CN117222356A - Data stream bridging for sensor transitions - Google Patents

Abstract

Data stream bridging for sensor transitions is described. A first glucose measurement data stream is received from a first glucose sensor worn by a user. A termination event of the first glucose sensor is detected when the generation of the first glucose measurement and/or the transmission via the first data stream ceases. Next, a second glucose measurement data stream is received from a second glucose sensor worn by the user that replaces the first glucose sensor. During a warm-up period of the second glucose sensor, an estimated glucose value of the user is output based on both the first glucose measurement data stream received from the first glucose sensor and the second glucose measurement data stream received from the second glucose sensor prior to the termination event.

Description

Data stream bridging for sensor transitions

RELATED APPLICATIONS

The present application claims the benefit of U.S. provisional patent application 63/189429 filed on 5 months 17 of 2021 and entitled "Data stream bridging for sensor transition (Data-Stream Bridging for Sensor Transitions)" and U.S. provisional patent application 63/231502 filed on 8 months 10 of 2021 and entitled "Data stream bridging for sensor transition (Data-Stream Bridging for Sensor Transitions"), the entire disclosures of each of which are hereby incorporated by reference.

Background

Diabetes is a metabolic disease affecting hundreds of millions of people. For these people, monitoring blood glucose levels and adjusting these levels to within acceptable ranges is important not only for alleviating long-term problems such as heart disease and vision disorders, but also for avoiding the effects of hyperglycemia and hypoglycemia. Maintaining blood glucose levels within an acceptable range can be challenging because such levels can change over time almost as well as in response to daily events such as eating or exercise. Advances in medical technology have enabled the development of various systems for monitoring blood glucose, including Continuous Glucose Monitoring (CGM) systems that measure and record glucose concentration in substantially real time. CGM systems are an important tool for users of these systems to ensure that the measured glucose value is within an acceptable range.

Many glucose monitoring systems utilize wearable devices that include sensors that can be inserted into the skin of a user to monitor glucose. Typically, these sensors are disposable and designed to operate for a predetermined amount of time (e.g., ten days), after which the sensor must be replaced with a new sensor. When a new sensor is inserted into the skin of a user, the sensor may take a significant amount of time (e.g., two hours) to "warm up" before the sensor can always produce accurate measurements. During this period, i.e. during the warm-up period, the glucose measurement results produced using the newly inserted glucose sensor may be inaccurate and/or may not always be accurate.

Because of the unreliability of the glucose sensor during the warm-up period, many conventional glucose monitoring systems do not display any type of glucose data at all during this period. However, the lack of available glucose data during the transitional period between sensors results in glucose alerts and alarms being disabled, which may create health risks to users that rely on glucose data to make important decisions, such as the time of insulin administration. Thus, users may resort to using painful finger sticks during transitional periods in order to monitor their glucose levels. Resorting to finger sticks can also be inconvenient or annoying and can limit the activities that these users can perform during the warm-up period.

Disclosure of Invention

To overcome these problems, data stream bridging for sensor transitions is described. A first glucose measurement data stream is received from a first glucose sensor worn by a user. The first data stream corresponds to a first period of time during which the wearable glucose monitoring device generates glucose measurements and streams (e.g., communicates) the measurements to a computing device associated with the user. A termination event of the first glucose sensor is detected when the generation of the first glucose measurement and/or the transmission via the first data stream ceases. Next, a second glucose measurement data stream is received from a second glucose sensor worn by the user that replaces the first glucose sensor. During a warm-up period of the second glucose sensor, an estimated glucose value of the user is output based on both the first glucose measurement data stream received from the first glucose sensor and the second glucose measurement data stream received from the second glucose sensor prior to the termination event.

This summary presents some concepts in a simplified form that are further described below in the detailed description. This summary is therefore not intended to identify essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

Drawings

The specific embodiments are described with reference to the accompanying drawings.

FIG. 1 is an illustration of an environment in an exemplary implementation that is operable to employ techniques described herein.

Fig. 2 depicts an example of a wearable glucose monitoring device in more detail.

FIG. 3 depicts an example of a system for generating an estimated glucose value for a warm-up period of a glucose sensor.

FIG. 4 depicts an exemplary implementation of a user interface displaying a graph of a user's glucose over time that includes an estimated glucose value for a warm-up period of a glucose sensor.

FIG. 5 depicts an example of a scenario in which a bridging system generates an estimated glucose value during a transition period between sensors.

FIG. 6 depicts an exemplary implementation of a user interface displaying a graph of a user's glucose over time including during a transition period between glucose sensors.

FIG. 7 depicts an example of a scenario in which a bridging system retrospectively generates an estimated glucose value to replace a glucose measurement at the end of a data stream of a first sensor.

FIG. 8 depicts a procedure in an exemplary implementation in which an estimated glucose value is output based on both a first glucose measurement stream received from a first glucose sensor and a second glucose measurement data stream received from a second glucose sensor that replaces the first glucose sensor prior to a termination event.

FIG. 9 depicts a procedure in an exemplary implementation in which glucose measurements associated with a first glucose sensor are retrospectively updated based on glucose measurements received from a second glucose sensor that replaces the first glucose sensor.

FIG. 10 depicts a procedure in an exemplary implementation in which the warm-up period of the new glucose sensor ends when the glucose measurement received from the new glucose sensor meets an accuracy threshold.

Fig. 11 illustrates an example of a system including various components of an example device that may be implemented as any type of computing device as described with reference to fig. 1-10 and/or utilized to implement embodiments of the techniques described herein.

Detailed Description

SUMMARY

Data stream bridging for sensor transitions is described. According to the described techniques, the bridging system is configured to "bridge a gap" between the sensors, such as where a user replaces a first glucose sensor with a second glucose sensor. This enables the system to estimate the glucose value for the user output during a sensor transition period that corresponds to a period between expiration of the old sensor (e.g., a period after the old sensor is no longer used to report data for the user and/or a period when the old sensor is near no longer used to report data for the user (e.g., 1 hour ago, 2 hours ago, 12 hours ago, etc.) and the new sensor being properly warmed up and calibrated. The bridging system may also be used to reduce the length of the warm-up period of the new sensor. As discussed in more detail throughout, the warm-up period of the sensor may be any amount of time (e.g., substantially immediately, 0 hours to 1 hour, 1 hour to 2 hours, greater than 2 hours, etc.).

In one or more implementations, the bridging system receives a first glucose measurement data stream from a first glucose sensor worn by a user. The first data stream corresponds to a first period of time during which the wearable glucose monitoring device generates glucose measurements and streams (e.g., communicates) the measurements to a computing device associated with the user. The bridging system detects or determines a termination event for the first glucose sensor corresponding to the generation of the first glucose measurement and/or the time at which transmission via the first data stream ceases and/or is about to cease (e.g., a "grace period" before the actual sensor expires).

The first sensor may be disposable and designed to operate for a predetermined amount of time (e.g., ten days), for example, or until the occurrence of an event is detected (e.g., signal quality drops below a quality threshold, variance of glucose measurements exceeds a variance threshold, or a chemical reaction occurs). In some cases, therefore, the bridging system detects a termination event when a timer associated with the first glucose sensor expires, indicating that a predetermined amount of time has elapsed. Alternatively or in addition, a termination event may be detected when the user physically removes the first glucose sensor. For example, the first glucose sensor may be inserted subcutaneously into the skin of the user, and a termination event may be detected when the first glucose sensor is removed from the skin of the user (which results in loss of connectivity between the first sensor and the computing device).

Regardless of the manner in which the termination event is detected or determined, the sensor bridging system is configured to "bridge the gap" during the transition period between the sensors (e.g., it can provide data while a second sensor intended to replace the first sensor is warming up). The transition period between sensors may be a period during which multiple sensors (e.g., two of them) are implanted simultaneously within the user's body and/or a period during which only one sensor (or no sensor) is implanted within the user's body. In one example, the transition period may extend from expiration of the first sensor to insertion of the second sensor. In another example, the transition period may extend from a grace period before expiration of the first sensor to insertion of the second sensor. In another example, the transition period may extend from expiration of the first sensor to after warm-up of the second sensor. In another example, the transition period may extend from a grace period before expiration of the first sensor to after warm-up of the second sensor. Any desired amount of time for the transition period may be used in accordance with the concepts discussed throughout. For example, the transition period or a portion of the transition period may be a predetermined amount of time. In another example, the transition period or a portion of the transition period may be a patient-specific amount of time determined based on one or more data streams and/or other data inputs from one or more sensors.

Notably, the transition period between the sensors may include both a first period that begins when a termination event of the first glucose sensor is detected and ends when the second sensor is activated (e.g., inserted into the body of the user) and a second period during which the second glucose sensor is warmed up. Notably, during the first period of time no glucose measurements are received, while during the second period of time, i.e., during the warm-up period, glucose measurements generated using the newly inserted glucose sensor may be inaccurate and/or may not always be accurate. Since the measurement results produced by the glucose sensor during the warm-up period may not accurately estimate the actual glucose level of the person for a substantial portion of the time of the period, most conventional systems do not estimate the glucose value for the user output at all until the end of the warm-up period. However, preventing output of glucose data during the transition period between sensors may lead to frustration for users who closely monitor glucose levels and health risks for some users who are not able to view their glucose data for a longer period of time during the transition period between sensors.

To address this issue, the bridging system described herein is configured to generate and output a set of estimated glucose measurements during a transition period between sensors. To this end, the bridging system generates an estimated glucose value for the user based on both the first glucose measurement received from the first glucose sensor and the second glucose measurement received from the second glucose sensor that replaced the first glucose sensor prior to the termination event. By way of example and not limitation, the bridging system may provide the first glucose measurement and the second glucose measurement-or data representing these measurements (e.g., feature vectors) -as inputs to one or more machine learning models. Such a machine learning model may be configured to predict a current glucose level of a user based on historical glucose measurements (e.g., first glucose measurements) of the user from an earlier time period and based on relatively current but possibly unsuitable measurements (e.g., second glucose measurements) of the user. In some cases, the model may also utilize other data streams in order to form more accurate glucose predictions, such as by utilizing temperature data, activity data, food record data, and the like. By generating and outputting the estimated glucose measurement, the bridging system may "bridge" at least a portion of the resulting gap of the accurate glucose measurement that occurred during the transition period. In this way, the user can view the estimated glucose value and other glucose related information (e.g., alerts and visualizations) in an uninterrupted manner during the transition period between sensors.

In one or more implementations, the bridging system is configured to reduce an amount of time that a warm-up period of a new glucose sensor lasts. To reduce the warm-up period of the new glucose sensor, the bridging system determines whether the new glucose measurement data stream received from the new glucose sensor meets an accuracy threshold based at least in part on the previous glucose measurement data stream received from the previous glucose sensor prior to the termination event of the previous glucose sensor. Then, when the accuracy threshold is satisfied, the warm-up period of the new glucose sensor is ended. Notably, by determining the warm-up time using the accuracy of the glucose measurements received from the new sensor, the warm-up time may be reduced relative to a predetermined amount of time. For example, the warm-up time may be shortened from a first predetermined amount of time (e.g., two hours, 30 minutes, etc.) to an amount of time less than the first predetermined amount of time (e.g., less than two hours, less than 30 minutes, respectively, etc.). This enables the user to take action to manage the glucose level earlier than if the predetermined amount of time was used for the warm-up period. Furthermore, by presenting accurate glucose earlier, potentially dangerous events related to the health of the user may be avoided. This also enables people relying on wearable glucose monitoring devices to reduce their use of finger sticks, thereby eliminating potentially painful and annoying activities from their lives.

Notably, the method can also personalize the warm-up period for the user because different users can have different warm-up accuracy thresholds based on the confidence in the accuracy of the first and second data streams. For example, measuring a high amount of sensor noise in the first data stream may indicate progressive sensor attenuation. In this case, the system may have less confidence in the first sensor data, which may result in a stricter accuracy threshold. Similarly, if the user has a strong reaction in their skin when inserting the second glucose sensor, resulting in more noise in the reading, this may also increase the accuracy threshold.

