a trained model (e.g., using a neural network) may be used to identify patterns or relationships, and the model may be applied to determine location. Such approaches may achieve higher sensitivity or specificity. While specific “arm” and “abdominal” locations are shown, other locations or classes may be used (e.g., waist locations may be determined, or “torso” locations may include both abdomen and waist).

[0586] Figure 14 is a flowchart of an exemplary method 1400 for determining the anatomical location of a sensor. The method may include, in operation 1402, receiving a temperature signal indicating the temperature of components of a continuous glucose sensor on a host. The method may include, in operation 1404, determining the 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 at least in part on fluctuations in the temperature signal. For example, larger temperature fluctuations may be observed in sensors inserted in peripheral locations (e.g., arms) or in locations less likely to be covered by clothing, rather than in sensors inserted on the abdomen or lumbar region.

[0587] In some examples, the method may further include receiving accelerometer signals, and determining the anatomical location may include determining the anatomical location based on the accelerometer signals. For example, higher activity levels or more frequent postural changes (both of which can be determined from accelerometer signals) may indicate peripheral locations (e.g., the back of the arm), while lower activity levels, less frequent postural changes, or more periodic postural changes (e.g., correlated with sleep or sitting) may indicate abdominal or lumbar locations. In some examples, a distribution of rate of change of location may be used to identify anatomical locations. For example, a distribution biased towards higher rates of change may suggest peripheral locations (e.g., on the arm), while a distribution biased towards lower rates of change may suggest locations on the torso (e.g., the abdomen). In other examples, a neural network or other trained model may be used to learn patterns or relationships that can be used to determine or predict anatomical locations (e.g., using sensor data and optionally based on user input data indicating a specified anatomical location).

[0588] In operation 1406, in some examples, temperature compensation may be based at least partially on anatomical location. For example, the temperature compensation algorithm may take into account the fact that subcutaneous temperature in the abdomen or lumbar region may change more slowly than subcutaneous temperature in the arm, which may have a lower mass to act as a heat sink or heat source.

[0589] In some cases, compression can be detected based at least partially on the signal from the temperature sensor. Sensor compression may occur, for example, when a person lies down or leans on the sensor, which may occur, for example, during sleep. When a glucose sensor is compressed, it may produce an estimated glucose value lower than the actual value. When a subject lies down on a glucose sensor, the sensor's temperature may rise. Sensor compression can be detected at least partially on the rise in the sensor's temperature. In one example, a rapid drop in glucose level occurring simultaneously with or preceding a rise in temperature may indicate that the sensor is being compressed. In some cases, additional information, such as activity information, may be used in conjunction with temperature. For example, a rapid drop in estimated glucose combined with low activity (suggesting the subject is not exercising) and a rise in sensor temperature (suggesting the subject is lying down on the sensor) may indicate a decrease in compression. In some cases, an alert may be triggered in response to a possible decrease in compression. For example, a notification may be delivered via a smart device, or an audio signal may be emitted from the smart device or sensor, prompting the subject to move away from the sensor to allow for the acquisition of an accurate estimated glucose value.

[0590] In some cases, sleep may be detected based at least partially on temperature sensor information. For example, warmer temperatures may be observed during sleep. More consistent temperatures or temperature patterns may be observed during sleep. Sleep may be detected by applying a model or algorithm that detects periods of warmer temperatures, consistent temperatures, or temperature patterns (e.g., binary patterns corresponding to covered or uncovered arm sensors) along with other sensor information, at an optional rate. In some cases, temperature information may be used in conjunction with posture information, activity information, respiration, or heart rate from a 3D accelerometer, or any combination thereof, to detect sleep. In some cases, alert behavior may be modified in response to sleep detection. For example, alert thresholds may be adjusted to reduce the number of alerts during sleep, or alert triggers may be adjusted to provide time to process hypoglycemic events, or only certain types of alerts (e.g., more urgent alerts) may generate sound when sleep is detected.

[0591] In some cases, compression detection, compensation, or alerts may be provided or modified during sleep. For example, when a person is sleeping and an estimated glucose level suddenly drops rapidly, compression may be inferred based on a combination of sleep conditions or state and the sudden drop in estimated glucose level, along with other information, optionally such as discontinuities in the glucose curve, rising temperature, or other information.

[0592] In some cases, it is desirable to reduce the cost of the hardware included in an analyte sensor system, such as the analyte sensor system 8 in Figure 1. 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 used in a sensor session lasting several days and then discarded. Therefore, it may be desirable to obtain very accurate temperature values ​​from an inexpensive temperature sensor.

[0593] The various examples described herein relate to systems and methods that utilize a trained temperature compensation model to generate compensated temperature values ​​from a system temperature sensor. In some examples, the trained temperature compensation model can compensate for factors that cause errors in raw temperature data, such as noise or other nonlinearities. As described herein, utilizing a trained model to compensate for temperature values ​​from a system temperature sensor allows for the generation of acceptablely accurate temperature values ​​using less expensive or more readily available system temperature sensors. For example, as described herein, using a trained model may, in some examples, enable the use of less expensive or more readily available temperature sensors, such as sensors included with or generated from suitable diodes in application-specific integrated circuits (ASICs) or other components of the analyte sensor system 8.

