ADJUSTING OPERATION OF A MEDICATION DELIVERY SYSTEM IN RESPONSE TO GESTURE-INDICATED ACTIVITY CHANGES

Abstract

A system disclosed here includes an insulin infusion device, a gesture-based physical behavior detection system that generates gesture data for a user, and a controller that controls operation of the insulin infusion device. The controller performs adaptive training of at least one feature, function, setting, or model associated with the insulin infusion device, based at least in part on the sensor data. The controller processes activity-identifying data that indicates a current behavior pattern of the user, and that includes gesture data provided by the gesture-based physical behavior detection system. The controller determines, from the activity-identifying data, that the current behavior pattern differs from a currently implemented therapy behavior pattern of the user, and, in response to the determination, alters the adaptive training of the at least one feature, function, setting, or model.

Description

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. provisional patent application No. 62/947,988, filed Dec. 13, 2019.

TECHNICAL FIELD

The present technology is generally related to the control, operation, and adjustment of a medication delivery system in response to changes in patient lifestyle, activity, or eating habits, as detected by a gesture-based physical behavior detection system.

BACKGROUND

Medical therapy delivery systems, such as fluid infusion devices, are relatively well known in the medical arts for use in delivering or dispensing an agent, such as insulin or another prescribed medication, to a patient. A typical medication infusion device includes a fluid pump mechanism and an associated drive system that actuates a plunger or piston of a fluid reservoir to deliver fluid medication from the reservoir to the body of a patient via a fluid delivery conduit between the reservoir and the body of a patient. Use of infusion pump therapy has been increasing, especially for delivering insulin to diabetic patients.

Control schemes have been developed to allow insulin infusion devices to monitor and regulate a patient's blood glucose level in a substantially continuous and autonomous manner. An insulin infusion device can be operated in an automatic mode wherein basal insulin is delivered at a rate that is automatically adjusted for the user. Moreover, an insulin infusion device can be operated to automatically calculate, recommend, and deliver insulin boluses as needed (e.g., to compensate for meals consumed by the user). Ideally, the amount of an insulin bolus should be accurately calculated and administered to maintain the user's blood glucose within the desired range. In particular, an automatically generated and delivered insulin bolus should safely manage the user's blood glucose level and keep it above a defined threshold level. To this end, an insulin infusion device operating in an automatic mode uses continuous glucose sensor data and control algorithms to regulate the user's blood glucose, based on a target glucose setpoint setting and user-initiated meal announcements that typically include estimations of the amount of carbohydrates to be consumed in an upcoming meal.

BRIEF SUMMARY

The subject matter of this disclosure generally relates to a system and related operating methodologies for the control, operation, and adjustment of a medication delivery system, such as an insulin infusion device. Certain settings, parameters, or operating modes of the medication delivery system can be adjusted or modified in response to changes in patient lifestyle, activity, or eating habits, as detected by a gesture-based physical behavior detection system.

In one aspect, the present disclosure provides a method of operating a medication delivery system having a fluid pump mechanism, an analyte sensor to provide sensor data indicative of a physiological characteristic of a user, and at least one controller that regulates operation of the fluid pump mechanism to deliver medication from the medication delivery system to the user, based at least in part on the sensor data. Exemplary embodiments of the method involve: performing adaptive training of at least one feature, function, setting, or model associated with the medication delivery system, based at least in part on the sensor data provided by the analyte sensor; processing activity-identifying data that indicates a current behavior pattern of the user, the activity-identifying data including gesture data for the user, the gesture data provided by a gesture-based physical behavior detection system; determining, from the activity-identifying data, that the current behavior pattern differs from a currently implemented therapy behavior pattern of the user; and in response to the determining, altering the adaptive training of the at least one feature, function, setting, or model, resulting in an altered adaptive training scheme.