In the following discussion, an exemplary environment is first described in which the techniques described herein may be employed. Examples of specific implementation details and programs that may be executed in the exemplary environment are then described as well as in other environments. The execution of the exemplary program is not limited to the exemplary environment, and the exemplary environment is not limited to the execution of the exemplary program.

Examples of environments

FIG. 1 is an illustration of an environment 100 in an exemplary implementation that is operable to employ data stream bridging for sensor transitions as described herein. The illustrated environment 100 includes a person 102 depicted wearing a wearable glucose monitoring device 104, examples of which include a wearable glucose monitoring device 104 (a) and a wearable glucose monitoring device 104 (b) having a first glucose sensor and a second glucose sensor, respectively. The illustrated environment 100 also includes a computing device 106 depicted as having a bridging system 108. The wearable glucose monitoring device 104 and the computing device 106 are communicatively coupled, including via a network 110. Some, all, or none of the components of the environment 100 shown in fig. 1 may be configured to be disposable (e.g., configured to be disposable by a user) or reusable (e.g., configured to be used by a user for multiple analyte monitoring sessions).

Alternatively or in addition, the wearable glucose monitoring device 104 and the computing device 106 may be communicatively coupled in other ways, such as using one or more wireless communication protocols or techniques. By way of example, the wearable glucose monitoring device 104 and the computing device 106 may communicate with each other using one or more of bluetooth (e.g., bluetooth low energy link), near Field Communication (NFC), 5G, and the like.

In accordance with the described techniques, the wearable glucose monitoring device 104 is configured to provide a measurement of glucose of the person 102. Although a wearable glucose monitoring device is discussed herein, it should be appreciated that data stream bridging may be used for sensor transitions of other devices capable of providing glucose measurements (e.g., non-wearable glucose devices (such as blood glucose meters requiring finger sticks), patches, etc.). Additionally or alternatively, data stream bridging may be used in conjunction with transitions of sensors that produce values different from glucose values (e.g., temperature, other analytes (e.g., lactate, sodium, insulin, etc.), and heart rate, to name a few). However, in implementations involving the wearable glucose monitoring device 104, the wearable glucose monitoring device may be configured with a glucose sensor that detects an analyte indicative of glucose of the person 102 and enables generation of glucose measurements as discussed above and below.

In one or more implementations, the wearable glucose monitoring device 104 is a Continuous Glucose Monitoring (CGM) system. As used herein, the term "continuous" used in connection with glucose monitoring may refer to the ability of a device to generate measurements substantially continuously, such that the device may be configured to generate glucose measurements at intervals of time (e.g., every hour, every 30 minutes, every 5 minutes, every 1 minute, every 30 seconds, etc.) in response to establishing a communicative coupling with a different device (e.g., when the computing device 106 establishes a wireless connection with the wearable glucose monitoring device 104 to retrieve one or more of the measurements), etc. Additional aspects of the configuration of the wearable glucose monitoring device 104 are discussed in more detail with respect to fig. 2.

In addition, the wearable glucose monitoring device 104 transmits glucose measurements to the computing device 106, such as via a wireless connection. The wearable glucose monitoring device 104 may transmit these measurements in real-time, for example, as they are generated using a glucose sensor. Alternatively or in addition, the wearable glucose monitoring device 104 may transmit glucose measurements to the computing device 106 at set time intervals. For example, the wearable glucose monitoring device 104 may be configured to transmit glucose measurements to the computing device 106 every five minutes (while the glucose measurements are being produced). Of course, the intervals at which glucose measurements are transmitted may be different from the above examples without departing from the spirit or scope of the described technology. According to the described techniques, the measurement results may be transmitted by the wearable glucose monitoring device 104 to the computing device 106 according to other bases, such as based on a request from the computing device 106. Regardless, the computing device 106 may at least temporarily maintain the glucose measurements of the person 102 in a computer-readable storage medium, such as the computing device 106.

The computing device 106 may be configured in a variety of ways without departing from the spirit or scope of the described technology. By way of example and not limitation, the computing device 106 may be configured as a mobile device (e.g., a mobile phone, a wearable device, or a tablet device), a desktop computer, or a laptop computer, just to name a few form factors. In one or more implementations, the computing device 106 may be configured as a dedicated device associated with a glucose monitoring platform (not shown), e.g., having functionality to obtain glucose measurements from the wearable glucose monitoring device 104, perform various calculations regarding glucose measurements, display information related to glucose measurements and glucose monitoring platform (not shown), communicate glucose measurements to the glucose monitoring platform, and so forth.

In addition, computing device 106 may represent more than one device in accordance with the described techniques. In one or more scenarios, for example, computing device 106 may correspond to both a wearable device (e.g., a smart watch) and a mobile phone. In such a scenario, the two devices may be capable of performing at least some of the same operations, such as receiving glucose measurements from the wearable glucose monitoring device 104, transmitting them to the glucose monitoring platform via the network 110, displaying information related to the glucose measurements, and so forth. Alternatively or in addition, different devices may have different capabilities that other devices do not have or are limited to a given device by computing instructions.

For example, in a scenario where computing device 106 corresponds to a separate smartwatch and mobile phone, smartwatches may be configured with various sensors and functions to measure various physiological markers (e.g., heart rate variability, respiration, blood flow rate, etc.) and activities (e.g., steps or other exercises) of person 102. In such a scenario, the mobile phone may not be configured with these sensors and functions, or it may include a limited amount of such functions-although in other scenarios, the mobile phone may be able to provide the same functions. Continuing with this particular scenario, the mobile phone may have capabilities not possessed by the smart watch, such as a camera to capture images associated with glucose monitoring and an amount of computing resources (e.g., battery and processing speed) that enable the mobile phone to more efficiently perform calculations regarding glucose measurements. Even in scenarios where the smart watch is capable of performing such calculations, the calculation instructions may limit the execution of those calculations to the mobile phone so as not to burden both devices and to efficiently utilize the available resources. In this regard, the computing device 106 may be configured and represent a different number of devices in a different manner than discussed herein without departing from the spirit and scope of the described technology.

In accordance with the described techniques, bridging system 108 is configured to "bridge a gap" during a transition between sensors (such as where person 102 replaces a first glucose sensor with a second glucose sensor, and/or may include a transition between sensors where the first glucose sensor and the second glucose sensor are implanted within person 102 simultaneously for a certain amount of time). In the illustrated environment 100, the person 102 is depicted in three stages, including a first stage 112, a second stage 114, and a third stage 116. Additionally, the temporal sequence of stages may correspond to a first stage 112, followed by a second stage 114, followed by a third stage 116. In a first stage 112, the person 102 is depicted wearing a wearable glucose monitoring device 104 (a) that includes a first glucose sensor. In the second stage 114, the person 102 is depicted without wearing a glucose monitoring device. This may correspond to a period of time after the person 102 removes the wearable glucose monitoring device 104 (a) but before the person 102 applies the wearable glucose monitoring device 104 (b), for example, when the person 102 switches from a first sensor using the wearable glucose monitoring device 104 (a) to a second sensor using the wearable glucose monitoring device 104 (b). In the third stage 116, the person 102 is depicted wearing a wearable glucose monitoring device 104 (b) that includes a second glucose sensor, as described above. Additional and/or different phases may exist in alternative embodiments (e.g., phases in which the person 102 is wearing the wearable glucose monitoring device 104 (a) and the wearable glucose monitoring device 104 (b) simultaneously). In any embodiment, the bridging system 108 may be used to help "bridge gaps" between a first data stream associated with the wearable glucose monitoring device 104 (a) and a second data stream associated with the wearable glucose monitoring device 104 (b) that is accurate or "pre-heated" for the person 102.

In one or more implementations, the wearable glucose monitoring device 104 (a) and the wearable glucose monitoring device 104 (b) may share one or more components, such as a common transmitter, processor, or computer-readable storage, to name a few. In such implementations, these shared components may be reusable, such that the reusable components may be coupled with one or more disposable components (e.g., glucose sensors) that are disposed of after a period of time. For example, the disposable component may be designed to operate for a predetermined amount of time (e.g., ten days, fifteen days, etc.), or until the occurrence of some event is detected (e.g., signal quality drops below a quality threshold, variance of glucose measurements exceeds a variance threshold, or a chemical reaction occurs). In this way, the wearable glucose monitoring device 104 (b) may use the same transmitter as the wearable glucose monitoring device 104 (a) to transmit glucose measurements to the computing device 106.

Alternatively, the wearable glucose monitoring device 104 (a) and the wearable glucose monitoring device 104 (b) may not share any components—they may be entirely separate devices. In such implementations, the wearable glucose monitoring device 104 (a) and the wearable glucose monitoring device 104 (b) may have the same design and be configured in the same manner, but they may be manufactured as separate devices such that they correspond to the first physical device and the separate second physical device. For example, the wearable glucose monitoring device 104 (a) may include a first glucose sensor and a first transmitter, and the wearable glucose monitoring device 104 (b) may include a second glucose sensor and a second transmitter separate from the first glucose sensor and the first transmitter but having the same configuration as the first glucose sensor and the first transmitter. Alternatively or in addition, the wearable glucose monitoring device 104 (a) and the wearable glucose monitoring device 104 (b) may be separate devices configured differently (e.g., different models), having different types of components from each other. In such a scenario, however, a separate device of a different design and configuration may still be configured to transmit glucose measurements to computing device 106.

The illustrated environment 100 also depicts a first data stream 118, a second data stream 120, a transition period 122, a termination event 124, and a warm-up period 126. In accordance with the described techniques, the first data stream 118 includes first glucose measurements 128 that are generated by the wearable glucose monitoring device 104 (a) using their respective glucose sensors (e.g., first glucose sensors) and transmitted to the computing device 106 as part of the first data stream 118. In a similar manner, the second data stream 120 includes second glucose measurements 130 (a), 130 (b) that are generated by the wearable glucose monitoring device 104 (b) using their respective glucose sensors (e.g., second glucose sensors) and transmitted to the computing device 106 as part of the first data stream 118. Here, the second glucose measurement 130 (a) corresponds to a portion of the measurements in the second data stream 120 generated by the wearable glucose monitoring device 104 (b) during the warm-up period 126. In contrast, the second glucose measurement 130 (b) corresponds to a portion of the measurements in the second data stream 120 that were generated by the wearable glucose monitoring device 104 (b) after the warm-up period 126, e.g., a subsequent portion of the glucose measurements of the second data stream 120.

In environment 100, first data stream 118 and second data stream 120 are shown as blocks to visually convey the time periods to which these streams correspond. By way of example, the first data stream 118 corresponds to a first period of time during which the wearable glucose monitoring device 104 (a) generates first glucose measurements 128 and streams (e.g., communicates) the measurements to the computing device 106. The termination event 124 corresponds to a time (e.g., point in time) at which the generation of the first glucose measurement result 128 and/or the transmission via the first data stream 118 stopped. Various events that may cause or be detected as termination events 124 are discussed in more detail below.

In addition, the illustrated environment 100 does not include "chunks" between the termination event 124 that terminates the first data stream 118 and the portion of the second data stream 120 corresponding to the warm-up period 126. This indicates that in one or more implementations, no glucose measurements are generated by the glucose monitoring device and transmitted to the computing device 106 during the period of time between the termination event 124 and the warm-up period 126 of the wearable glucose monitoring device 104 (b). In practice, this is consistent with the depiction of the person 102 in the second phase 114, in which the person 102 is depicted as not wearing a glucose monitoring device, e.g., after the person 102 has removed the wearable glucose monitoring device 104 (a) and before the person 102 has applied the wearable glucose monitoring device 104 (b). However, in some implementations, the generation and streaming of glucose measurements may not be "interrupted" -although the glucose measurements may originate from a different source-such as the case where the wearable glucose monitoring device 104 (b) is applied while the person 102 is still wearing the wearable glucose monitoring device 104 (a). In this case, the bridging system 108 may receive sensor measurements from both the wearable glucose monitoring device 104 (a) and the wearable glucose monitoring device 104 (b) simultaneously. In other words, the cooling period of the first glucose sensor may overlap with the warming period of the second glucose sensor. In this scenario, an estimated glucose value may be determined based on real-time glucose measurements received from two different sensors that are worn simultaneously.