[0594] The temperature compensation model can be any suitable type of model, including, for example, a neural network, a state model, or any other suitable trained model. The input to the temperature compensation model may include, for example, raw temperature data and uncompensated temperature data. Raw temperature data may include, for example, data generated by a system temperature sensor to indicate temperature, such as current, voltage, or count. Uncompensated temperature data may include data indicating uncompensated temperature. For example, in some examples, a temperature sensor provides temperature data derived from raw temperature data. In some examples, the input to the temperature compensation model may include both raw temperature data and uncompensated temperature data. In some examples, the output of the temperature compensation model may include compensated ...

Claims

1. An analyte sensor system for generating estimated analyte values, External temperature sensor and, In vivo analyte sensor, A system comprising at least one processor programmed to perform an operation, wherein the operation is Accessing the first sensor signal from the aforementioned in vivo analyte sensor, Accessing the first temperature signal from the external temperature sensor, Determining that the first temperature signal satisfies the temperature signal conditions, After determining that the first temperature signal satisfies the temperature signal conditions, the first analyte sensor temperature is generated based at least partially on the first temperature signal. By determining that the temperature of the first analyte sensor is within the first temperature range, it is determined that the temperature of the first analyte sensor satisfies the temperature conditions at least partially. To generate a first temperature-compensated sensitivity based at least partially on the temperature of the first analyte sensor, An analyte sensor system comprising generating a first estimated analyte value based at least in part on the first sensor signal and the first temperature-compensated sensitivity.

2. Determining that the temperature of the first analyte sensor satisfies the temperature conditions is Using the first analyte sensor temperature and at least one previous analyte sensor temperature indicated by the previous temperature signal, a rate of temperature change is generated. The analyte sensor system according to claim 1, further comprising determining that the rate of temperature change satisfies the rate of change condition.

3. The aforementioned operation, Using the first temperature signal, a first in vitro temperature is generated, The analyte sensor system according to claim 1, further comprising generating the first analyte sensor temperature using the first in vivo temperature.

4. The analyte sensor system according to claim 3, wherein determining that the first extracorporeal temperature indicated by the first temperature signal satisfies the temperature condition includes determining that the difference between the first extracorporeal temperature and the first analyte sensor temperature is less than a threshold.

5. The aforementioned operation, It is determined that the temperature of the second analyte sensor, indicated by the second temperature signal from the external temperature sensor, does not satisfy the temperature conditions. The analyte sensor system according to claim 1, further comprising: performing a response action in response to determining that the second analyte sensor temperature, indicated by the second temperature signal, does not satisfy the temperature condition.

6. The analyte sensor system according to claim 1, further comprising determining that the first temperature signal satisfies the temperature signal condition before the operation generates the first temperature-compensated sensitivity.

7. A glucose sensor system for generating estimated glucose values, External temperature sensor and, In vivo analyte sensor, A system comprising at least one processor programmed to perform an operation, wherein the operation is Accessing the first sensor signal from an in vivo glucose sensor, Accessing the first temperature signal from the external temperature sensor, To generate a first glucose sensor temperature based at least partially on the first temperature signal, To generate a first temperature-compensated sensitivity based at least partially on the temperature of the first glucose sensor, To generate a first temperature-compensated non-glucose signal based at least partially on the temperature of the first glucose sensor, A glucose sensor system comprising generating a first estimated glucose value based at least in part on the first sensor signal, the first temperature-compensated sensitivity, and the first temperature-compensated non-glucose signal.

8. The aforementioned operation, Accessing a second sensor signal from an in vivo glucose sensor, Accessing the second temperature signal from the aforementioned external temperature sensor, A second glucose sensor temperature is generated based at least partially on the second temperature signal, The second glucose sensor temperature is determined to not meet the temperature conditions, To generate a second temperature-compensated non-glucose signal based at least partially on the default temperature, The glucose sensor system according to claim 7, further comprising generating a second estimated glucose value based at least in part on the second sensor signal and the second temperature-compensated non-glucose signal.

9. To generate the first temperature-compensated non-glucose signal, The difference between the first glucose sensor temperature and the reference glucose sensor temperature is determined, The glucose sensor system according to claim 7, further comprising applying a non-glucose compensation coefficient to the difference.

10. The glucose sensor system according to claim 7, wherein the operation further includes determining that the first glucose sensor temperature satisfies a temperature condition before generating the first temperature-compensated sensitivity, and determining that the first glucose sensor temperature satisfies the temperature condition includes determining that the first glucose sensor temperature is within a first temperature range.

11. The aforementioned operation, The process further includes determining whether the temperature of the first glucose sensor satisfies the temperature conditions before generating the first temperature-compensated sensitivity, and determining whether the temperature of the first glucose sensor satisfies the temperature conditions Using the first glucose sensor temperature and at least one previous glucose sensor temperature indicated by the previous temperature signal, a rate of temperature change is generated. The glucose sensor system according to claim 7, further comprising determining that the rate of temperature change does not exceed a rate of change condition.