In another aspect, the disclosure provides a non-transitory computer readable medium having stored thereon program code instructions that are configurable to cause at least one processor to perform a method that involves: performing adaptive training of at least one feature, function, setting, or model associated with a medication delivery system, based at least in part on sensor data provided by an analyte sensor that measures a physiological characteristic of a user; processing activity-identifying data that indicates a current behavior pattern of the user, the activity-identifying data including gesture data for the user, the gesture data provided by a gesture-based physical behavior detection system; determining, from the activity-identifying data, that the current behavior pattern differs from a currently implemented therapy behavior pattern of the user; and in response to the determining, altering the adaptive training of the at least one feature, function, setting, or model, resulting in an altered adaptive training scheme.

In yet another aspect, the disclosure provides a system having: an insulin infusion device that regulates delivery of insulin to a user; a gesture-based physical behavior detection system configured to generate gesture data for the user, and configured to communicate the gesture data; and at least one controller that controls operation of the insulin infusion device. The at least one controller is configured to: perform adaptive training of at least one feature, function, setting, or model associated with the insulin infusion device, based at least in part on the sensor data; process activity-identifying data that indicates a current behavior pattern of the user, the activity-identifying data including gesture data for the user, the gesture data provided by a gesture-based physical behavior detection system; determine, from the activity-identifying data, that the current behavior pattern differs from a currently implemented therapy behavior pattern of the user; and in response to the determining, altering the adaptive training of the at least one feature, function, setting, or model, resulting in an altered adaptive training scheme.

The details of one or more aspects of the disclosure are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the techniques described in this disclosure will be apparent from the description and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified block diagram representation of an exemplary embodiment of a system that includes a medication delivery system that responds to changes in patient activity as indicated by the output of a gesture-based physical behavior detection system;

FIG. 2 is a plan view of an exemplary embodiment of an insulin infusion device that is suitable for use as the medication delivery system shown in FIG. 1;

FIG. 3 is a top perspective view of an embodiment of an insulin infusion device implemented as a patch pump device that is suitable for use as the medication delivery system shown in FIG. 1;

FIG. 4 is a perspective view of an exemplary embodiment of a smart insulin pen that is suitable for use as the medication delivery system shown in FIG. 1;

FIG. 5 is a perspective view of an exemplary embodiment of a smart pen accessory that is suitable for use with the medication delivery system shown in FIG. 1;

FIG. 6 is a block diagram representation of an exemplary embodiment of a computer-based or processor-based device suitable for deployment in the system shown in FIG. 1;

FIG. 7 is a block diagram representation of a closed loop glucose control system arranged in accordance with certain embodiments;

FIG. 8 is a block diagram representation of a gesture-based physical behavior detection system arranged in accordance with certain embodiments;

FIG. 9 is a flow chart that illustrates an infusion device control process according to certain embodiments; and

FIG. 10 is a flow chart that illustrates a gesture training process according to certain embodiments.

DETAILED DESCRIPTION

The following detailed description is merely illustrative in nature and is not intended to limit the embodiments of the subject matter or the application and uses of such embodiments. As used herein, the word “exemplary” means “serving as an example, instance, or illustration.” Any implementation described herein as exemplary is not necessarily to be construed as preferred or advantageous over other implementations. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary or the following detailed description.

It should be understood that various aspects disclosed herein may be combined in different arrangements than the combinations specifically presented in the description and accompanying drawings. It should also be understood that, depending on the example, certain acts or events of any of the processes or methods described herein may be performed in a different sequence, may be added, merged, or left out altogether (e.g., all described acts or events may not be necessary to carry out the techniques). In addition, while certain aspects of this disclosure are described as being performed by a single module or unit for purposes of clarity, it should be understood that the techniques of this disclosure may be performed by a combination of units or modules associated with, for example, a medical device.

In one or more examples, the described techniques may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media may include non-transitory computer-readable media, which corresponds to a tangible medium such as data storage media (e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer).

Program code instructions may be configurable to be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, controllers, application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processor” as used herein may refer to any of the foregoing structure or any other physical structure suitable for implementation of the described techniques. Also, the techniques could be fully implemented in one or more circuits or logic elements.