The second data stream 120 corresponds to a second time period subsequent to the first time period. The second time period also corresponds to the time at which the wearable glucose monitoring device 104 (b) generates the second glucose measurements 130 (a), 130 (b) and streams (e.g., communicates) the measurements to the computing device 106.

The first and second glucose measurements 128, 130 (a), 130 (b) are shown separate from the first and second data streams 118, 120 to visually convey how the measurement of glucose by the person 102 may change over time-even though the first and second glucose measurements 128, 130 (a), 130 (b) are included in the first and second data streams 118, 120, respectively. By showing the first glucose measurement 128 and the second glucose measurements 130 (a), 130 (b) separately and as dots, the environment 100 shown indicates how the measurement of glucose of the person 102 may change over time. The "dots" shown may also more closely represent how the computing device 106 outputs (e.g., displays) the glucose of the person 102, for example, as a glucose trace.

Notably, the second glucose measurement 130 (a) corresponding to the warm-up period 126 of the wearable glucose monitoring device 104 (b) (e.g., the warm-up period of the second glucose sensor) has been shown to have greater variability than the first glucose measurement 128 and the second glucose measurement 130 (b) generated and transmitted after the warm-up period 126. This means that in one or more scenarios, glucose sensors may take a certain amount of time (e.g., two hours, 30 minutes, etc.) before they can be used to always produce accurate measurements. During this period, i.e., during the warm-up period 126, glucose measurements produced using the newly inserted glucose sensor may be inaccurate and/or may not always be accurate.

This may occur because some glucose sensors are designed such that one or more chemical reactions occur in response to insertion into the body (e.g., of person 102). The one or more chemical reactions may enable the sensor to detect an analyte indicative of glucose in the body. One example of such a chemical reaction is that the substance coating the glucose sensor dissolves during the preheat period 126 when inserted into the body of the person 102. Alternatively or in addition, the glucose sensor may need to be filled with fluid from the body of the person 102 during the warm-up period 126. Indeed, for various reasons, the glucose sensor may not be able to produce or may not be reliable to produce accurate and/or consistently accurate measurements of the glucose of the person 102 during the warm-up period without departing from the spirit or scope of the described technology. Regardless, the measurements produced by the wearable glucose monitoring device 104 (b) during the warm-up period 126 may not accurately estimate the actual glucose level of the person 102 (if any) during a majority of the period.

Generally, the bridging system 108 is configured to generate an estimated glucose value during a period of time during which the glucose monitoring device is prone to producing unsuitable glucose measurements (e.g., measurements that fail to accurately estimate the actual glucose level of the person). In one or more implementations, for example, the bridging system 108 is configured to generate and output a set of estimated glucose values 132 during a warm-up period 126 of a glucose sensor (the second glucose sensor discussed above) of the wearable glucose monitoring device 104 (b). The bridging system 108 may generate the estimated glucose value 132 of the person 102 based on the first glucose measurement 128 of the first data stream 118 and based on the second glucose measurement 130 (a) of the second data stream 120 corresponding to the warm-up period 126.

By way of example and not limitation, the bridging system 108 may provide the first glucose measurement 128 and the second glucose measurement 130 (a) -or data representing these measurements (e.g., feature vectors) -as inputs to one or more machine learning models. Such a machine learning model may be configured to predict a user's current glucose level based on the user's historical glucose measurements from an earlier time period (e.g., first glucose measurement 128) and based on the user's relatively current but possibly unsuitable measurements (e.g., second glucose measurement 130 (a)).

By generating the estimated glucose value 132, the bridging system 108 may "bridge" at least a portion of the resulting gap of accurate glucose measurements that occur during the transition period 122. In one or more implementations, the bridging system 108 may also be configured to generate an estimated glucose value for the person 102 during the second phase 114 (e.g., when the person is not wearing any glucose monitoring device, and the second phase corresponds to a period of time between the termination event 124 and the beginning of the warm-up period 126). The bridging system 108 may be configured to generate an estimated glucose value for the person 102 during the time period based only on the first glucose measurement 128, such as by using one or more machine learning models configured to predict the person's glucose based on historical glucose measurements. To this end, the bridging system 108 may generate an estimated glucose value for the person 102 based on the first glucose measurement result 128 when the person 102 is not wearing the glucose monitoring device (e.g., during the second phase 114). In contrast, the bridging system 108 may generate the estimated glucose value 132 of the person 102 during the warm-up period 126 based on both the first glucose measurement 128 and the second glucose measurement 130 (a) generated during the warm-up period 126. Additionally or alternatively, the bridging system 108 may utilize other data streams in order to accurately generate the estimated glucose value 132, such as by utilizing temperature data, activity data, food record data, and the like.

In one or more implementations, the computing device 106 (e.g., an application associated with the computing device 106) may effectively replace the second glucose measurement 130 (a) with the estimated glucose value 132, e.g., for decision support and display. By using the first glucose measurement 128 and the second glucose measurement 130 (a), the bridging system 108 generates a more accurate trace (e.g., estimated glucose value 132) of the actual glucose of the person 102 than represented by the second glucose measurement 130 (a). With a more accurate trace, the person 102 or the healthcare provider of the person 102 may be able to rely on the estimated glucose value 132 during the warm-up period 126 to guide treatment decisions such as whether to eat and eat content, whether to administer insulin, or whether to contact the healthcare provider to alleviate a serious health condition, to name a few. In the context of continuously measuring glucose and obtaining data describing such measurements, such as by a continuous glucose monitoring system, consider the discussion of FIG. 2 below.

FIG. 2 depicts an example 200 of a specific implementation of a wearable glucose monitoring device in more detail. In particular, the illustrated example 200 includes a top view and a corresponding side view of the wearable glucose monitoring device 104. It should be appreciated that the wearable glucose monitoring device 104 may be varied in a variety of ways in a particular implementation in accordance with the following discussion without departing from the spirit or scope of the described technology. As described above, for example, data stream bridging for sensor transitions may be used in conjunction with other types of devices for glucose monitoring such as non-wearable devices (e.g., blood glucose meters requiring finger sticks), patches, and the like.

In this example 200, the wearable glucose monitoring device 104 is shown to include a glucose sensor 202 and a sensor module 204. Here, the sensor 202 is depicted in a side view as having been subcutaneously inserted into the skin 206 (e.g., of the person 102). The sensor module 204 is depicted in top view as a dashed rectangle. In the illustrated example 200, the wearable glucose monitoring device 104 also includes a transmitter 208. The use of the dashed rectangle of the sensor module 204 indicates that it may be housed in the transmitter 208 or otherwise implemented within the housing of the transmitter. In this example 200, the wearable glucose monitoring device 104 further includes an adhesive pad 210 and an attachment mechanism 212.

In operation, glucose sensor 202, adhesive pad 210, and attachment mechanism 212 may be assembled to form an application assembly, wherein the application assembly is configured to be applied to skin 206 such that glucose sensor 202 is inserted subcutaneously as depicted. In such a scenario, the emitter 208 may be attached to the component via the attachment mechanism 212 after the component is applied to the skin 206. Alternatively, the emitter 208 may be incorporated as part of the application assembly such that the glucose sensor 202, the adhesive pad 210, the attachment mechanism 212, and the emitter 208 (with the sensor module 204) may all be applied to the skin 206 at the same time. In one or more implementations, the application assembly is applied to the skin 206 using a separate sensor applicator (not shown). Unlike the finger pricks required for conventional blood glucose meters, user-initiated application of the wearable glucose monitoring device 104 is almost painless and does not require blood drawing. Furthermore, automatic sensor applicators typically enable the person 102 to embed the glucose sensor 202 subcutaneously into the skin 206 without the assistance of a clinician or healthcare provider.

The applicator assembly may also be removed by peeling the adhesive pad 210 from the skin 206. It should be understood that the wearable glucose monitoring device 104 and its various components as shown are merely one example form factor, and that the wearable glucose monitoring device 104 and its components may have different form factors without departing from the spirit or scope of the described technology.

In operation, glucose sensor 202 is communicatively coupled to sensor module 204 via at least one communication channel, which may be a wireless connection or a wired connection. Communication from glucose sensor 202 to sensor module 204 or from sensor module 204 to glucose sensor 202 may be accomplished actively or passively, and may be continuous (e.g., analog) or discrete (e.g., digital).

Glucose sensor 202 may be a device, molecule, and/or chemical that changes or causes a change in response to an event that is at least partially independent of glucose sensor 202. The sensor module 204 is implemented to receive an indication of a change in the glucose sensor 202 or a change caused by the sensor 202. For example, the glucose sensor 202 may include a glucose oxidase that reacts with glucose and oxygen to form hydrogen peroxide that can be electrochemically detected by the sensor module 204, which may include electrodes. In this example, glucose sensor 202 may be configured to or include a glucose sensor configured to detect an analyte indicative of a glucose level in blood or interstitial fluid using one or more measurement techniques. In one or more implementations, the glucose sensor 202 may also be configured to detect analytes in blood or interstitial fluid indicative of other markers (such as lactate levels), which may improve the accuracy of identifying or predicting glucose-based events. Additionally or alternatively, the wearable glucose monitoring device 104 may include additional sensors of the glucose sensor 202 to detect those analytes indicative of other markers.

In another example, the glucose sensor 202 (or an additional sensor-not shown-of the wearable glucose monitoring device 104) may include a first electrical conductor and a second electrical conductor, and the sensor module 204 may electrically detect a change in the electrical potential across the first electrical conductor and the second electrical conductor of the glucose sensor 202. In this example, the sensor module 204 and the glucose sensor 202 are configured as thermocouples such that the change in potential corresponds to a temperature change. In some examples, the sensor module 204 and the glucose sensor 202 are configured to detect a single analyte, such as glucose. In other examples, the sensor module 204 and the glucose sensor 202 are configured to detect a variety of analytes, such as sodium, potassium, carbon dioxide, and glucose. Alternatively or in addition, the wearable glucose monitoring device 104 includes a plurality of sensors to detect not only one or more analytes (e.g., sodium, potassium, carbon dioxide, glucose, and insulin) but also one or more environmental conditions (e.g., temperature). Thus, the sensor module 204 and the glucose sensor 202 (as well as any additional sensors) may detect the presence of one or more analytes, the absence of one or more analytes, and/or a change in one or more environmental conditions.

In one or more implementations, the sensor module 204 can include a processor and memory (not shown). By utilizing a processor, the sensor module 204 may generate the glucose measurement 214 based on communication with the glucose sensor 202 indicating the changes discussed above. The glucose measurement 214 may correspond to a glucose measurement of any of the data streams discussed with respect to fig. 1, for example, at least a portion of the first glucose measurement 128 of the first data stream 118 or at least a portion of the second glucose measurement 130 (a), 130 (b) of the second data stream 120. Of course, the glucose measurement 214 may correspond to other data streams without departing from the spirit or scope of the present technology. Regardless, based on the above-described communications from the glucose sensor 202, the sensor module 204 is further configured to generate a communicable data packet including at least one glucose measurement 214. In one or more implementations, the sensor module 204 can configure the packets to include additional data including, by way of example and not limitation, supplemental sensor information 216. Such supplemental sensor information may include sensor identifiers, sensor status, temperatures corresponding to glucose measurements 214, measurements of other analytes corresponding to glucose measurements 214, and the like. It should be appreciated that supplementing the sensor information 216 may include supplementing a variety of data of the at least one glucose measurement 214 without departing from the spirit or scope of the described technology.

