Integration of additional sensors into adaptation of AID systems

Abstract

This disclosure is techniques, devices, and systems that provide adjustments to parameter settings for insulin delivery algorithms based on inputs from multiple universal sensor devices. Most of the universal sensor devices provide sensor readings that are not used as input to drug delivery algorithms. The universal sensor device may be operable to detect a characteristic, such as a change in a state of a user. The processor may evaluate a sensor reading provided by a particular sensor relative to a sensor baseline reading of the particular sensor. Using results of the evaluation, the processor may calculate adjustments to one or more parameter settings of the drug delivery algorithm. The dose of the medicament may be modified based on an adjustment of one or more parameter settings.

Description

[0001] Cross - Reference to Related Applications

[0002] This application claims the priority and benefit of U.S. Application No. 63 / 375,561, filed on September 14, 2022, the entire content of which is incorporated herein by reference. Background Art

[0003] Because a user's insulin requirements vary over time, adaptation of an automated insulin delivery (AID) system is crucial to ensure that each user's insulin therapy is properly maintained. Adaptation of an AID system based solely on glucose and insulin delivery history may not provide sufficient granularity for the control result to accurately adapt the AID system.

[0004] It would be beneficial to have a device or algorithm that utilizes data obtainable from multiple sensors that measure or generate data with sufficient granularity to enable an AID system to be accurately adapted. Summary of the Invention

[0005] This Summary of the Invention is provided to introduce a selection of concepts in a simplified form that will be further described in the Detailed Description below. This Summary of the Invention is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to assist in determining the scope of the claimed subject matter.

[0006] According to an example of the disclosed subject matter, a system is disclosed that includes a plurality of sensors, a memory, and a processor. Each sensor of the plurality of sensors can be configured to generate a corresponding output related to the state of a user. The memory can store a plurality of sensor baseline readings of the corresponding sensors of the plurality of sensors. When executing programming instructions, the processor is operable to obtain corresponding sensor readings output from the corresponding sensors of the plurality of sensors. The corresponding sensor readings can be evaluated against the corresponding sensor baseline readings stored in the plurality of sensor baseline readings that correspond to the corresponding sensors. Based on this evaluation, an indication to adjust insulin delivery algorithm parameters can be generated.

[0007] Another example of a non - transitory computer - readable medium is provided. When executing programming instructions implemented in the non - transitory computer - readable medium, the processor is operable to obtain corresponding sensor readings output from the corresponding sensors of the plurality of sensors. The corresponding sensor readings can be evaluated against the corresponding sensor baseline readings stored in the plurality of sensor baseline readings that correspond to the corresponding sensors. Based on this evaluation, insulin delivery algorithm parameters can be adjusted, and an indication to adjust insulin delivery algorithm parameters can be generated.

[0008] An example of a wearable drug delivery device is provided. The wearable drug delivery device can include a memory, a communication circuitry, and a processor. The memory can store multiple sensor baseline readings of respective sensors among a plurality of sensors and a drug delivery algorithm. The communication circuitry can be operable to receive wireless signals. The processor can be coupled to the memory and the communication circuitry. The processor can be operable to execute programming instructions and, when executing the programming instructions, can be operable to obtain sensor readings from one or more sensors among the plurality of sensors. Based on the received sensor readings and corresponding baseline sensor readings, the processor can determine that the sensor readings received from one or more sensors among the plurality of sensors indicate that a parameter input to the drug delivery algorithm is to be modified. An adaptation parameter can be calculated based on a sum of individual insulin delivery parameters generated for each of the respective one or more sensors among the plurality of sensors. The parameter input to the drug delivery algorithm can be modified. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 An example of a drug delivery system suitable for implementing the subject matter described herein is illustrated.

[0010] Figure 2 A non-drug delivery related sensor network operable to communicate with the drug delivery system is illustrated.

[0011] Figure 3 is a flowchart of an example process that utilizes the output of a respective non-drug delivery related sensor according to an example disclosed herein.

[0012] Figure 4 A flowchart illustrating a detailed process of generating an adaptation parameter that can be used to modify a parameter of a drug delivery algorithm is shown. DETAILED DESCRIPTION

[0013] The following discussion provides a generalization framework for adapting parameters that affect the performance of an MDA based on readings provided by general sensors (as opposed to sensors that provide inputs used in determining a drug dose by a drug delivery algorithm (MDA) of an AID system). The general sensor readings are used to inform parameters that control how the MDA responds to analyte sensor inputs (such as inputs from a continuous glucose monitor, a ketone monitor, etc.). The general sensor readings, when obtained individually, one or more in combination, or as a totality, can indicate that the user has a higher or lower risk of a glucose concentration associated with hypoglycemia or hyperglycemia.

[0014] A Medication Delivery Algorithm (MDA) can include a system based on an "artificial pancreas" algorithm, or more generally, can include an artificial pancreas (AP) application that can be used in an AID system. For ease of discussion, the computer programs and computer applications that implement the medication delivery algorithm or application may be referred to herein as "AP applications". The AP application can be configured to provide automated delivery of insulin based on analyte sensor inputs (such as signals received from an analyte sensor such as a continuous glucose monitor (CGM), a ketone sensor, etc.). Signals from the analyte sensor can include glucose measurements, timestamps, and the like.

[0015] The MDA can include a number of factors used in calculating the insulin dose to be provided by automated insulin delivery. Different parameters can include basal settings, setpoints, the Q:R ratio of the cost function (e.g., the ratio of the cost of glucose deviation from the target BG level to the cost of insulin delivery deviation from the expected delivery amount), and gain parameters, among other parameters.

[0016] Additionally or alternatively, although the disclosed examples may be described with reference to closed-loop algorithm implementations (i.e., without user involvement during normal operation), variants of the disclosed examples can be implemented to enable open-loop use (i.e., with user involvement) or hybrid closed-loop use (i.e., very limited user involvement). Open-loop implementations allow for the use of different forms of insulin delivery, such as smart pens, syringes, etc. For example, the disclosed AP applications and algorithms can be operable to perform various functions related to open-loop operation, such as generating prompts that request input of information such as diabetes type, weight, or age. Similarly, the AP application or algorithm can receive the dose amount of insulin from the user via a user interface. Other open-loop actions can also be achieved by adjusting user settings, etc. in the AP application or algorithm.

[0017] Now, systems, devices, computer-readable media, and methods according to the present disclosure will be described more fully hereinafter with reference to the accompanying drawings, in which one or more examples are shown. The systems, devices, and methods described herein can be implemented in many different forms and should not be construed as limited to the examples set forth herein. Rather, these examples are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the technology and devices to those skilled in the art. Each of the systems, devices, media, and methods disclosed herein provides one or more advantages over conventional systems, components, and methods.

[0018] Figure 1 A drug delivery system is illustrated.

[0019] In some examples, the drug delivery system 100 is adapted to deliver insulin to a user in accordance with the disclosed embodiments. The drug delivery system 100 may include a wearable drug delivery device 102, a controller 104, and an analyte sensor 106.