12. The aforementioned operation, Before generating the first temperature-compensated sensitivity, Determining that the temperature of the first glucose sensor satisfies the temperature conditions, Using the first temperature signal, a first in vitro temperature is generated, The glucose sensor system according to claim 7, further comprising generating the first glucose sensor temperature using the first extracorporeal temperature.

13. The aforementioned operation, Accessing a second sensor signal from the aforementioned in vivo glucose sensor, Accessing the second temperature signal from the aforementioned external temperature sensor, A second glucose sensor temperature is generated based at least partially on the second temperature signal, The second glucose sensor temperature is determined to not meet the temperature conditions, The glucose sensor system according to claim 7, further comprising performing a response action in response to determining that the temperature of the second glucose sensor exceeds the temperature condition.

14. The aforementioned response action is, To generate a second temperature-compensated sensitivity based at least partially on the default glucose sensor temperature, The glucose sensor system according to claim 13, comprising generating a second estimated glucose value based at least in part on the second sensor signal and the second temperature-compensated sensitivity.

15. The aforementioned response action is, To temporarily suspend the display of glucose concentration on the display associated with the glucose sensor system, To generate a second estimated glucose value based at least in part on the second sensor signal and the temperature-uncompensated sensitivity, or The glucose sensor system according to claim 13, comprising at least one of determining that the first temperature signal satisfies the temperature signal conditions.

16. A method for generating an estimated glucose concentration, Accessing the first sensor signal from an in vivo glucose sensor, Accessing the first temperature signal from an external temperature sensor, To generate a first glucose sensor temperature based at least partially on the first temperature signal, To generate a first temperature-compensated sensitivity based at least partially on the temperature of the first glucose sensor, To generate a first temperature-compensated non-glucose signal based at least partially on the temperature of the first glucose sensor, A method comprising generating a first estimated glucose concentration based at least in part on the first sensor signal, the first temperature-compensated sensitivity, and the first temperature-compensated non-glucose signal.

17. Accessing a second sensor signal from an in vivo glucose sensor, Accessing the second temperature signal from the aforementioned external temperature sensor, A second glucose sensor temperature is generated based at least partially on the second temperature signal, The second glucose sensor temperature is determined to not meet the temperature conditions, To generate a second temperature-compensated non-glucose signal based at least partially on the default temperature, A second estimated glucose concentration is generated based at least partially on the second sensor signal and the second temperature-compensated non-glucose signal, The method according to claim 16, further comprising:

18. To generate the first temperature-compensated non-glucose signal, The difference between the first glucose sensor temperature and the reference glucose sensor temperature is determined, The method according to claim 16, comprising applying a non-glucose compensation factor to the difference.

19. The method according to claim 16, further comprising determining that the temperature of the first glucose sensor satisfies a temperature condition before generating the first temperature-compensated sensitivity.

20. The method according to claim 19, wherein determining that the temperature of the first glucose sensor satisfies the temperature condition includes determining that the temperature of the first glucose sensor is within a first temperature range.

21. The determination that the temperature of the first glucose sensor satisfies the temperature conditions is Using the first glucose sensor temperature and at least one previous glucose sensor temperature indicated by the previous temperature signal, a rate of temperature change is generated. The method according to claim 19, comprising determining that the rate of temperature change does not exceed the rate of change condition.

22. Accessing a second sensor signal from the aforementioned in vivo glucose sensor, Accessing the second temperature signal from the aforementioned external temperature sensor, A second glucose sensor temperature is generated based at least partially on the second temperature signal, The second glucose sensor temperature is determined to not meet the temperature conditions, In response to determining that the temperature of the second glucose sensor exceeds the temperature condition, a response action is performed. The method according to claim 16, further comprising:

23. The aforementioned response action is, To generate a second temperature-compensated sensitivity based at least partially on the default glucose sensor temperature, The method according to claim 22, comprising generating a second estimated glucose concentration based at least in part on the second sensor signal and the second temperature-compensated sensitivity.

24. The method according to claim 22, wherein the response action includes pausing the display of glucose concentration on a display associated with the glucose sensor.

25. The method according to claim 22, wherein the response action comprises generating a second estimated glucose concentration based at least in part on the second sensor signal and the temperature-uncompensated sensitivity.

26. Receiving a second sensor signal from the aforementioned in vivo glucose sensor, To generate a second temperature-compensated sensitivity based at least partially on the temperature of the first glucose sensor, A second estimated glucose concentration is generated based at least partially on the second sensor signal and the second temperature-compensated sensitivity, The method according to claim 16, further comprising:

27. The method according to claim 16, further comprising determining that the first temperature signal satisfies the temperature signal condition before generating the first temperature-compensated sensitivity.

28. The method according to claim 27, wherein the determination that the first temperature signal satisfies the temperature signal conditions is performed by the extracorporeal temperature sensor.

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