In implementations in which the wearable glucose monitoring device 104 is configured for wireless transmission, the transmitter 208 may wirelessly transmit the glucose measurements 214 and/or the supplemental sensor information 216 as a data stream to the computing device. Alternatively or in addition, the sensor module 204 may buffer the glucose measurements 214 and/or the supplemental sensor information 216 (e.g., in a memory of the sensor module 204 and/or other physical computer-readable storage medium of the wearable glucose monitoring device 104) and cause the transmitter 208 to transmit the buffered glucose measurements 214 and/or the buffered supplemental sensor information 216 at various intervals (e.g., time intervals (every second, every thirty seconds, every minute, every five minutes, every hour, etc.), storage intervals (when the buffered glucose measurements 214 and/or the supplemental sensor information 216 reach a threshold data amount or measurement amount), etc.).

Having considered examples of environments and examples of wearable glucose monitoring devices, consider now some examples that discuss details of techniques for data flow bridging for sensor transitions in accordance with one or more implementations.

Data stream bridging for sensor transitions

FIG. 3 depicts an example 300 of a system for generating an estimated glucose value for a warm-up period of a glucose sensor. The illustrated example 300 includes the bridging system 108.

In this example 300, the bridging system 108 is depicted as receiving as input the first data stream 118 and the second data stream 120. The second data stream 120 may be received after the first data stream 118 and/or may be received simultaneously. The bridging system 108 is also depicted as outputting an estimated glucose value 132. As shown, bridging system 108 includes a stream handler 302 and a value prediction engine 304. The stream processing program 302 is depicted as including a termination detection engine 306 and a data accuracy module 308. The value prediction engine 304 is depicted as including a prediction logic component 310. These components are configured to perform various aspects that enable the bridging system 108 to "bridge gaps" for transitions between sensors as discussed above and below. Although the bridging system 108 is depicted as having these various components, it should be understood that in implementations, the bridging system 108 may include fewer, more, and/or different components without departing from the spirit or scope of the described technology.

In one or more implementations, the termination detection engine 306 is configured to detect a termination event of the glucose sensor. In the context of fig. 1, for example, the termination detection engine 306 is configured to detect the termination event 124. As described above, the termination event 124 may correspond to a time at which generation and/or transmission of the first glucose measurement 128 as part of the first data stream 118 by the wearable glucose monitoring device 104 (a) stopped or is about to stop (e.g., a grace period prior to stopping). In accordance with the described techniques, the termination detection engine 306 may be configured to detect different types of termination events.

By way of example, the glucose sensor may be configured to be used within a limited predetermined amount of time (e.g., 7 days, 10 days, or 30 days, to name a few). In such implementations, each glucose sensor may be associated with a timer that begins, for example, when the glucose sensor is inserted into the skin of the user or otherwise deployed to measure the glucose of the user. The termination detection engine 306 may maintain a timer and/or detect when the timer expires. In implementations in which the timer counts down (decays), for example, the end detection engine 306 may detect when the timer reaches zero or falls to some other threshold that indicates the end of the life of the respective sensor. In contrast, in implementations in which the timer is counting (increasing), the end detection engine 306 may detect the time that the timer reaches a threshold (e.g., 10 days) indicating the end of the lifetime of the respective sensor.

Alternatively or in addition, the termination detection engine 306 may detect a termination event corresponding to a change in condition other than identifying an expiration timer value representing the "lifetime" of the glucose sensor. By way of example, the termination detection engine 306 may detect a termination event based on a loss of connectivity (e.g., communication or physical) with the glucose sensor. For example, termination detection engine 306 may detect that a termination event has occurred when no glucose measurement is received from the glucose sensor after a threshold amount of time, when no signal (e.g., a "heartbeat") is received from the glucose sensor, when a signal is received indicating that connectivity with the glucose sensor is complete, when an end-of-life chemical reaction for the glucose sensor is detected, or when a chemical reaction for a new sensor is detected, to name a few.

As described above, the glucose sensor of the wearable glucose monitoring device 104 may be inserted subcutaneously into the skin of the person 102. In one or more implementations, the termination detection engine 306 may be configured to detect termination events when the glucose sensors are removed from the skin of the user (e.g., pulled from the skin of the person 102). In accordance with the described techniques, the termination detection engine 306 is configured to detect a termination event 124 that indicates when the generation and/or transmission of glucose measurements of the first data stream 118 stopped. Alternatively or in addition, the termination event may be initiated by a user interaction with a user interface (e.g., user interface 316). For example, a user may be able to select a control to end a session with a first glucose sensor and start a new session with a second glucose sensor. It should be appreciated that the termination detection engine 306 may detect different types of termination events without departing from the spirit or scope of the described technology.

In general, the value prediction engine 304 is configured to generate the estimated glucose value 132 using actual glucose measurements generated using a glucose sensor. For example, the value prediction engine 304 may use measurements from one or more of the first data stream 118 or the second data stream 120 to generate the estimated glucose value 132. In addition, the value prediction engine 304 may generate an estimated glucose value 132 to replace one or more glucose measurements in the first data stream 118 or the second data stream 120. In the context of fig. 1, for example, the value prediction engine 304 generates an estimated glucose value 132 to replace the glucose measurement of the second data stream 120, in particular to replace the second glucose measurement 130 (a) corresponding to the warm-up period 126 of the glucose sensor of the wearable glucose monitoring device 104 (b). As discussed with respect to fig. 5-7, the value prediction engine 304 may also be used to generate estimated glucose values for other time periods. These time periods may include, for example, a period of time when the user is not wearing any sensors and retrospectively lasting for a period of time when the use (or connectivity) of the sensors is over (e.g., the lifetime of the sensors is over). Additionally or alternatively, the value prediction engine 304 may utilize other data streams (not shown) in order to accurately generate estimated glucose values, such as by utilizing temperature data, activity data, food record data, and the like.

When generating the estimated glucose values 132 for the warm-up period 126 of the glucose sensor, the value prediction engine 304 may generate these values based on the first glucose measurement results 128 and also based on the second glucose measurement results 130 (a) corresponding to the warm-up period 126. To generate the estimated glucose value 132 from the first glucose measurement 128 and the second glucose measurement 130 (a), the value prediction engine 304 uses the prediction logic 310. In operation, prediction logic 310 may be configured as executable code (e.g., a "binary") capable of receiving these glucose measurements as input, processing them according to encoded algorithms, formulas, rule sets, and/or models, and determining and outputting estimated glucose value 132. The prediction logic 310 may be implemented based on a variety of algorithms, formulas, rules, and/or models without departing from the spirit or scope of the described technology.

In one or more implementations, for example, the prediction logic 310 may determine the estimated glucose value 132 using one or more weighting techniques (such as a weighted average). For example, the prediction logic 310 may associate a relatively smaller weight with the second glucose measurement 130 (a) toward the beginning of the warm-up period 126, and may associate a relatively larger weight with the second glucose measurement 130 (a) closer to the end of the warm-up period 126. In one or more implementations, the prediction logic 310 can associate progressively greater weights with the second glucose measurement 130 (a) over time. As one example, at the beginning of the warm-up period 126, the prediction logic 310 may assign a weight of 0.9 to the first glucose measurement 128 and a weight of 0.1 to the second glucose measurement 130 (a), and then calculate the estimated glucose value 132 as the sum of the weighted first and second glucose measurements. In contrast, toward the end of warm-up period 126, prediction logic 310 may assign a weight of 0.1 to first glucose measurement 128 and a weight of 0.9 to second glucose measurement 130 (a), and then calculate estimated glucose value 132 as the sum of the weighted first and second glucose measurements.

As part of determining the estimated glucose value 132, the prediction logic 310 may also associate weights with the first glucose measurements 128 or with predictions derived from these measurements. For example, the prediction logic 310 may include one or more machine learning models that are trained based on historical data of a population of users to predict glucose of a person over a period of time in the future (e.g., 30 minutes, 2 hours, or all days at hand). Such a machine learning model may predict glucose of the person 102 in response to receiving as input one or more sequences (e.g., traces) of glucose measurements (e.g., last 30 minutes, last 6 hours, or last day of glucose measurements of the person 102). In such implementations, the prediction logic 310 may determine the estimated glucose value 132 based on the glucose value predicted for the warm-up period 126 (e.g., predicted from the first glucose measurement 128) and also based on the second glucose measurement 130 (a) corresponding to the warm-up period 126.

For example, when the prediction logic 310 associates progressively higher weights with the second glucose measurement 130 (a) over time, the prediction logic 310 may also associate progressively lower weights with the glucose values predicted for the warm-up period 126. In this way, the predicted glucose value may affect the estimated glucose value 132 toward the beginning of the warm-up period 126 to a greater extent, and the second glucose measurement 130 (a) may affect the estimated glucose value 132 toward the end of the warm-up period 126 to a greater extent. The weight associated with the predicted glucose value may be referred to as "decay" and the weight associated with the second glucose measurement 130 (a) may be referred to as "increase" over time. In one or more implementations, the prediction logic 310 may determine the estimated glucose value 132 by calculating a weighted average of the predicted glucose value and the second glucose measurement 130 (a).

The prediction logic 310 may also determine an estimated glucose value 132 based on the flow supplement 312. The flow supplement 312 is shown with dashed lines to indicate that its use is optional, e.g., in one or more implementations, the flow supplement may not be used to determine the estimated glucose value 132. However, in implementations in which the flow supplement 312 is used to determine the estimated glucose value 132, the flow supplement 312 may include data describing one or more aspects of the person 102, one or more sensors used to obtain measurements, the first data stream 118 or the second data stream 120 via which the measurements are communicated to the computing device 106, and/or other factors that may affect the accuracy or reliability of the measurements generated by these sensors and/or received by the computing device 106.

By way of example, aspects of the person 102 that may be described by the flow supplement 312 include the temperature of the person 102, food that the person 102 has consumed (e.g., food intake, time, food type, and/or macronutrients), exercises performed by the person 102 (e.g., duration, intensity, type, and/or performance indicators), sleep of the person 102 (e.g., duration, quality, and/or time), analytes measured for the person 102 other than glucose (e.g., insulin, lactic acid, sodium, potassium, and/or carbon dioxide), physiological markers of the person (e.g., heart Rate Variability (HRV), blood pressure, and/or blood oxygen level), pressure of the person 102 (e.g., identification of pressure events), demographic characteristics of the person (e.g., age, gender, location, and genetic markers), and identified or diagnosed health status of the person (e.g., type 1 diabetes, type 2 diabetes, pregnancy, injury, and/or surgery), to name a few examples.

An exemplary aspect of one or more sensors that may be described by the flow supplement 312 includes a "state" of the sensor, where the "state" of the sensor refers to a value that the sensor is designed to determine and associate with one or more measurements. Such a status indicates whether the sensor is operating "normal" or outside of normal conditions when the corresponding measurement is generated. Other exemplary aspects of the sensor that may be described by the flow supplement 312 include, for example, a manufacturing lot of the sensor, a unique identifier of the sensor, a model number of the sensor, factory calibration information of the sensor, a remaining time the sensor is to be used to generate a measurement, a relative accuracy of the sensor, a relative variability of the sensor, information about detected errors related to the sensor, a measure of the quality of information generated by the sensor, and/or information about an interface of the sensor with other components, to name a few.

Exemplary aspects of the data stream that may be described by the stream supplement 312 include connectivity issues (e.g., loss of connectivity) between the wearable glucose monitoring device 104 (a) and the computing device 106, connectivity issues between the wearable glucose monitoring device 104 (b) and the computing device 106, detected damage related to one or more portions of the data stream, quality metrics associated with one or more portions of the data stream (e.g., describing the quality of the corresponding data packets and/or transmissions of the data packets), and delays of one or more portions of the data stream, to name a few. Indeed, these are merely examples of aspects that may be described by the flow supplements 312, and the flow supplements may describe a variety of other aspects that affect the accuracy of the measurements produced by the sensors and/or received by the computing device 106 without departing from the spirit or scope of the described technology.