[0020] The wearable drug delivery device 102 may be a wearable device worn on the user's body. The wearable drug delivery device 102 may be a multi-part device. For example, the wearable drug delivery device 102 may have a first part and a second part coupled together. The first part and / or the second part may be inserted or slid into a tray or holder adhered to the user's body, and the first part and / or the second part may be removed from the tray. If a first part and a second part are used, the first part may include reusable components (e.g., an electronic circuit system, a processor, a memory, a pump mechanism, and possibly a rechargeable battery), and the second part may include disposable components (e.g., a reservoir, a needle and / or cannula, a disposable battery, and other parts or components that come into contact with a liquid drug or agent). Additionally, the first part and the second part may each include their own housing or may be combined together to form a single housing. The wearable drug delivery device 102 may be directly coupled to the user (e.g., via an adhesive, directly, via a tray, etc., directly attached to a body part and / or skin of the user). In an example, the surface of the wearable drug delivery device 102 or the tray to which the wearable drug delivery device 102 is coupled may include an adhesive to facilitate attachment to the user's skin.

[0021] The wearable drug delivery device 102 may include a processor 114. The processor 114 may be implemented in hardware, software, or any combination thereof. The processor 114 may be, for example, a microprocessor, logic circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or a microprocessor coupled to a memory. The processor 114 may maintain the date and time and be operable to perform other functions (e.g., calculations, etc.). The processor 114 is operable to execute a control application 126 stored in the memory 112 such that the processor 114 can direct the operation of the wearable drug delivery device 102. The control application 126 may control the insulin delivery to the user according to the MDA control scheme described herein. For example, the control application 126 may be an MDA algorithm. The memory 112 may hold settings 124 for the user, such as specific factor settings, subjective insulin requirement parameters settings, MDA settings, such as maximum insulin delivery, insulin sensitivity settings, total daily insulin (TDI) settings, insulin decay settings, etc. The memory may also store other data 129, such as total daily insulin values, blood glucose measurements from the analyte sensor 106 or the controller 104, the amount of insulin doses (both basal and bolus) for the previous few minutes, hours, days, weeks, or months, etc. The analyte sensor 106 may be operable to collect the user's physiological condition data (such as blood glucose measurements and timestamps), which may be shared with the wearable drug delivery device 102, the controller 104, or both. For example, the communication circuitry 142 of the wearable drug delivery device 102 may be operable to communicate with the analyte sensor 106 and the controller 104, as well as devices 130, 133, and 134. The communication circuitry 142 may be operable to communicate via Bluetooth, cellular communication, and / or other wireless protocols. Although not shown, the memory 112 may include both a main memory and a secondary memory. The memory 112 may include random access memory (RAM), read only memory (ROM), optical storage devices, magnetic storage devices, removable storage media, solid state storage devices, etc.

[0022] The wearable drug delivery device 102 may include a reservoir 120. The reservoir 120 may be operable to store a drug, medication, or therapeutic agent suitable for automated delivery. A fluid path to the user may be provided via a tube and a needle / cannula (not shown). The fluid path may include, for example, a tube coupling the wearable drug delivery device 102 (e.g., via a tube coupling a needle or cannula to the reservoir 120) to the user. The wearable drug delivery device 102 may be operable to discharge a drug, medication, or therapeutic agent (such as insulin) from the reservoir 120 based on a control signal from the processor 114, thereby delivering a dose of the drug, medication, or therapeutic agent (such as insulin) to the user via the fluid path. The processor 114 may be operable to cause insulin to be discharged from the reservoir 120.

[0023] One or more communication links 128 may exist, where one or more devices are physically separated from the wearable drug delivery device 102, and the one or more devices include, for example, a controller 104 and / or a sensor 106 of a user and / or the user's caregiver. The communication link 128 may include any wired or wireless communication link operating according to any known communication protocol or standard (such as Wi-Fi, near field communication standards, cellular standards, or any other wireless protocol). The analyte sensor 106 may communicate with the wearable drug delivery device 102 via a wireless communication link 131 and / or may communicate with the controller 104 via a wireless communication link 137.

[0024] The wearable drug delivery device 102 may also include a user interface 116, such as an integrated display device, for displaying information to the user and, in some embodiments, for receiving information from the user. For example, the user interface 116 may include a touch screen that enables the user to provide input and / or one or more input devices, such as buttons, knobs, or keyboards.

[0025] In addition, the processor 114 may be operable to receive data or information from the analyte sensor 106 and other devices that may be operable to communicate with the wearable drug delivery device 102.

[0026] The wearable drug delivery device 102 may dock with a network 108. The network 108 may include a local area network (LAN), a wide area network (WAN), or a combination thereof. A computing device 143 may dock with the network, and the computing device may communicate with the insulin delivery device 102. The computing device 143 may be a healthcare provider device through which the user's controller 104 may interact to obtain information, store settings, etc. An AID algorithm that presents a graphical user interface on the computing device 143 as the control application 120 or in cooperation with the control application 120 may enable the input and presentation of information related to the AID algorithm.

[0027] The drug delivery system 100 may include an analyte sensor 106 for sensing the level of one or more analytes of a user. The analyte levels may be used as physiological condition data and sent to the controller 104 and / or the wearable drug delivery device 102. The sensor 106 may be coupled to the user, for example, by an adhesive, and may provide information or data regarding one or more medical conditions and / or physical attributes of the user. The sensor 106 may be a continuous glucose monitor (CGM), or another type of device or sensor that provides a blood glucose measurement, which is operable to provide a blood glucose concentration measurement. The sensor 106 may be physically separated from the wearable drug delivery device 102 or may be an integrated component thereof. The sensor 106 may provide physiological condition data indicating the measured or detected blood glucose level of the user to the processor 114 and / or the processor 119. The information or data provided by the sensor 106 may be used to modify the insulin delivery schedule, thereby adjusting the drug delivery operation of the wearable drug delivery device 102.

[0028] The drug delivery system 100 may further include a controller 104. In the example shown, the controller 104 may include a processor 119 and a memory 118. The controller 104 may be a dedicated device, such as a dedicated personal diabetes manager (PDM) device. The controller 104 may be a programmed general-purpose device, which is a portable electronic device, such as any portable electronic device, smartphone, smartwatch, fitness device, tablet computer, etc., which includes, for example, a dedicated processor, such as a microprocessor. The controller 104 may be used to program or adjust the operation of the wearable drug delivery device 102 and / or the sensor 106. The processor 119 may perform processing to manage the blood glucose level of the user and to control the delivery of a drug or therapeutic agent to the user. The processor 119 may also be operable to execute the programming code stored in the memory 118. For example, the memory 118 may be operable to store a control application 120, such as an AID algorithm executed by the processor 119. The control application 120 may be responsible for controlling the wearable drug delivery device 102, including automated insulin delivery based on recommendations and instructions from the AID algorithm, such as those described herein.

[0029] Memory 118 may store one or more applications, such as control application 120, and settings 121 of insulin delivery device 102 as described above. Additionally, memory 118 may be operable to store other data and / or computer programs 126, such as drug delivery history, blood glucose measurements over a period of time, total daily insulin values, etc. For example, memory 118 is coupled to processor 119 and is operable to store programming instructions (such as control application 120 and settings 121), as well as data related to the user's blood glucose measurements (such as other data 126), and / or data related to the amount of insulin dispensed by wearable drug delivery device 102.

[0030] Controller 104 may include a user interface (UI) 123 for communicating with the user. User interface 123 may include a display for presenting information, such as a touch screen. When the touch screen is a touch screen, the touch screen may also be used to receive input. User interface 123 may also include input elements, such as a keyboard, buttons, knobs, etc. In an operational example, user interface 123 may include a touch screen display controllable by processor 119 and may be operable to present a graphical user interface, and in response to received input, the touch screen display may be operable to generate a signal indicative of a subjective insulin demand parameter. Under the control of processor 119, the touch screen display may be operable to receive input from computer applications, such as a consumer food tracker, a carbohydrate calculator, a map application based on the user's GPS, Wi-Fi, or Bluetooth location, etc.

[0031] Controller 104 may interact with a network via a wireless communication link in wireless communication link 128, such as a LAN or WAN or a combination of such networks, which provides one or more servers or cloud-based services 110 via communication circuitry 122. Communication circuitry 122, which may include transceivers 127 and 125, may be coupled to processor 119. Communication circuitry 122 may be operable to transmit communication signals (e.g., command and control signals) to and receive communication signals from wearable drug delivery device 102 and analyte sensor 106 (e.g., via transceiver 127 or 125). In an example, communication circuitry 122 may include a first transceiver (such as 125) and a second transceiver (such as 127), where the first transceiver may be a Bluetooth transceiver operable to communicate with communication circuitry 122 of wearable drug delivery device 102, and the second transceiver may be a cellular or Wi-Fi transceiver operable to communicate with computing device 143 or with cloud-based service 110 via network 108.

[0032] The cloud-based service 110 can be operable to store user historical information, such as blood glucose measurements over a set period of time (e.g., days, months, years), medication delivery history including insulin delivery amounts (both basal and bolus doses) and insulin delivery times, type of insulin delivered, indicated or detected meal times, blood glucose measurement trends or offsets, or other user-related diabetes treatment information, specific factor settings including default settings, current settings, and past settings, and so on.

[0033] Other devices, such as the smart accessory device 130 (e.g., smartwatch, etc.), fitness device 133, and other sensor devices 134 can be part of the medication delivery system 100. For example, the other sensor devices 134 can be at least one of a heart rate monitor, oxygen sensor, carbohydrate calculator, pedometer, meal tracker application, blood glucose sensor, ketone sensor, etc. These sensor devices 130, 133, and 134 can communicate with the wearable medication delivery device 102 to provide sensor readings and other values related to the readings given by the respective sensor devices to the wearable medication delivery device 102. These sensor devices 130, 133, and 134 can execute computer programming instructions to perform some control functions otherwise performed by the processor 114 or the processor 119. These sensor devices 130, 133, and 134 can include a user interface, such as a touchscreen display, for displaying information such as accelerometer data, steps, heart rate, etc., such as those described in the examples referenced Figures 1 - 3 For example, the display can be operable to present a graphical user interface for receiving inputs, such as inputs for receiving meal amounts, estimates of the carbohydrate amounts in the meal, etc. These sensor devices 130, 133, and 134 can also have a wireless communication connection with the sensor 106 to directly receive blood glucose level data or to receive the presentation of the graphical user interface in parallel, as Figure 1 shown. In addition, the smart accessory device 130 can be operable to execute one or more programming applications that present information about when the user eats, the amount of sleep of the user, the amount of screen time of the user, as well as applications related to stress levels, the number of times the user checks their blood glucose level, meal amounts, meal fat content, meal frequency, number of restaurant visits per week, number of sick days, number of stress days, number of days of participating in meditation and gentle exercise, medications and supplements used by the user, etc. Examples of other devices 134 can include smart scales (such as Bluetooth scales, etc.), exercise monitors / timers, heart rate and / or heart rate variability monitors, blood oxygen monitors, etc.

[0034] In an operating example, a processor (such as 114 or 119) in a drug delivery system can be operable to receive physiological data inputs from devices external to the drug delivery system (such as 133, 130, and other devices 134) via a communication circuitry (such as 142 of the wearable drug delivery device 102 or 122 of the controller 104). The processor can utilize the physiological data inputs to validate an assessment of glucose measurements over a period of time, as discussed in more detail with reference to the following examples.

[0035] In another operating example, the controller 104 can be operable to execute programming code that causes the processor 119 of the controller 104 to also perform different functions. For example, the processor 119 of the controller 104 can execute an AID algorithm, which is one of the control applications 120 stored in the memory 112 or memory 118. The processor can be operable to present on a user interface (for at least one component of the user interface 123). The user interface 123 can be a touchscreen display controlled by the processor 119, and the user interface 123 is operable to present a graphical user interface that provides an input for subjective insulin demand parameters that can be used by the AID algorithm. The processor 119 can cause the graphical user interface to be presented, thereby providing an input device that enables the input of subjective insulin demand parameters. The AID algorithm can generate instructions for the pump 118 to deliver basal insulin, etc., to the user.

[0036] The processor 119 is also operable to collect user-related physiological condition data from a sensor (such as the analyte sensor 106) or heart rate data from, for example, a fitness device 133 or a smart accessory device 130. In an example, the processor 119 that executes the AID algorithm can determine an insulin dose to be delivered based on the collected user physiological condition and specific factors determined based on the subjective insulin demand parameters. The processor 119 can output a control signal to the wearable drug delivery device 102 via one of the transceivers 125 or 127. The output signal can cause the processor 114 to transmit a command signal to the pump 118 based on the output of the AID algorithm to deliver a drug dose related to the determined insulin dose in the reservoir 120 to the user.

[0037] The drug delivery system 100 operates according to an operating cycle that repeats after a period of time or upon completion of a previous cycle. As mentioned, during each operating cycle, the analyte sensor 106 of the drug delivery system 100 can obtain one or more measurements of the analyte in the user's blood, such as a blood glucose value and / or a ketone level value. Still during the operating cycle, the (one or more) analyte measurement values can be transmitted via a communication circuitry (not shown) in the analyte sensor 106 to a processor (e.g., the processor 119 at the controller 104 or the processor 114 of the wearable drug delivery device 102, or both). The processor 119 or 114 can evaluate the received analyte measurement values and, based on an algorithm executed by the processor 119 or 114, recommend a dose of a liquid drug (such as the doses listed herein) to be delivered to the user. The wearable drug delivery system 100 is operable to deliver the recommended dose to the user. Upon completion of the delivery of the recommended dose, the current operating cycle ends and a new operating cycle begins, in which the above-described process is repeated. The duration of the operating cycle can be approximately 5 minutes, but other durations are available, such as 2 minutes, 10 minutes, etc. In some examples, the analyte sensor 106 can provide multiple analyte measurement values during the operating cycle, and the processor 119 or 114 can process or not process each received analyte measurement value.