As described above, prediction logic 310 may be implemented based on a variety of algorithms, formulas, rules, and/or models. In implementations in which prediction logic 310 is implemented based on or otherwise includes one or more machine learning models, for example, at least one of these models may be "generic" to a population. Such models may be trained using training data describing users in a user population and trained to generate predictions of glucose for individual users in the user population. Broadly, models that are "generic" to a population are not further trained for a particular end user as they are being used in operation to generate predictions for the particular end user, e.g., by using transfer learning and/or training data for the particular end user. Alternatively or in addition, at least one of the models used by prediction logic 310 may be specific to the end user. Such models may be trained using historical data describing a particular end user for which the model is trained to generate predictions of glucose. In one or more implementations, such "user-specific" models may be initially "generic" to a population and then further trained using transfer learning and/or training data describing the specific user.

In one or more implementations, the machine learning model of the prediction logic 310 may receive as inputs: data from the first data stream 118 (e.g., one or more of the first glucose measurements 128), data from the second data stream 120 (e.g., one or more of the second glucose measurements 130 (a)), and a stream supplement 312 describing various aspects of the person 102 and/or aspects of the context in which the prediction logic 310 generates the estimated glucose value 132. The machine learning model may then determine the estimated glucose value 132 by applying weights to the inputs (e.g., feature vectors representing the data described above). These weights may correspond to an underlying model determined using historical data of the user population and/or the person 102. In such implementations, the machine learning model is configured to output data (e.g., feature vectors) indicative of the estimated glucose value 132. The output data (e.g., feature vectors) may be processed to obtain an estimated glucose value 132. During the warm-up period 126 and until the end of the warm-up period 126, the value prediction engine 304 may use the prediction logic component 310 to generate the estimated glucose value 132. In one or more implementations, the machine learning model may output a probability that a user's glucose measurement will be above a particular glucose threshold (e.g., above 180 mg/dL) during a transition between sensors to indicate a risk of hyperglycemia and/or a probability that a user's glucose measurement will be below a particular glucose threshold (e.g., below 70 mg/dL) during a transition between sensors to indicate a risk of hypoglycemia.

In this example 300, the data accuracy module 308 determines the time at which the warm-up period ended. In one or more implementations, the data accuracy module 308 determines the end of the warm-up period based on a predetermined amount of time (e.g., substantially no time for warm-up, less than one hour (such as 30 minutes), two hours, more than two hours, etc.) after detecting the sensor inserted into the person 102. The predetermined amount of time may be set based on a number of tests controlled in the real world that indicate that the glucose sensor will meet the threshold amount of accuracy after the predetermined amount of time without some defect. In the context of fig. 1, for example, the data accuracy module 308 may determine that the warm-up period 126 ends after detecting a predetermined amount of time after the glucose sensor of the wearable glucose monitoring device 104 is inserted into the body of the person 102. Alternatively or in addition, the data accuracy module 308 may determine the end of the warm-up period based on determining that the data of the second data stream 120 accurately describes glucose of the person 102 (e.g., the accuracy of the second data stream 120 meets an accuracy threshold).

In one or more implementations, for example, the prediction logic 310 may determine the accuracy of the data of the second data stream 120 based on the glucose predictions generated by the prediction logic 310 (e.g., using the first data stream 118) and also based on the data of the second data stream 120. By way of example, the data accuracy module 308 may compare the data of the second data stream 120 to a prediction and determine an accuracy of the data of the second data stream 120 based in part on the comparison. For example, the data accuracy module 308 may identify differences between the predictions and the data of the second data stream 120 by comparing them. Based on these differences, the data accuracy module 308 may further determine that the data of the second data stream 120 remains within the glucose prediction horizon generated by the prediction logic 310 for a certain threshold amount of time, has a certain threshold frequency, and/or is for a certain threshold number of measurements.

Alternatively or in addition, the data accuracy module 308 may calculate one or more metrics related to the data of the second data stream 120 and determine the accuracy of the second data stream 120 based on the metrics. For example, the data accuracy module 308 may calculate the variability of the second glucose measurement 130 (a) of the second data stream 120. In this scenario, when the data accuracy module 308 determines that the calculated variability meets the accuracy threshold, then the data accuracy module 308 may also determine that the warm-up period 126 ends. In one or more implementations, the data accuracy module 308 may generate a measure of accuracy based on one or more of the techniques described above and/or using other techniques. It should be appreciated that the data accuracy module 308 may determine the accuracy of the data stream generated by and received from the sensor in a variety of ways without departing from the spirit or scope of the described technology.

By determining the warm-up period 126 using the accuracy of the data of the second data stream 120, the warm-up period 126 may be shortened relative to a predetermined amount of time. For example, the warm-up period 126 may be shortened from a first predetermined amount of time (e.g., two hours, 30 minutes, etc.) to an amount of time less than the first predetermined amount of time (e.g., less than two hours, less than 30 minutes, respectively, etc.). The use of glucose predictions for the warm-up period 126 based on the first data stream 118 may also reduce the warm-up period 126 relative to a predetermined amount of time. In doing so, computing device 106 may present glucose of person 102 to them (or another user associated with person 102) earlier than if the predetermined amount of time was used for warm-up period 126. This enables the person 102, the healthcare provider, or another user to take action to manage the glucose level of the person 102 earlier than if the predetermined amount of time was used for the warm-up period 126. By presenting accurate glucose earlier, potentially dangerous events related to the health of person 102 may be avoided. This also enables a person relying on the wearable glucose monitoring device 104 to reduce finger stick compared to the case of using a predetermined amount of time. This eliminates potentially painful and annoying activities from their life and adds to the quality of life.

The illustrated example 300 also includes a display module 314 that is depicted as receiving the estimated glucose value 132 as input. In general, the display module 314 is configured to generate a user interface 316 that displays a glucose presentation 318. In one or more implementations, the user interface 316 is displayed via a display device of the computing device 106, and the user interface 316 may be displayed within an application. Such applications may correspond to a glucose monitoring platform (e.g., a provider of the wearable glucose monitoring device 104) and run on the computing device 106, such as in the background and/or in response to selection of an icon via a display device of the computing device 106. Further, such applications may correspond to web applications that interact with the glucose monitoring platform over network 110 to provide a variety of functions.

The glucose display 318 may be configured in a variety of ways to display one or more of the first glucose measurement 128, the estimated glucose value 132, and/or the second glucose measurement 130 (a), 130 (b). For example, the glucose display 318 may be configured as one or more glucose traces plotted over time. In one or more implementations, the glucose display 318 may also be configured to display the second glucose measurement 130 (a) simultaneously with the estimated glucose value 132. By displaying both, the glucose display 318 visually conveys the accuracy (or inaccuracy) of the actual measurement produced during the warm-up period 126 and/or the difference between the actual measurement and the estimated glucose value 132. Alternatively or in addition, the display module 314 may determine that the glucose measurement or the estimated glucose value is not displayed in a variety of scenarios, such as where the difference between the estimated glucose value 132 and the second glucose measurement 130 (a) exceeds a threshold. In the context of displaying a user interface including glucose presentation, consider the discussion of FIG. 4 below.

FIG. 4 depicts an example 400 of a specific implementation of a user interface displaying a graph of a user's glucose over time that includes an estimated glucose value for a warm-up period of a glucose sensor.

Example 400 depicts an example of computing device 106. Here, the computing device includes a display device 402 depicted as a user interface 404 that is output for display at four chronological stages 406 through 412, including a first stage 406, a second stage 408, a third stage 410, and a fourth stage 412. It should be appreciated that the user interface 404 may correspond to the user interface 316. Thus, the display module 314 may cause output of the user interface 404 for display.

At a first stage 406, the user interface 404 includes a chart plotting the first glucose measurement 128 over time. This may correspond to an example of glucose display 318. Here, the user interface 404 also includes a legend 414 that identifies a first symbol 416 and a second symbol 418. In this example 400, a first symbol 416 represents a glucose measurement that has been produced by a glucose sensor, and a second symbol 418 represents an estimated glucose value (e.g., estimated by the prediction logic component 310 based on the glucose measurement produced by the glucose sensor). Notably, the chart presented via the user interface 404 at the first stage 406 displays only glucose measurements, which is indicated by the plurality of first symbols 416 drawn on the chart. Additionally, the graph depicted at the first stage 406 does not display any estimated glucose value, which is indicated by the absence of the second symbol 418 on the graph.

The user interface 404 also includes a current glucose element 420. Generally, the current glucose element 420 displays a glucose measurement or estimated glucose value corresponding to the current time, e.g., the most recently generated glucose measurement or the most recently estimated glucose value. However, when the user is not wearing a glucose sensor, the current glucose element 420 may display one or more symbols (e.g., '-' or 'N/a') indicating that no measurement or estimate is available for display in the current glucose element 420. Alternatively, the current glucose element 420 may display the estimated glucose value when the user is not wearing a glucose sensor.

In this example 400, the second phase 408 corresponds to a point in time that follows an earlier point in time corresponding to the first phase 406. At the second stage 408, the user interface 404 is depicted displaying the plurality of first glucose measurements 128 displayed at the first stage 406. However, the chart does not depict the sign between time 422 and the current time, which is indicated by the text 'NOW' along the x-axis of the graph. This may indicate that the corresponding user is not wearing a glucose sensor, such as after removal of a first sensor and before application of a subsequent second sensor. In the context of fig. 1, for example, the user interface displayed at the second stage 408 may correspond to the person 102 at the second stage 114, e.g., after removal of the wearable glucose monitoring device 104 (a) but before application of the wearable glucose monitoring device 104 (b). At the second stage 408, the current glucose element 420 does not display a value indicative of glucose for the corresponding user. Instead, the current glucose element 420 displays '- - -' at the second stage 408, which, as described above, may indicate that the user is not currently wearing a sensor or that glucose measurements are not received by the computing device 106 from a sensor.

The third stage 410 corresponds to a point in time after the point in time corresponding to the second stage 408. At the third stage 410, the user interface 404 is depicted displaying the plurality of first glucose measurements 128 displayed at the first stage 406 and the second stage 408, but fewer than any previous stage. In this example 400, a glucose symbol is not depicted between time 422 and subsequent time 424. According to the described techniques, this gap between symbols may correspond to a period of time between donning the sensors (i.e., after removal of the first sensor and before application of the second sensor). Further, the user interface 404 is depicted displaying a plurality of estimated glucose values 132 at a third stage 410. This may indicate that the corresponding user is wearing a second sensor (e.g., a sensor of the wearable glucose monitoring device 104 (b)), but that the glucose value is being estimated and displayed, rather than displaying the measurement results generated by and received from the second sensor. For example, this third stage 410 may correspond to a point in time during the warm-up period 126 of the second sensor in which the estimated glucose value 132 is displayed in place of the second glucose measurement 130 (a), e.g., because the second glucose measurement 130 (a) does not meet the accuracy threshold.

At the third stage 410, the current glucose element 420 displays a glucose value with an additional information indicator '/x'. In accordance with the described techniques, the additional information indicator in the current glucose element 420 may be used as a visual "warning" or "alert" that indicates the presence of relevant information related to the value or symbol displayed in the current glucose element 420. Here, the additional information indicator corresponds to a warning that alerts the user that the estimated glucose value 132 is estimated and may not correspond to the user's actual glucose in some situations, such as where the user's actual glucose changes rapidly or due to an event not being considered by the prediction logic 310. The current glucose element 420 may include additional information indicators related to displaying different related information without departing from the spirit or scope of the described technology.