[0038] The wearable drug delivery device 102 generally has a life cycle based on the amount of liquid drug stored in the reservoir 120 of the wearable drug delivery device 102 and / or the amount of liquid drug delivered to the user. The AID application or algorithm can use multiple parameters, such as blood glucose measurements, total daily insulin, insulin onboard, etc., in determining the amount of liquid drug that has been delivered. In an operating example, the processor 119 of the controller 104 can be operable to evaluate the effectiveness of the control of the AID algorithm of the drug delivery device 102. For example, the processor 119 can be operable to determine a customized glycemic index value for different food items or combinations of food items (e.g., meals such as breakfast, lunch, or dinner, and snacks or sandwiches, etc.) using an indication of carbohydrates, and can perform functions or calculations more detailedly described by examples of reference Figure 2 and Figure 3 thereof.

[0039] Although system 100 is described with reference to insulin delivery and the use of AID algorithms, system 100 can be operable to implement a drug delivery regimen using a variety of different liquids or therapeutic agents via a drug delivery algorithm. The liquid agent can be or include any liquid form of a drug that can be administered via a subcutaneous cannula by a drug delivery device, including, for example, insulin, glucagon-like peptide-1 (GLP-1), pramlintide, glucagon, co-formulations of two or more of GLP-1, pramlintide, and insulin; and analgesic drugs such as opioids or anesthetics (e.g., morphine, etc.), methadone, arthritis drugs, hormones such as estrogen and testosterone, antihypertensive drugs, chemotherapeutic drugs, fertility drugs, etc.

[0040] Figure 2 An example of an ecosystem of sensors in accordance with examples disclosed herein is illustrated, which sensors generate sensor readings or tracking information that generates (generally) data values not used by an AID system and an MDA (e.g., sensor readings or tracking information other than blood glucose measurements). In some embodiments, most of the sensors in the system are (generally) not used by an AID system or an MDA, particularly where most refers to more than half of the sensors. For example, sensor ecosystem 200 can include a plurality of general sensors that provide a wide range of sensor readings. General sensors can include, for example, accelerometers, gyroscopes, pedometers, activity monitors, blood oxygen (O 2 ) sensors, heart rate monitors, sleep monitors, stress monitors, caffeine intake trackers, alcohol intake trackers, water intake trackers, meal frequency counters, meal content calculators, carbohydrate calculators, clocks, ambient light detectors, screen time trackers, ambient temperature monitors, humidity sensors, sweat sensors, etc.

[0041] For example, sensor ecosystem 200 can include a consumer food tracker 211, a drug delivery system 212, consumer fitness devices and applications 215, a scale 217, drugs and supplements 219, consumer applications and circadian rhythm data 222, etc. Drug delivery system 212 can include an analyte sensor, a drug delivery device 216, and a controller 218 similar to those discussed with reference to Figure 1 those.

[0042] One or more components of the drug delivery system 212 include a communication circuitry (not shown in this example) that is operable to communicate with a data network 220 and / or directly communicate with one or more devices in the sensor ecosystem 200 via a direct wireless connection 220a (e.g., Bluetooth, Wi-Fi, etc.). For example, the consumer food tracker 211, the consumer fitness device and application 215, and other wearable devices 213 can provide data (as shown in Table 2 below), such as accelerometer sensor readings, GPS information related to the frequency of co-location with restaurants or the frequency of restaurant visits per week (number of times in the previous week / 7 days), meal frequency, meal amount, meal carbohydrate content, meal fat content, meal protein content, caffeine intake, hours of sleep, etc. These types of data are generally not included as inputs for setting parameter values used in, for example, drug delivery algorithms (or AID systems). The data provided can be used to adapt parameter settings, such as glucose control targets, clinical input parameters (e.g., TDI), cost function parameters (such as the R(t) value of the Q:R cost function ratio), insulin-to-carbohydrate ratios, or correction factors (e.g., insulin sensitivity). Other factors, such as basal dose (e.g., B(t)) and system gain (e.g., K(t)) can also be adapted using the techniques described herein.

[0043] Return Figure 2 , the scale 217 and the analyte sensor 214 can measure the user's physiological attributes or can receive the user's physiological attributes from the user as inputs to the information of the corresponding devices related to the user's physiological attributes.

[0044] In the processes described in reference Figure 3 and subsequent content, the general sensor n can provide readings S that deviate from the baseline sensor readings of a specific sensor over time n (i.e., general sensor n values), and can also provide an indication of the user's current or potential experience of a specific hypoglycemic or hyperglycemic incidence. The indication generated can be based solely on the specific sensor deviation or, in combination, on the specific sensor deviation and other sensor deviations from the baseline.

[0045] The processor can receive sensor readings as inputs from multiple different sensors, and based on an evaluation of the received sensor readings, the processor can, based on the (one or more) general sensors S n(one or more) outputs, various values ​​are applied to various parameters applied by the AID system / MDA to adjust the AID system. For example, the processor can change (e.g., increase or decrease) one or more of the following adjustment parameter settings of the AID system / MDA; glucose control target; blood glucose set point SP(t); input clinical parameters (such as total daily insulin (TDI)); Q:R ratio of the cost function, for example by adjusting the value R(t) of the cost function; insulin to carbohydrate (IC) ratio; correction factor (also known as insulin sensitivity), basal dose rate B(t); gain K(t), etc. With respect to the Q:R ratio and R(t) of the cost function, the R value is a function of time and represents a coefficient of the insulin cost determined based on the (potential) deviation of the insulin delivery amount from the expected insulin delivery amount.

[0046] Figure 3 The process of determining whether to adjust the parameters of the insulin delivery algorithm is illustrated. Process 300 can be performed by a processor within a drug delivery system. For example, a processor and a memory as part of a wearable drug delivery device can be operable to directly or indirectly (e.g., via controller 104) receive input from a plurality of sensors configured to detect a physiological condition of a user (also referred to as a state of the user). The memory can be operable to store a plurality of sensor baseline readings of corresponding sensors in the plurality of sensors.

[0047] For example, the processor is executing the implementation Figure 3 The programming instructions of the process 300 may be operable to obtain a corresponding sensor S from the plurality of sensors S. n Relevant sensor readings are received (step 310). The processor may be operable to receive the respective sensor readings continuously, at periodic intervals based on a request from the processor, or according to a scheduled delivery. For example, some sensors may run continuously and provide substantially real-time values, such as a heart rate monitor, while other sensors may generate outputs (related to the user's state) every wearable drug delivery operation cycle (e.g., every 5 minutes), such as a blood glucose monitor. Alternatively, the sensor may have a threshold setting and may transmit an output only when the threshold is reached or exceeded, such as a pressure monitor, etc.

[0048] The corresponding sensor reading S can be evaluated n (t) and a corresponding sensor baseline reading S corresponding to the corresponding sensor among the stored plurality of sensor baseline readings n,baseline (Step 320). For example, as shown in Equation 1 below, the processor may execute a function where the processor determines a sensor reading difference between the corresponding sensor reading and the corresponding sensor baseline reading (resulting in a first difference value) and calculates the difference using the corresponding sensor baseline reading and the maximum sensor output value (S n,max)Determine the maximum sensor difference (resulting in a second difference). The maximum sensor output value (S n,max ) can be the maximum output that the sensor is expected to output, and the sensor baseline reading S n,baseline can be the average or median reading (or output value) of the sensor. The average or median reading can be determined over a time range. The time range can be hours, days, weeks, months, or years. The time range can be a rolling window, or it can be a defined or specific time range, e.g., the time range for which an AID setting (such as a Q:R ratio) is determined. The processor can determine the quotient of the first difference divided by the second difference. The quotient can be multiplied by a weighting factor W n specific to the corresponding sensor S n . For example, the heart rate output of n can have a specific weighting, and a heart rate / oxygen saturation combination monitor can have a different weighting for the heart rate monitor result value and also a different weighting for the oxygen saturation monitor result value. The weighting factor W n can be determined based on clinical trials or demographic assessments, an evaluation of the user history of the corresponding sensor, to isolate strong predictors of hypoglycemia or hyperglycemia. In some embodiments, the weighting factor is determined by regression analysis or machine learning. The product result of the weighting factor W n and the quotient can be a parameter adjustment value P Sn attributable to the corresponding sensor (S

[0049] . An example equation that can be implemented in this process is as follows:

[0050]

[0051] where the value P Sn (t) is the parameter adjustment value of the corresponding sensor (S n ) at time t, W n is the weighting of the corresponding sensor (S n ), and the numerator is the difference between the sensor reading (S n (t)) at time t and the corresponding sensor baseline reading (S n,baseline ), and the denominator is the maximum sensor difference between the maximum sensor output value (S n,max ) and the corresponding sensor baseline reading (S n,baseline ). The numerator is the first difference mentioned above, and the denominator is the second difference mentioned above. Note that the units cancel out in P Sn in Equation 1.

[0052] Thus, the output received from each general sensor can be utilized based on the criteria and upper limit, as well as the expected impact of the corresponding sensor on the user's hyperglycemia or hypoglycemia threshold.

[0053] For example, at 330, based on the assessment, the processor may adjust the insulin delivery algorithm parameters.

[0054] Then the expected impact on hyperglycemia or hypoglycemia can be performed as an adaptation parameter, and then the adaptation parameter is multiplied by each adjustment parameter in the AID system listed above, similar to the following equation:

[0055]

[0056] Where the adaptation parameter A(t) is the average of the available impact factors of n number of sensors. The adaptation parameter A(t) can be applied to each or one or more of the adjustment parameter settings mentioned above. Accordingly, in some embodiments, the adaptation parameter is determined by separately determining the parameter adjustment values for multiple sensors (especially each sensor among multiple sensors) and determining the adaptation parameter as the average of the parameter adjustment values.

[0057] In a specific example, two general sensors can be implemented into the AID system - an accelerometer and a sensor that describes the frequency of the user's GPS location near a restaurant (e.g., a computer application that evaluates the GPS location using locations the user frequently visits). Such inputs from the accelerometer, the sensor that evaluates the GPS signal, or any other general sensor can be utilized by the drug delivery algorithm by applying a weighting factor to the output of each sensor based on their association with the user state (the drug delivery is designed to control this state). This weighting method provides a robust approach for any general sensor to be integrated into the drug delivery algorithm without the need for model identification with detailed specifications. In the example, since each sensor is assigned a weighting factor, the result of A(t) is the weighted sum of each weighting factor P Sn (t).

[0058] Using the above framework, Table 1 below shows different readings of the corresponding sensors and examples of the corresponding sensor readings of the sensors. For example, the weighting W n can result in the establishment of the following for each expected impact on hyperglycemia or hypoglycemia:

[0059]

[0060] Table 1

[0061] In this example, based on clinical data or an assessment of the user's history, a corresponding correlation between the sensor readings output by the accelerometer or the restaurant co-location frequency and the user's blood glucose concentration (also referred to as glucose concentration) can be determined. For example, the accelerometer can be negatively correlated with the glucose concentration - specifically, higher readings in the accelerometer are generally expected to result in a higher likelihood of hypoglycemia. Alternatively, the restaurant co-location frequency can be positively correlated with the glucose concentration - specifically, a higher frequency of presence (i.e., co-location) in a restaurant is expected to result in a higher likelihood of hyperglycemia.

[0062] Then, P n The effects on hyperglycemia or hypoglycemia can be defined as follows:

[0063]

[0064] Then, the adjustment factor A(t) as shown in Equation 2 above can be calculated as the average of the available impact factors:

[0065]

[0066] Then, the -0.0231 value can be converted into an adjustment to various adjustment parameters (also referred to as insulin delivery algorithm parameter values) for the user within the AID system, as a bias to reduce insulin delivery in the current cycle by 2.3% (i.e., increase the glucose target by 2.3%, reduce the input clinical parameters by 2.3%, increase the Q:R ratio by 2.3%, increase the IC ratio by 2.3%, increase the correction factor by 2.3%, etc.). Accordingly, in some embodiments, adjusting the insulin delivery algorithm parameter values includes estimating (i.e., selecting) the value of the insulin delivery algorithm parameter value to be adjusted; and in response to the estimation of the insulin delivery algorithm parameter to be adjusted, generating a signal identifying the insulin delivery algorithm parameter value to be adjusted. Additionally, in some embodiments, adjusting the insulin delivery algorithm parameter values further includes evaluating (especially in response to the signal) other corresponding sensor readings received from other corresponding sensors among the plurality of sensors. Further, based on the other corresponding sensor readings, a corresponding insulin delivery parameter value can be established for each of the other corresponding sensors among the plurality of sensors. Additionally, using the insulin delivery parameter value and each corresponding insulin delivery parameter value of all the other corresponding sensors among the plurality of sensors, an adaptation parameter can be generated that can be used by the drug delivery algorithm, especially where the adaptation parameter can be used by the drug delivery algorithm to recalculate the parameter settings of the drug delivery algorithm. In some embodiments, adjusting the insulin delivery algorithm parameter values includes estimating the value of the insulin delivery algorithm parameter value to be adjusted, and in response to the estimation of the insulin delivery algorithm parameter to be adjusted, adjusting the insulin delivery algorithm parameter, especially where adjusting the insulin delivery algorithm parameter includes generating a signal identifying the insulin delivery algorithm parameter value to be adjusted.

[0067] Different sensors can have different impacts and are assigned different sensor impacts, which can be adjusted over time as the processor receives further outputs from the respective sensors. For example, as represented in Table 2, the processor can use further sensor outputs from sensors (such as the sensors listed in Table 2) to calibrate the sensor baseline readings (S n,baseline ).