At the third stage 410, the user interface 404 also displays a remaining time indicator 426. The remaining time indicator 426 may indicate an actual amount of time remaining for the warm-up period 126, such as where the warm-up period 126 is configured to last for a predetermined amount of time (e.g., two hours, 30 minutes, etc.). Alternatively or in addition, the remaining time indicator 426 may indicate an estimated amount of time remaining for the warm-up period 126, such as where the data accuracy module 308 determines an end of the warm-up period 126 based on an accuracy of glucose measurements generated by and received from the respective sensors meeting an accuracy threshold. The remaining time indicator 426 may include a numerical value that indicates the amount of time remaining and/or a non-digital visual element that indicates the amount of time remaining. The remaining time indicator 426 may indicate the actual or estimated amount of time remaining in various ways, including visually, audibly, and/or tactilely, without departing from the spirit or scope of the described technology.

The fourth stage 412 corresponds to a point in time after the point in time corresponding to the third stage 410. At a fourth stage 412, the user interface 404 is depicted displaying the plurality of estimated glucose values 132 displayed at the third stage 410 and additional values of the estimated glucose values 132. To the left of time 424, no symbol is displayed in the chart of the user interface 404 at the fourth stage 412. This gap in symbols and the left-hand shift of the estimated glucose value 132 across stages 408 through 412 indicates the passage of time. At the fourth stage 412, the user interface 404 also displays a second glucose measurement 130 (b), which in the context of fig. 1 corresponds to a time period after the warm-up period 126. In accordance with the described techniques, the display module 314 may display the second glucose measurement 130 (b) after the glucose sensor has been deployed for a warm-up period 126 that lasts until a predetermined amount of time elapses or in response to the data accuracy module 308 determining that the accuracy of the measurement meets an accuracy threshold.

FIG. 5 depicts an example 500 of a scenario in which a bridging system generates an estimated glucose value during a transition period between sensors.

The illustrated example 500 includes the person 102 depicted at the first stage 112, the second stage 114, and the third stage 116, as well as the computing device 106 and the bridging system 108. The illustrated example 500 also depicts the first glucose measurement 128 of the first data stream 118 and the second glucose measurement 130 (a), 130 (b) of the second data stream 120. In this example 500, the bridging system 108 is also depicted as outputting the estimated glucose value 132 of the warm-up period 126 in a similar manner as in fig. 1.

However, in contrast to fig. 1, in this example 500, the bridging system 108 is depicted as outputting a second set of estimated glucose values 502. In this example 500, the second set of estimated glucose values 502 corresponds to the second phase 114, a period of time during which the person 102 is not wearing a glucose sensor. This may correspond to a time of user transition between sensors, e.g., a transition from a first sensor (e.g., of the wearable glucose monitoring device 104 (a)) to a second sensor (e.g., of the wearable glucose monitoring device 104 (b)). In other words, the bridging system 108 may generate an estimated glucose value for a period of time that corresponds to beginning removal of the wearable glucose monitoring device 104 (a) from the person 102 and ending deployment of the wearable glucose monitoring device 104 (b). In contrast to the estimated glucose values 132, a second set of estimated glucose values 502 may be generated based on only the first glucose measurements 128. This is because no sensor may be deployed in the person 102 during the period of time corresponding to the determination of the second set of estimated glucose values 502. For example, when the bridging system 108 determines and causes the second set of estimated glucose values 502 to be output, the glucose sensor of the wearable glucose monitoring device 104 (b) may not have been deployed.

In accordance with the described techniques, the second set of estimated glucose values 502 may correspond to predictions of glucose generated by the prediction logic 310 based on the first glucose measurements 128 of the first data stream 118. In contrast to the estimated glucose values 132, the prediction logic 310 may determine the second set of estimated glucose values 502 without using the second glucose measurement 130 (a). Indeed, the prediction logic 310 may determine the second set of estimated glucose values 502 even before the second glucose measurement 130 (a) is generated, for example, because the wearable glucose monitoring device 104 (a) may not have been deployed when the second set of estimated glucose values 502 is determined. In this scenario, prediction logic 310 may generate a glucose prediction of second set of estimated glucose values 502 using one or more machine learning models (such as one or more generic models configured to generate predictions for users of a user population and/or one or more user-specific models configured to generate predictions for specific users). In the context of displaying a set of estimated glucose values during transitions between sensors, consider the discussion of FIG. 6 below.

FIG. 6 depicts an example 600 of a specific implementation of a user interface displaying a graph of a user's glucose over time including during a transition period between glucose sensors.

The illustrated example 600 depicts another example of a computing device having a display device 402. In this example 600, the display device 402 is depicted as outputting the user interface 404 at four chronologically ordered stages 602-608, including a first stage 602, a second stage 604, a third stage 606, and a fourth stage 608. The user interface 404 may correspond to the user interface 316, and the display module 314 may cause output of the user interface 404. However, in contrast to the example depicted in fig. 4, stages 602-608 may plot glucose for a corresponding user at a transition in use between glucose sensors (including when the user is not wearing a glucose sensor).

In this example 600, the first stage 602 is similar to the first stage 406 depicted in fig. 4. For example, at a first stage 602, the user interface 404 includes a chart plotting the first glucose measurement 128 over time. The presentation of the graph with plotted glucose may correspond to another example of glucose presentation 318. Notably, the chart presented via the user interface 404 at the first stage 602 displays only glucose measurements, and does not display any estimated glucose values.

The second stage 604 in this example 600 corresponds to a point in time that follows an earlier point in time corresponding to the first stage 602. In a similar manner as the second stage 408 of fig. 4, the user interface 404 is depicted as displaying the plurality of first glucose measurements 128 previously displayed at the first stage 602 at the second stage 604. Here, the user interface 404 is depicted displaying a plurality of first glucose measurements 128 until time 610. Time 610 may correspond to a termination event 124, i.e., a condition in which person 102 removes wearable glucose monitoring device 104 (a) and its corresponding glucose sensor, thereby stopping the generation and transmission of glucose measurements.

In contrast to the second stage 408 of fig. 4, however, the user interface 404 is depicted as displaying the second set of estimated glucose values 502 between time 610 and the current time, which is indicated by the text 'NOW' along the x-axis of the chart. This is in contrast to the second stage 408 of fig. 4, because the second stage 408 of fig. 4 does not depict symbols during this same period of time. Thus, stages 602-608 depict different scenarios than stages 406-412 of fig. 4, i.e., stages 602-608 depict scenarios in which the bridging system 108 is configured to predict glucose values for periods of time in which the user is not wearing a sensor and cause these values to be displayed.

Since the user interface 404 at the second stage 604 displays such predicted values, the current glucose element 420 displays a glucose value with an additional information indicator '×' at the second stage 604. In this scenario, the additional information indicator may be used as a visual "warning" or "alert" indicating the presence of relevant information related to the value or symbol displayed in the current glucose element 420. Here, the additional information indicator corresponds to a warning that alerts the user that the second set of estimated glucose values 502 is estimated and may not correspond to the user's actual glucose in some situations, such as where the user's actual glucose changes rapidly or due to an event not considered by the prediction logic component 310.

Third stage 606 and fourth stage 608 are similar to third stage 410 and fourth stage 412 of FIG. 4, except that third stage 606 and fourth stage 608 display second set of estimated glucose values 502-rather than displaying no measurements or values at user transitions between sensors. This may enable the user or healthcare provider to continue making therapeutic decisions even during periods when the user is not wearing glucose sensors (e.g., transitions between sensors). In the context of retrospectively updating at least one glucose sensor value of a glucose measurement data stream, consider the discussion of FIG. 7 below.

FIG. 7 depicts an example 700 of a scenario in which a bridging system retrospectively generates an estimated glucose value to replace a glucose measurement at the end of a data stream of a first sensor.

The illustrated example 700 includes the person 102 depicted at the first stage 112, the second stage 114, and the third stage 116, as well as the computing device 106 and the bridging system 108. The illustrated example 700 also depicts the first glucose measurement 128 of the first data stream 118 and the second glucose measurement 130 (a), 130 (b) of the second data stream 120. In this example 700, the bridging system 108 is also depicted as outputting the estimated glucose value 132 of the warm-up period 126 in a similar manner as in fig. 1.

However, in contrast to fig. 1, in this example 700, the bridging system 108 is depicted as outputting a retrospective glucose value 702. The transition period 704 in this example 700 also spans a different amount of time than the transition period 122 of fig. 1. The transition period 122 of fig. 1 begins at a termination event 124 and continues to the end of a warm-up period 126. In contrast, as shown, the transition period 704 begins at the beginning of the cool-down period 706 and continues to the end of the warm-up period 126. The cooling period 706 may correspond to a predetermined amount of time at the end of the life of the sensor. Alternatively, the data accuracy module 308 (or a different module configured to calculate a confidence in the accuracy of the glucose measurements) may determine the beginning of the cooling period 706 based on the accuracy of the first glucose measurement 128 such that the beginning of the cooling period 706 is determined when the first glucose measurement 128 fails to meet a threshold accuracy (e.g., based on unexpected variability of the sensor signal). Regardless of whether the cooling period 706 corresponds to a predetermined amount of time or is determined based on the accuracy of the measurement, the bridging system 108 may be configured to retrospectively update the first glucose measurement 128 corresponding to the cooling period 706.

In one or more implementations, the bridging system 108 can determine the retrospective glucose value 702 based on the first glucose measurement 128 generated during the cooling period 706 and also based on one or more of the second glucose measurements 130 (a), 130 (b). In one or more scenarios, the data accuracy module 308 may not determine that the first glucose measurement 128 fails to meet the threshold accuracy until the computing device 106 receives the second glucose measurement 130 (a), 130 (b). In practice, the bridging system 108 may determine the retrospective glucose value 702 when the glucose sensor of the wearable glucose monitoring device 104 (b) generates glucose values and transmits them to the computing device 106. In one or more implementations, the prediction logic 310 may thus be configured to retrospectively generate a glucose value, e.g., given as an input measurement before prediction (e.g., one or more of the first glucose measurements 128 before the cooling period 706), a measurement corresponding to the same time as the prediction (e.g., the first glucose measurement 128 corresponding to the cooling period 706), and a measurement after prediction (e.g., one or more of the second glucose measurements 130 (a), 130 (b)). The prediction logic 310 may use one or more machine learning models and/or weighted averages to generate the retrospective glucose value 702 of the cooling period 706.

Having discussed exemplary details of the techniques for data stream bridging for sensor transitions, consider now some examples of procedures that illustrate additional aspects of the present technique.

Exemplary procedure

This section describes an example of a procedure for data stream bridging for sensor transitions. Aspects of the program may be implemented in hardware, firmware, or software, or a combination thereof. A program is illustrated as a set of blocks that specify operations performed by one or more devices and are not necessarily limited to the orders shown for performing the operations by the respective blocks. In at least some implementations, the program is executed by a bridging system, such as bridging system 108 that utilizes stream processing program 302 and value prediction engine 304.

FIG. 8 depicts a procedure 800 in an exemplary implementation in which an estimated glucose value is output based on both a first glucose measurement data stream received from a first glucose sensor prior to a termination event and a second glucose measurement data stream received from a second glucose sensor that is replacing the first glucose sensor or is intended to replace the first glucose sensor at a point in time.

A first glucose measurement data stream is received from a first glucose sensor worn by a user (block 802). By way of example, the bridging system 108 receives the first data stream 118 of the first glucose measurement 128 from the first glucose sensor of the wearable glucose monitoring device 104 (a) worn by the person 102.

A termination event of the first glucose sensor is detected (block 804). By way of example, the termination detection engine 306 detects a termination event 124 of the first glucose sensor. As described above, the termination event 124 corresponds to a time at which generation and/or transmission of the first glucose measurement 128 by the wearable glucose monitoring device 104 (a) as part of the first data stream 118 is stopped or is about to stop. In different implementations, the termination detection engine 306 may be configured to detect different types of termination events.

By way of example, the glucose sensor may be configured to be used within a limited predetermined amount of time (e.g., 7 days, 10 days, or 30 days, to name a few). In such implementations, each glucose sensor may be associated with a timer, for example, that begins when the glucose sensor is inserted into a user or otherwise deployed to measure the user's glucose. The termination detection engine 306 may maintain a timer and/or detect when the timer expires. For example, when the timer counts down (decays), the end detection engine 306 may detect when the timer reaches zero or falls to some other threshold value that indicates the end of the lifetime of the respective sensor. In contrast, when the timer is counting (increasing), the end detection engine 306 may detect a time when the timer reaches a threshold (e.g., 10 days) indicating the end of the lifetime of the respective sensor.

Alternatively or in addition, the termination detection engine 306 may detect a changed termination event corresponding to a condition different from an expiration timer value identifying a "lifetime" of the glucose sensor. By way of example, the termination detection engine 306 may detect a termination event based on a loss of connectivity (e.g., communication or physical) with the glucose sensor. For example, termination detection engine 306 may detect that a termination event has occurred when no glucose measurement is received from the glucose sensor after a threshold amount of time, when no signal (e.g., a "heartbeat") is received from the glucose sensor, when a signal is received indicating that connectivity with the glucose sensor is complete, when an end-of-life chemical reaction for the glucose sensor is detected, or when a chemical reaction for a new sensor is detected, to name a few. As described above, the glucose sensor of the wearable glucose monitoring device 104 may be inserted subcutaneously into the skin of the person 102. In one or more implementations, the termination detection engine 306 may be configured to detect a termination event when the glucose sensor is removed from the skin of the user (e.g., pulled from the skin of the person 102).

A second glucose measurement data stream is received from a second glucose sensor worn (e.g., immediately or eventually) by the user in place of the first glucose sensor (block 806). By way of example, the bridging system 108 receives the second data stream 120 of glucose measurements 130 (a) from the second glucose sensor of the wearable glucose monitoring device 104 (b) worn by the person 102. The second data stream 120 and the first data stream 118 may be received sequentially and/or simultaneously with each other.

During the warm-up period of the second glucose sensor, an estimated glucose value of the user is output based on both the first glucose measurement data stream received from the first glucose sensor and the second glucose measurement data stream received from the second glucose sensor (block 808). By way of example, during the warm-up period 126 of the second glucose sensor of the wearable glucose monitoring device 104 (b), the value prediction engine 304 outputs an estimated glucose value 132 of the person 102 based on both the first data stream 118 of first glucose measurements 128 received from the first glucose sensor and the second data stream 120 of glucose measurements 130 (a) received from the second glucose sensor prior to the termination event 124.

In one or more implementations, the prediction logic 310 may use one or more weighting techniques (such as a weighted average) to determine the estimated glucose value 132. For example, the prediction logic 310 may associate a relatively smaller weight with the second glucose measurement 130 (a) corresponding to the warm-up period 126 toward the beginning of the warm-up period 126, and the prediction logic 310 may associate a relatively larger weight with the second glucose measurement 130 (a) corresponding to the warm-up period 126 that is closer to the end of the warm-up period 126. In one or more implementations, the prediction logic 310 can associate progressively greater weights with the second glucose measurement 130 (a) corresponding to the warm-up period 126 over time.

FIG. 9 depicts a procedure 900 in an exemplary implementation in which glucose measurements associated with a first glucose sensor are retrospectively updated based on glucose measurements received from a second glucose sensor that replaces the first glucose sensor.

A first glucose measurement data stream is received from a first glucose sensor worn by a user (block 902). By way of example, the bridging system 108 receives the first data stream 118 of the first glucose measurement 128 from the first glucose sensor of the wearable glucose monitoring device 104 (a) worn by the person 102.

A second glucose measurement data stream is received from a second glucose sensor worn by the user in place of the first glucose sensor (block 904). By way of example, the bridging system 108 receives the second data stream 120 of glucose measurements 130 (a) from a second glucose sensor of the wearable glucose monitoring device 104 (b) worn by the person 102 that replaces the first glucose sensor.

At least one glucose measurement in the first glucose measurement data stream is retrospectively updated based on the second data stream of glucose measurements received from the second glucose sensor (block 906). By way of example, the transition period between sensors may include a cooling period 706 corresponding to a predetermined amount of time at the end of the life of the sensor. Alternatively, the data accuracy module 308 may determine the beginning of the cooling period 706 based on the accuracy of the first glucose measurement 128 such that the beginning of the cooling period 706 is determined when the first glucose measurement 128 fails to meet a threshold accuracy. Regardless of whether the cooling period 706 corresponds to a predetermined amount of time or is determined based on the accuracy of the measurements, the bridging system 108 may be configured to retrospectively update at least one of the first glucose measurements 128 corresponding to the cooling period 706.

The bridging system 108 may determine the retrospective glucose value 702 based on the first glucose measurement 128 generated during the cooling period 706 and also based on one or more of the second glucose measurements 130 (a), 130 (b). In one or more scenarios, the data accuracy module 308 may not determine that the first glucose measurement 128 fails to meet the threshold accuracy until the computing device 106 receives the second glucose measurement 130 (a), 130 (b). In practice, the bridging system 108 may determine the retrospective glucose value 702 when the glucose sensor of the wearable glucose monitoring device 104 (b) generates glucose values and transmits them to the computing device 106.

In one or more implementations, the prediction logic 310 may thus be configured to retrospectively generate a glucose value, e.g., given as an input measurement before prediction (e.g., one or more of the first glucose measurements 128 before the cooling period 706), a measurement corresponding to the same time as the prediction (e.g., the first glucose measurement 128 corresponding to the cooling period 706), and a measurement after prediction (e.g., one or more of the second glucose measurements 130 (a), 130 (b)). The prediction logic 310 may use one or more machine learning models and/or weighted averages to generate the retrospective glucose value 702 of the cooling period 706.

FIG. 10 depicts a procedure 1000 in an exemplary implementation in which a warm-up period of a new glucose sensor is ended when a glucose measurement received from the new glucose sensor meets an accuracy threshold.

A warm-up period of a new glucose sensor worn by the user that replaces the previous glucose sensor is initiated (block 1002). During a warm-up period of the new glucose sensor, a new glucose measurement data stream is received from the new glucose sensor (block 1004), and a determination is made as to whether the new glucose measurement data stream received from the new glucose sensor meets an accuracy threshold based at least in part on a previous glucose measurement data stream received from a previous glucose sensor prior to a termination event of the previous glucose sensor (block 1006). By way of example, the prediction logic 310 may determine the accuracy of the data of the second data stream 120 based on the glucose predictions generated by the prediction logic 310 (e.g., using the first data stream 118) and also based on the data of the second data stream 120. The data accuracy module 308 may compare the data of the second data stream 120 to the prediction and determine an accuracy of the data of the second data stream 120 based in part on the comparison.

For example, the data accuracy module 308 may identify differences between the predictions and the data of the second data stream 120 by comparing them. Based on these differences, the data accuracy module 308 may further determine that the data of the second data stream 120 remains within the glucose prediction horizon generated by the prediction logic 310 for a certain threshold amount of time, has a certain threshold frequency, and/or is for a certain threshold number of measurements. Alternatively or in addition, the data accuracy module 308 may calculate one or more metrics related to the data of the second data stream 120 and determine the accuracy of the second data stream 120 based on the metrics. For example, the data accuracy module 308 may calculate the variability of the second glucose measurement 130 (a) of the second data stream 120.

When the accuracy threshold is met, the warm-up period of the new glucose sensor is ended (block 1008). By way of example, when the data accuracy module 308 determines that the calculated variability meets the accuracy threshold, then the data accuracy module 308 may also determine that the warm-up period 126 ends. Notably, by determining the warm-up period 126 using the accuracy of the data of the second data stream 120, the warm-up period 126 may be shortened relative to a predetermined amount of time. The above-described systems and programs may be used in various combinations to implement different methods without departing from the spirit or scope of the described technology. Indeed, at least one method may include aspects that differ from the described procedure and/or the described system according to the described techniques.

Having described exemplary procedures in accordance with one or more implementations, consider now examples of systems and apparatus that can be used to implement the various techniques described herein.

Exemplary systems and apparatus

Fig. 11 illustrates an example of a system, generally indicated at 1100, that includes an example of a computing device 1102 that is representative of one or more computing systems and/or devices that can implement the various techniques described herein. This is illustrated by the inclusion of a bridging system 108. For example, computing device 1102 may be a server of a service provider, a device associated with a client (e.g., a client device), a system-on-chip, and/or any other suitable computing device or computing system.

The exemplary computing device 1102 as illustrated includes a processing system 1104, one or more computer-readable media 1106, and one or more I/O interfaces 1108 communicatively coupled to each other. Although not shown, computing device 1102 may also include a system bus or other data and command transfer system that couples the various components to one another. A system bus may include any of several different bus structures or combinations thereof, such as a memory bus or memory controller, a peripheral bus, a universal serial bus, and/or a processor or local bus that utilizes any of a variety of bus architectures. A variety of other examples are also contemplated, such as control lines and data lines.

The processing system 1104 represents functionality that performs one or more operations using hardware. Thus, the processing system 1104 is shown as including hardware elements 1110 that may be configured as processors, functional blocks, and the like. This may include implementation in hardware as application specific integrated circuits or other logic devices formed using one or more semiconductors. The hardware elements 1110 are not limited by the materials from which they are formed or the processing mechanisms employed therein. For example, the processor may be comprised of semiconductors and/or transistors, such as electronic Integrated Circuits (ICs). In this context, the processor-executable instructions may be electronically-executable instructions.

Computer-readable medium 1106 is shown to include memory/storage 1112. Memory/storage 1112 represents memory/storage capacity associated with one or more computer-readable media. Memory/storage 1112 may include volatile media (such as Random Access Memory (RAM)) and/or nonvolatile media (such as Read Only Memory (ROM), flash memory, optical disks, magnetic disks, and so forth). The memory/storage 1112 may include fixed media (e.g., RAM, ROM, a fixed hard drive, etc.) and removable media (e.g., flash memory, a removable hard drive, an optical disk, and so forth). The computer-readable medium 1106 may be configured in a variety of other ways as described further below.

Input/output interface 1108 represents functionality that allows a user to input commands and information to computing device 1102, and also allows information to be presented to the user and/or other components or devices using various input/output devices. Examples of input devices include keyboards, cursor control devices (e.g., a mouse), microphones, scanners, touch functionality (e.g., capacitive sensors or other sensors configured to detect physical touches), cameras (e.g., which may employ visible wavelengths or non-visible wavelengths (such as infrared frequencies) to recognize movement as gestures that do not involve touches), and so forth. Examples of output devices include a display device (e.g., monitor or projector), speakers, printer, network card, haptic response device, and the like. Accordingly, the computing device 1102 may be configured in a variety of ways as described further below to support user interaction.

Various techniques may be described herein in the general context of software, hardware elements, or program modules. Generally, such modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The terms "module," "functionality," and "component" as used herein generally represent software, firmware, hardware, or a combination thereof. The features of the techniques described herein are platform-independent, meaning that the techniques may be implemented on a variety of commercial computing platforms having a variety of processors.

Implementations of the described modules and techniques may be stored on or transmitted across some form of computer readable media. Computer readable media can include a variety of media that are accessible by computing device 1102. By way of example, and not limitation, computer readable media may comprise "computer readable storage media" and "computer readable signal media".

"computer-readable storage medium" may refer to media and/or devices capable of storing information permanently and/or non-temporarily, rather than merely a signal transmission, carrier wave, or signal itself. Thus, computer-readable storage media refers to non-signal bearing media. Computer-readable storage media include hardware, such as volatile and nonvolatile, removable and non-removable media and/or storage devices, implemented in methods or techniques suitable for storage of information such as computer-readable instructions, data structures, program modules, logic elements/circuits, or other data. Examples of a computer-readable storage medium may include, but are not limited to, RAM, ROM, EEPROM, flash memory or other storage technology, CD-ROM, digital Versatile Disks (DVD) or other optical storage, hard disk, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other storage devices, tangible media, or articles of manufacture adapted to store the desired information and which may be accessed by a computer.