[0068]

[0069]

[0070] Table 2

[0071] Accordingly, in some embodiments, the output related to the user's state generated by the (one or more) sensors includes at least one of the user's stress level, the user's movement speed, the number of times the user checks their blood glucose level, the number of glucose measurements, the number of caffeine intakes, the amount of meals, the fat content of meals, the meal frequency, the number of (weekly) restaurant visits, the number of sick days, the number of stress days, the number of days of participating in meditation and smooth exercise, the medications and supplements used by the user, and / or the sleep time.

[0072] As can be seen in Table 2 above, the outputs from each respective sensor can be different and have different units. Figure 4 Illustrated is a flowchart of a process for evaluating a corresponding sensor baseline reading corresponding to a respective sensor among the respective sensor readings and the stored multiple sensor baseline readings according to an example discussed herein. Process 400 tracks additional exemplary steps implementing Equation 1, which are reproduced below for convenience. In the example, Equation 1 can be used to characterize a parameter value (P n ) attributable to a sensor reading (S n ) at time t, as follows:[[]]

[0073]

[0074] where S n (t) is the current sensor reading from sensor S n , S n,baseline is the average or typical value of the user output by the sensor, and S n,max is the maximum change in the sensor reading, as explained above. In step 410, the numerator of Equation 1, the sensor reading difference between the corresponding sensor reading and the corresponding sensor baseline reading, is determined, while in step 420, in the denominator, the maximum setting difference using the corresponding sensor baseline reading is determined. The parameter value (P nThere is no unit because the units cancel out in Equation 1. For example, at 430, the processor can calculate the quotient of the division as a preliminary parameter value using the determined difference in sensor readings and the determined maximum setting difference. By applying a weighting to the calculated preliminary parameter value, an insulin delivery parameter value P is established for the corresponding sensor. Sn (t).

[0075] For each sensor, determine the parameter value P from Equation 1 Sn (t) and use it to adapt the parameter settings of the AID system / MDA. At step 450, the processor can generate adapted parameters for use by the drug delivery algorithm or the AID system. For example, Equation 2 (reproduced below for convenience) can be used to determine the adapted parameters that can be applied to the corresponding adjustable AID system / MDA parameter settings. The adapted parameter A(t) can be calculated according to Equation 2:

[0076]

[0077] where the adapted parameter is the average of the available influencing factors, where the sum is the sum of the corresponding values of the available influencing factors PSn(t), and n is the number of sensors (i.e., the number of influencing factors).

[0078] The calculated adapted parameter A(t) is then applied to each adjustable parameter of the AID system / MDA as mentioned above. Generally, each sensor obtains a weighting factor; then, you have the weighted sum of each weighting factor (to combine all sensors).

[0079] Certain examples of the present disclosure are described above. However, it should be explicitly stated that the present disclosure is not limited to these examples, but rather, it is intended that additions and modifications to what is explicitly described herein are also included within the scope of the disclosed examples. Moreover, it should be understood that the features of the various examples described herein are not mutually exclusive and can exist in various combinations and permutations without departing from the spirit and scope of the disclosed examples, even if such combinations or permutations are not expressed herein. In fact, those of ordinary skill in the art will envision variations, modifications, and other implementations of what is described herein without departing from the spirit and scope of the disclosed examples. Thus, the disclosed examples are not limited solely by the foregoing illustrative description.

[0080] It should be emphasized that the abstract of the present disclosure is provided to enable the reader to quickly determine the nature of the technical disclosure. When submitting the abstract, it should be understood that the abstract will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing detailed description, for the purpose of streamlining the present disclosure, various features are combined in a single example. This method of disclosure should not be construed as reflecting an intention that the claimed examples require more features than those expressly recited in each claim. Instead, as reflected in the following claims, the novel subject matter lies in less than all the features of a single disclosed example. Accordingly, the following claims are hereby incorporated into the detailed description, where each claim stands on its own as a separate example. In the appended claims, the terms "including" and "in which" are used as the plain English equivalents of the respective terms "comprising" and "wherein". Also, the terms "first", "second", "third", etc. are used merely as labels and are not intended to impose numerical requirements on their objects.

[0081] The description of the above examples is given for purposes of illustration and description. It is not intended to be exhaustive or to limit the present disclosure to the precise form disclosed. Many modifications and variations are possible in light of the present disclosure. It is intended that the scope of the present disclosure not be limited by this detailed description, but rather by the appended claims. Future filed applications claiming the priority of this application may claim the disclosed subject matter in a different manner and may generally include any combination of one or more of the features disclosed herein in various ways or otherwise presented.

[0082] Although the present invention is defined in the appended claims, it should be understood that the present invention may also (alternatively) be defined in accordance with the following embodiments:

[0083] 1. A system, comprising:

[0084] a plurality of sensors, wherein each sensor is configured to generate a corresponding output related to the state of a user;

[0085] a memory storing a plurality of sensor baseline readings of the corresponding sensors among the plurality of sensors; and

[0086] a processor operable to execute programming instructions, wherein when the processor executes the programming instructions, it is operable to:

[0087] obtain corresponding sensor readings output from the corresponding sensors among the plurality of sensors;

[0088] evaluate the corresponding sensor readings and the corresponding sensor baseline readings corresponding to the corresponding sensors among the stored plurality of sensor baseline readings; and

[0089] Based on the evaluation, adjust the insulin delivery algorithm parameters.

[0090] 2. The system according to embodiment 1, wherein when the processor evaluates the corresponding sensor baseline reading corresponding to the corresponding sensor among the corresponding sensor readings and the stored multiple sensor baseline readings, the processor is operable to:

[0091] Determine the sensor reading difference between the corresponding sensor reading and the corresponding sensor baseline reading;

[0092] Use the corresponding sensor baseline reading to determine the maximum setting difference;

[0093] Calculate a preliminary parameter value using the determined sensor reading difference and the determined maximum setting difference; and

[0094] Establish the insulin delivery parameter value for the corresponding sensor by applying a weighting to the calculated preliminary parameter value.

[0095] 3. The system according to embodiment 2, wherein when the processor evaluates the corresponding sensor reading and the corresponding sensor baseline reading, the processor is further operable to:

[0096] Estimate the value of the insulin delivery algorithm parameter value to be adjusted; and

[0097] In response to the estimation of the insulin delivery algorithm parameter to be adjusted, generate a signal identifying the insulin delivery algorithm parameter value to be adjusted.

[0098] 4. The system according to embodiment 3, wherein the processor is further operable to:

[0099] In response to the signal, evaluate other corresponding sensor readings received from other corresponding sensors among the multiple sensors;

[0100] Establish the corresponding insulin delivery parameter value for each of the other corresponding sensors among the multiple sensors; and

[0101] Use the insulin delivery parameter value and each corresponding insulin delivery parameter value of all the other corresponding sensors among the multiple sensors to generate adaptation parameters that can be used by the drug delivery algorithm.

[0102] 5. The system according to embodiment 1, wherein the processor is further operable to:

[0103] Calculate the insulin delivery parameter value for each of the multiple sensors based on the corresponding sensor reading outputs received from each of the multiple sensors;

[0104] Calculate adaptation parameters using the insulin delivery parameter values of each of the plurality of sensors; and

[0105] Recalculate the parameter settings of the drug delivery algorithm.

[0106] 6. The system according to embodiment 1, wherein the memory is further operable to store a drug delivery algorithm, and the processor is further operable to execute the drug delivery algorithm.

[0107] 7. The system according to embodiment 1, further comprising:

[0108] A wearable drug delivery device, comprising the memory, the processor, and communication circuitry, wherein the communication circuitry is operable to wirelessly communicate with one or more of the plurality of sensors.

[0109] 8. The system according to embodiment 1, further comprising:

[0110] A controller device, comprising the memory, the processor, and communication circuitry, wherein the communication circuitry is operable to wirelessly communicate with one or more of the plurality of sensors.

[0111] 9. The system according to embodiment 1, wherein the plurality of sensors configured to detect the user's state includes:

[0112] At least one of a wearable fitness monitor, a heart rate monitor, an oxygen sensor, a carbohydrate calculator, a pedometer, a meal tracking application, a blood glucose detector, or a ketone detector.

[0113] 10. The system according to embodiment 9, wherein one or more of the plurality of sensors further includes:

[0114] Communication circuitry operable to transmit sensor readings indicative of the respective detected states.

[0115] 11. The system according to embodiment 1, wherein the sensor readings of most of the plurality of sensors are not used as direct inputs to the drug delivery algorithm or the medication delivery algorithm of the AID system.

[0116] 12. A non-transitory computer-readable medium implementing programming instructions that, when executed by a processor, cause the processor to:

[0117] Obtain corresponding sensor readings output from sensors;

[0118] Evaluate the corresponding sensor readings and the corresponding sensor baseline readings corresponding to the respective sensors among a plurality of sensor baseline readings; and

[0119] Based on the evaluation, adjust the insulin delivery algorithm parameters.

[0120] 13. The non-transitory computer-readable medium according to embodiment 12, wherein the corresponding sensor readings output of the corresponding sensor among the plurality of sensors are not used as direct inputs to a drug delivery algorithm or a medication delivery algorithm of an AID system.

[0121] 14. The non-transitory computer-readable medium according to embodiment 12, wherein the processor is further caused, when evaluating the corresponding sensor readings and the corresponding sensor baseline readings corresponding to the corresponding sensor among the stored plurality of sensor baseline readings:

[0122] Determine a sensor reading difference between the corresponding sensor readings and the corresponding sensor baseline readings;

[0123] Determine a maximum setting difference using the corresponding sensor baseline readings;

[0124] Calculate a preliminary parameter value using the determined sensor reading difference and the determined maximum setting difference; and

[0125] Establish an insulin delivery parameter value for the corresponding sensor by applying a weighting to the calculated preliminary parameter value.

[0126] 15. The non-transitory computer-readable medium according to embodiment 12, wherein the processor is further caused, when evaluating the corresponding sensor readings and the corresponding sensor baseline readings corresponding to the corresponding sensor among the stored plurality of sensor baseline readings:

[0127] Evaluate the value of an insulin delivery algorithm parameter value to generate an indication to adjust the insulin delivery algorithm parameter; and

[0128] In response to an estimate to adjust the insulin delivery algorithm parameter, generate a signal representing the generated indication to adjust the insulin delivery algorithm parameter.

[0129] 16. The non-transitory computer-readable medium according to embodiment 15, wherein the processor is further caused to:

[0130] In response to the signal, evaluate other corresponding sensor readings received from other corresponding sensors among the plurality of sensors;

[0131] Establish corresponding insulin delivery parameter values for each of the other corresponding sensors among the plurality of sensors; and

[0132] Use the insulin delivery parameter value and each corresponding insulin delivery parameter value of all sensors among the other corresponding sensors among the plurality of sensors to generate adaptation parameters that can be used by a drug delivery algorithm.

[0133] 17. The non-transitory computer-readable medium as described in embodiment 16, further causing the processor to:

[0134] Calculate an insulin delivery parameter value for each of the plurality of sensors based on the corresponding sensor reading outputs received from each of the plurality of sensors;

[0135] Calculate an adaptation parameter using the insulin delivery parameter values for each of the plurality of sensors; and

[0136] Recalculate the parameter settings of the drug delivery algorithm.

[0137] 18. A wearable drug delivery device, comprising:

[0138] A memory storing a plurality of sensor baseline readings of the corresponding sensors among the plurality of sensors and a drug delivery algorithm;

[0139] A communication circuit system operable to receive wireless signals; and

[0140] A processor coupled to the memory and the communication circuit system, the processor being operable to execute programming instructions, wherein when executing the programming instructions, the processor is operable to:

[0141] Obtain sensor readings from one or more of the plurality of sensors;

[0142] Based on the received sensor readings and the corresponding baseline sensor readings, determine that the sensor readings received from one or more of the plurality of sensors indicate that the parameters input to the drug delivery algorithm are to be modified;

[0143] Calculate an adaptation parameter based on the sum of the individual insulin delivery parameters generated for each of the corresponding one or more of the plurality of sensors; and

[0144] Modify the parameters input to the drug delivery algorithm.

[0145] 19. The wearable drug delivery device as described in embodiment 15, wherein for each of the received sensor readings, the processor is operable to:

[0146] Determine the sensor reading difference between the corresponding sensor reading and the corresponding sensor baseline reading;

[0147] Utilize the corresponding sensor baseline reading to determine the maximum setting difference;

[0148] Calculate a preliminary parameter value using the determined sensor reading difference and the determined maximum setting difference; and

[0149] An insulin delivery parameter value for the corresponding sensor is established by applying a weighting to the calculated preliminary parameter value, wherein the insulin delivery parameter value for the corresponding sensor is included in the sum.

[0150] 20. The wearable drug delivery device according to embodiment 15, wherein the communication circuitry is operable to:

[0151] Receive a signal from a sensor external to the body of a user of the wearable device; and

[0152] Provide the received signal to the processor, wherein the received signal represents sensor readings not used as a direct input to a drug delivery algorithm executed by the processor.

Claims

1. A system, comprising: a plurality of sensors, each of which is configured to generate a corresponding output related to the state of a user; a memory storing a plurality of sensor baseline readings of the corresponding sensors among the plurality of sensors; and a processor operable to execute programming instructions, wherein when executing the programming instructions, the processor is operable to: obtain corresponding sensor readings output from the corresponding sensors among the plurality of sensors; evaluate the corresponding sensor readings and the corresponding sensor baseline readings stored for the corresponding sensors among the plurality of sensors; and based on the evaluation, adjust insulin delivery algorithm parameters.

2. The system according to claim 1, wherein when the processor evaluates the corresponding sensor readings and the corresponding sensor baseline readings stored for the corresponding sensors among the plurality of sensors, the processor is operable to: determine a sensor reading difference between the corresponding sensor readings and the corresponding sensor baseline readings; determine a maximum setting difference using the corresponding sensor baseline readings; calculate a preliminary parameter value using the determined sensor reading difference and the determined maximum setting difference; and establish an insulin delivery parameter value for the corresponding sensor by applying a weighting to the calculated preliminary parameter value.