"computer-readable signal media" may refer to signal bearing media configured to transmit instructions to hardware of computing device 1102, such as via a network. Signal media may typically be embodied in a modulated data signal, such as a carrier wave, data signal, or other transport mechanism, and include computer readable instructions, data structures, program modules, or other data. Signal media also include any information delivery media. The term "modulated data signal" means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media.

As previously described, hardware elements 1110 and computer-readable media 1106 represent modules, programmable device logic, and/or fixed device logic that may be implemented in hardware in some embodiments to implement at least some aspects of the techniques described herein, such as executing one or more instructions. The hardware may include integrated circuits or systems on a chip, application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs), complex Programmable Logic Devices (CPLDs), and other embodied components in silicon or other hardware. In this context, the hardware may operate as a processing device executing program tasks defined by instructions and/or logic embodied by the hardware, as well as hardware for storing instructions for execution (e.g., the previously described computer-readable storage medium).

Combinations of the foregoing may also be employed to implement the various techniques described herein. Thus, software, hardware, or executable modules may be implemented as one or more instructions and/or logic components embodied on some form of computer readable storage medium and/or by one or more hardware elements 1110. The computing device 1102 may be configured to implement specific instructions and/or functions corresponding to software and/or hardware modules. Thus, implementations of modules that may be executed by the computing device 1102 as software may be at least partially implemented in hardware, for example, through use of computer-readable storage media and/or hardware elements 1110 of the processing system 1104. The instructions and/or functions may be executed/operated on by one or more articles of manufacture (e.g., one or more computing devices 1102 and/or processing systems 1104) to implement the techniques, modules, and examples described herein.

The techniques described herein may be supported by various configurations of computing device 1102 and are not limited to the specific examples of techniques described herein. This functionality may also be implemented in whole or in part through the use of a distributed system, such as through the "cloud" 1114 via platform 1116 as described below.

Cloud 1114 includes and/or represents platform 1116 for resource 1118. Platform 1116 abstracts underlying functionality of hardware (e.g., servers) and software resources of cloud 1114. Resource 1118 may include applications and/or data that are available when computer processing is performed on a server remote from computing device 1102. Resources 1118 may also include services provided over the internet and/or over a user network such as a cellular or Wi-Fi network.

Platform 1116 may abstract resources and functionality to connect computing device 1102 with other computing devices. The platform 1116 may also be used to abstract scaling of resources to provide corresponding levels of scaling to meet the demands on the resources 1118 implemented via the platform 1116. Thus, in an interconnected device implementation, the specific implementation of the functionality described herein may be distributed throughout the system 1100. For example, the functionality may be implemented in part on the computing device 1102 and via the platform 1116 that abstracts the functionality of the cloud 1114.

Conclusion(s)

Although the systems and techniques have been described in language specific to structural features and/or methodological acts, it is to be understood that the systems and techniques defined in the appended claims are not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as exemplary forms of implementing the claimed subject matter.

Claims (28)

1. A method, the method comprising:

receiving a first glucose measurement data stream from a first glucose sensor worn by a user;

detecting a termination event of the first glucose sensor;

receiving a second glucose measurement data stream from a second glucose sensor worn by the user in place of the first glucose sensor; and

During a warm-up period of the second glucose sensor, an estimated glucose value of the user is output based on both the first glucose measurement data stream received from the first glucose sensor and the second glucose measurement data stream received from the second glucose sensor prior to the termination event.

2. The method of claim 1, wherein the outputting further comprises:

predicting a glucose value of the user during the warm-up period based on the first glucose measurement data stream received from the first glucose sensor prior to the termination event of the first glucose sensor; and

the estimated glucose value is output based on both the predicted glucose value and the second glucose measurement data stream received from the second glucose sensor.

3. The method of claim 2, wherein the predicting is further based on at least one of food record data or activity data.

4. The method of claim 2 or 3, wherein the outputting further comprises determining a weighted average between the predicted glucose value and the second glucose measurement data stream received from the second glucose sensor.

5. The method of any one of claims 2 to 4, further comprising: after the warm-up period ends, the estimated glucose value is output based on the second glucose measurement data stream received in real-time from the second glucose sensor worn by the user.

6. The method of any one of claims 1-5, wherein the warm-up period of the second glucose sensor comprises a predetermined amount of time.

7. The method of any one of claims 1 to 6, further comprising:

determining an accuracy of the glucose measurement of the second data stream; and

the warm-up period of the second glucose sensor is ended when the accuracy of the glucose measurement of the second data stream meets an accuracy threshold.

8. The method of claim 7, wherein the accuracy of the glucose measurement of the second data stream is based on a comparison of the glucose measurement of the second data stream to a predicted glucose value determined based on the first glucose measurement data stream received prior to the termination event.

9. The method of any one of claims 1 to 8, the method further comprising:

predicting a glucose value of the user based on the first glucose measurement data stream received from the first glucose sensor prior to the termination event; and

the predicted glucose value is output for a period of time after the termination event and before the second glucose sensor is worn by the user.

10. The method of any one of claims 1-9, wherein the detecting the termination event of the first glucose sensor comprises detecting the termination event when a timer associated with the first glucose sensor expires.

11. The method of any one of claims 1 to 10, wherein the detecting the termination event of the first glucose sensor comprises detecting the termination event based on a loss of connectivity with the first glucose sensor or based on user interaction with a user interface.

12. The method of any one of claims 1 to 11, wherein the first glucose sensor is inserted subcutaneously into the skin of the user, and wherein the termination event is detected in response to removing the first glucose sensor from the skin of the user.

13. The method of any one of claims 1-12, wherein the outputting comprises causing the estimated glucose value to be displayed in a user interface of a computing device communicatively coupled to the second glucose sensor.

14. The method of any one of claims 1 to 13, wherein the first glucose sensor and the second glucose sensor are disposable Continuous Glucose Monitoring (CGM) sensors.

15. A method, the method comprising:

receiving a first glucose measurement data stream from a first glucose sensor worn by a user;

receiving a second glucose measurement data stream from a second glucose sensor worn by the user in place of the first glucose sensor; and

at least one glucose measurement in the first glucose measurement data stream is retrospectively updated based on the second glucose measurement data stream received from the second glucose sensor.

16. A method, the method comprising:

initiating a warm-up period of a new glucose sensor worn by the user that replaces the previous glucose sensor;

receiving a new glucose measurement data stream from the new glucose sensor during the warm-up period of the new glucose sensor;

Determining whether the new glucose measurement data stream received from the new glucose sensor meets an accuracy threshold based at least in part on a previous glucose measurement data stream received from the previous glucose sensor prior to a termination event of the previous glucose sensor; and

when the accuracy threshold is met, the warm-up period of the new glucose sensor is ended.

17. A method, the method comprising:

outputting an estimated glucose value for the user based on a first glucose measurement data stream received from a first glucose sensor worn by the user;

during a warm-up period of a second glucose sensor that replaces the first glucose sensor, outputting an estimated glucose value of the user based on the first glucose measurement data stream received from the first glucose sensor and a second glucose measurement data stream received from the second glucose sensor; and

after the warm-up period of the second glucose sensor is over, outputting an estimated glucose value for the user based on the second glucose measurement data stream received from the second glucose sensor.

18. The method of claim 17, the method further comprising: after a termination event associated with the first glucose sensor and before the warm-up period of the second glucose sensor begins, outputting a predicted glucose value for the user based on the first glucose measurement data stream received from the first glucose sensor prior to the termination event.

19. The method of claim 17 or 18, the method further comprising: after a termination event associated with the first glucose sensor and before a warm-up period of the second glucose sensor begins, the predicted glucose value of the user is prevented from being output.

20. The method of any one of claims 17 to 19, wherein the first glucose sensor and the second glucose sensor are disposable Continuous Glucose Monitoring (CGM) sensors.

21. A system, the system comprising:

one or more processors; and

a memory storing computer-readable instructions executable by the one or more processors to perform operations comprising:

receiving a first glucose measurement data stream from a first glucose sensor worn by a user;

Detecting a termination event of the first glucose sensor;

receiving a second glucose measurement data stream from a second glucose sensor worn by the user in place of the first glucose sensor; and

during a warm-up period of the second glucose sensor, an estimated glucose value of the user is output based on both the first glucose measurement data stream received from the first glucose sensor and the second glucose measurement data stream received from the second glucose sensor prior to the termination event.

22. A system, the system comprising:

one or more processors; and

a memory storing computer-readable instructions executable by the one or more processors to perform operations comprising:

receiving a first glucose measurement data stream from a first glucose sensor worn by a user;

receiving a second glucose measurement data stream from a second glucose sensor worn by the user in place of the first glucose sensor; and

at least one glucose measurement in the first glucose measurement data stream is retrospectively updated based on the second glucose measurement data stream received from the second glucose sensor.

23. A system, the system comprising:

one or more processors; and

a memory storing computer-readable instructions executable by the one or more processors to perform operations comprising:

initiating a warm-up period of a new glucose sensor worn by the user that replaces the previous glucose sensor;

receiving a new glucose measurement data stream from the new glucose sensor during the warm-up period of the new glucose sensor;

determining whether the new glucose measurement data stream received from the new glucose sensor meets an accuracy threshold based at least in part on a previous glucose measurement data stream received from the previous glucose sensor prior to a termination event of the previous glucose sensor; and

when the accuracy threshold is met, the warm-up period of the new glucose sensor is ended.

24. A system, the system comprising:

one or more processors; and

a memory storing computer-readable instructions executable by the one or more processors to perform operations comprising:

outputting an estimated glucose value for the user based on a first glucose measurement data stream received from a first glucose sensor worn by the user;

During a warm-up period of a second glucose sensor that replaces the first glucose sensor, outputting an estimated glucose value of the user based on the first glucose measurement data stream received from the first glucose sensor and a second glucose measurement data stream received from the second glucose sensor; and

after the warm-up period of the second glucose sensor is over, outputting an estimated glucose value for the user based on the second glucose measurement data stream received from the second glucose sensor.

25. A non-transitory computer-readable medium that is executable by one or more processors to perform instructions comprising:

receiving a first glucose measurement data stream from a first glucose sensor worn by a user;

detecting a termination event of the first glucose sensor;

receiving a second glucose measurement data stream from a second glucose sensor worn by the user in place of the first glucose sensor; and

during a warm-up period of the second glucose sensor, an estimated glucose value of the user is output based on both the first glucose measurement data stream received from the first glucose sensor and the second glucose measurement data stream received from the second glucose sensor prior to the termination event.

26. A non-transitory computer-readable medium that is executable by one or more processors to perform instructions comprising:

receiving a first glucose measurement data stream from a first glucose sensor worn by a user;

receiving a second glucose measurement data stream from a second glucose sensor worn by the user in place of the first glucose sensor; and

at least one glucose measurement in the first glucose measurement data stream is retrospectively updated based on the second glucose measurement data stream received from the second glucose sensor.

27. A non-transitory computer-readable medium that is executable by one or more processors to perform instructions comprising:

initiating a warm-up period of a new glucose sensor worn by the user that replaces the previous glucose sensor;

receiving a new glucose measurement data stream from the new glucose sensor during the warm-up period of the new glucose sensor;

determining whether the new glucose measurement data stream received from the new glucose sensor meets an accuracy threshold based at least in part on a previous glucose measurement data stream received from the previous glucose sensor prior to a termination event of the previous glucose sensor; and

When the accuracy threshold is met, the warm-up period of the new glucose sensor is ended.

28. A non-transitory computer-readable medium that is executable by one or more processors to perform instructions comprising:

outputting an estimated glucose value for the user based on a first glucose measurement data stream received from a first glucose sensor worn by the user;

during a warm-up period of a second glucose sensor that replaces the first glucose sensor, outputting an estimated glucose value of the user based on the first glucose measurement data stream received from the first glucose sensor and a second glucose measurement data stream received from the second glucose sensor; and

after the warm-up period of the second glucose sensor is over, outputting an estimated glucose value for the user based on the second glucose measurement data stream received from the second glucose sensor.