3. The system according to claim 1 or 2, wherein when the processor evaluates the corresponding sensor readings and the corresponding sensor baseline readings, the processor is further operable to: estimate a value of an insulin delivery algorithm parameter value to be adjusted; and in response to the estimation of the insulin delivery algorithm parameter to be adjusted, generate a signal identifying the insulin delivery algorithm parameter value to be adjusted.

4. The system according to any one of the preceding claims, wherein the processor is further operable to: in particular in response to the signal, evaluate other corresponding sensor readings received from other corresponding sensors among the plurality of sensors; establish corresponding insulin delivery parameter values for each of the other corresponding sensors among the plurality of sensors; and use the insulin delivery parameter values and each of the corresponding insulin delivery parameter values of all the sensors among the other corresponding sensors among the plurality of sensors to generate adaptation parameters that can be used by a drug delivery algorithm, in particular, wherein the adaptation parameters can be used by the drug delivery algorithm to recalculate the parameter settings of the drug delivery algorithm.

5. The system according to any one of the preceding claims, wherein the processor is further operable to: calculate insulin delivery parameter values for each of the plurality of sensors based on the corresponding sensor readings output from each of the plurality of sensors; calculate adaptation parameters using the insulin delivery parameter values of each of the plurality of sensors; and recalculate the parameter settings of the drug delivery algorithm based on the adaptation parameters.

6. The system according to any one of the preceding claims, wherein the memory is further operable to store a drug delivery algorithm, and the processor is further operable to execute the drug delivery algorithm.

7. The system according to any one of the preceding claims, further comprising: A wearable drug delivery device, including the memory, the processor, and a communication circuitry, wherein the communication circuitry is operable to wirelessly communicate with one or more of the plurality of sensors.

8. The system according to any one of the preceding claims, further comprising: A controller device, including the memory, the processor, and a communication circuitry, wherein the communication circuitry is operable to wirelessly communicate with one or more of the plurality of sensors.

9. The system according to any one of the preceding claims, wherein the plurality of sensors configured to detect a user's state comprises: At least one of a wearable fitness monitor, a heart rate monitor, an oxygen sensor, a carbohydrate calculator, a pedometer, a meal tracking application, a blood glucose detector, or a ketone detector.

10. The system according to any one of the preceding claims, wherein one or more of the plurality of sensors further comprises: A communication circuitry, the communication circuitry being operable to transmit sensor readings indicative of the corresponding detected state.

11. The system according to any one of the preceding claims, wherein sensor readings of most of the plurality of sensors are not used as direct inputs to a drug delivery algorithm or a medication delivery algorithm of an AID system.

12. A non-transitory computer-readable medium having programming instructions which, when executed by a processor, cause the processor to: Obtain corresponding sensor readings output from a sensor; Evaluate the corresponding sensor readings and corresponding sensor baseline readings corresponding to the corresponding sensor among a plurality of sensor baseline readings; and Based on the evaluation, adjust insulin delivery algorithm parameters.

13. The non-transitory computer-readable medium according to claim 12, wherein the output of the corresponding sensor readings of the corresponding sensor among the plurality of sensors is not used as a direct input to a drug delivery algorithm or a medication delivery algorithm of an AID system.

14. The non-transitory computer-readable medium according to claim 12 or 13, wherein the processor is further caused to, when evaluating the corresponding sensor readings and the corresponding sensor baseline readings corresponding to the corresponding sensor among the stored plurality of sensor baseline readings: Determine a sensor reading difference between the corresponding sensor readings and the corresponding sensor baseline readings; Use the corresponding sensor baseline readings to determine a maximum setting difference; Calculate a preliminary parameter value using the determined sensor reading difference and the determined maximum setting difference; and Establish an insulin delivery parameter value for the corresponding sensor by applying a weighting to the calculated preliminary parameter value.

15. The non-transitory computer-readable medium according to any one of claims 12 to 14, wherein the processor is further caused to, when evaluating the corresponding sensor readings and the corresponding sensor baseline readings corresponding to the corresponding sensor among the stored plurality of sensor baseline readings: Evaluate a value of an insulin delivery algorithm parameter value, generating an indication to adjust the insulin delivery algorithm parameter; and In response to an estimate to adjust insulin delivery algorithm parameters, a signal is generated representing an indication of the generated insulin delivery algorithm parameters to be adjusted.

16. The non-transitory computer-readable medium according to any one of claims 12 to 15, wherein the processor is further caused to: In particular, in response to the signal, evaluate other corresponding sensor readings received from other corresponding sensors among the plurality of sensors; Establish corresponding insulin delivery parameter values for each of the other corresponding sensors among the plurality of sensors; and Use the insulin delivery parameter values and each corresponding insulin delivery parameter value of all sensors among the other corresponding sensors among the plurality of sensors to generate adaptation parameters that can be used by a drug delivery algorithm. In particular, the adaptation parameters can be used by the drug delivery algorithm to recalculate the parameter settings of the drug delivery algorithm.

17. The non-transitory computer-readable medium according to claims 12 to 16, wherein the processor is further caused to: Calculate insulin delivery parameter values for each of the plurality of sensors based on the corresponding sensor reading outputs received from each of the plurality of sensors; Calculate adaptation parameters using the insulin delivery parameter values of each of the plurality of sensors; and Recalculate the parameter settings of the drug delivery algorithm using the adaptation parameters.

18. A wearable drug delivery device comprising: a memory storing a plurality of sensor baseline readings of corresponding sensors among a plurality of sensors and a drug delivery algorithm; a communication circuit system operable to receive wireless signals; and a processor coupled to the memory and the communication circuit system, the processor operable to execute programming instructions, wherein the processor is operable, when executing the programming instructions, to: Obtain sensor readings from one or more sensors among the plurality of sensors; Based on the received sensor readings and the corresponding baseline sensor readings, determine that the sensor readings received from one or more sensors among the plurality of sensors indicate that the parameters input to the drug delivery algorithm are to be modified; Calculate adaptation parameters based on the sum of individual insulin delivery parameters generated for each of the corresponding one or more sensors among the plurality of sensors; and Modify the parameters input to the drug delivery algorithm.

19. The wearable drug delivery device according to claim 18, wherein for each sensor reading among the received sensor readings, the processor is operable to: Determine the sensor reading difference between the corresponding sensor reading and the corresponding sensor baseline reading; Utilize the corresponding sensor baseline reading to determine the maximum setting difference; Calculate a preliminary parameter value using the determined sensor reading difference and the determined maximum setting difference; and and Establish the insulin delivery parameter value of the corresponding sensor by applying a weighting to the calculated preliminary parameter value, wherein the insulin delivery parameter value of the corresponding sensor is included in the sum.

20. The wearable drug delivery device according to claim 18 or 19, wherein the communication circuit system is operable to: Receiving a signal from a sensor outside the body of a user of the wearable device; and Providing the received signal to the processor, where the received signal represents a sensor reading that is not used as a direct input to a drug delivery algorithm executed by the processor.

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