Techniques and technologies may be described herein in terms of functional and/or logical block components, and with reference to symbolic representations of operations, processing tasks, and functions that may be performed by various computing components or devices. Such operations, tasks, and functions are sometimes referred to as being computer-executed, computerized, software-implemented, or computer-implemented. It should be appreciated that the various block components shown in the figures may be realized by any number of hardware, software, and/or firmware components configured to perform the specified functions. For example, an embodiment of a system or a component may employ various integrated circuit components, e.g., memory elements, digital signal processing elements, logic elements, look-up tables, or the like, which may carry out a variety of functions under the control of one or more microprocessors or other control devices.

FIG. 1 is a simplified block diagram representation of an exemplary embodiment of a system 100 that responds to changes in the user's activity (e.g., eating, sleeping, exercise, and/or working habits) by regulating operation of a medication delivery system 102 in an appropriate manner. In certain embodiments, the medication delivery system 102 responds to changes in patient activity as indicated by the output of a gesture-based physical behavior detection system 104 and/or the output of at least one ancillary sensor, detector, or measurement system 106 (hereinafter referred to as ancillary system(s) 106). Certain embodiments of the system 100 include, without limitation: the medication delivery system 102 (or device) that regulates delivery of medication to a user; at least one gesture-based physical behavior detection system 104 that monitors user behavior and/or status to obtain gesture data that indicates user activity events or behavior; at least one ancillary system 106; at least one user device 108 that includes or cooperates with a suitably written and configured patient care application 110; an analyte sensor 112 to measure a physiological characteristic of the user, such that sensor data obtained from the analyte sensor 112 can be used to control, regulate, or otherwise influence the operation of the medication delivery system 102; and at least one patient history and outcomes database 114. In accordance with certain cloud-implemented embodiments, the system includes at least one data processing system 116, which may be in communication with any or all of the other components of the system 100. Other configurations and topologies for the system 100 are also contemplated here, such as a system that includes additional intermediary, interface, or data repeating devices in the data path between a sending device and a receiving device.

At least some of the components of the system 100 are communicatively coupled with one another to support data communication, signaling, and/or transmission of control commands as needed, via at least one communications network 120. The at least one communications network 120 may support wireless data communication and/or data communication using tangible data communication links. FIG. 1 depicts network communication links in a simplified manner. In practice, the system 100 may cooperate with and leverage any number of wireless and any number of wired data communication networks maintained or operated by various entities and providers. Accordingly, communication between the various components of the system 100 may involve multiple network links and different data communication protocols. In this regard, the network can include or cooperate with any of the following, without limitation: a local area network; a wide area network; the Internet; a personal area network; a near-field data communication link; a cellular communication network; a satellite communication network; a video services or television broadcasting network; a network onboard a vehicle; or the like. The components of the system 100 may be suitably configured to support a variety of wireless and wired data communication protocols, technologies, and techniques as needed for compatibility with the at least one communication network 120.

The system 100 can support any type of medication delivery system 102 that is compatible with the features and functionality described here. For example, the medication delivery system 102 may be realized as a user-activated or user-actuated fluid delivery device, such as a manual syringe, an injection pen, a smart insulin pen, or the like. As another example, the medication delivery system 102 may be implemented as an electronic device that is operated to regulate the delivery of medication fluid to the user. In certain embodiments, however, the medication delivery system 102 includes or is realized as an insulin infusion device, e.g., a portable patient-worn or patient-carried insulin pump, a smart insulin pen, or the like. In such embodiments, the analyte sensor 112 includes or is realized as a glucose meter, a glucose sensor, or a continuous glucose monitor. For the sake of brevity, conventional techniques related to insulin infusion device operation, infusion set operation, and other functional aspects of the systems (and the individual operating components of the systems) may not be described in detail here. Examples of infusion pumps may be of the type described in, but not limited to, U.S. Pat. Nos.: 4,562,751; 4,685,903; 5,080,653; 5,505,709; 5,097,122; 6,485,465; 6,554,798; 6,558,320; 6,558,351; 6,641,533; 6,659,980; 6,752,787; 6,817,990; 6,932,584; and 7,621,893; each of which are herein incorporated by reference.

FIG. 2 is a plan view of an exemplary embodiment of an insulin infusion device 130 suitable for use as the medication delivery system 102 shown in FIG. 1. The insulin infusion device 130 is a portable medical device designed to be carried or worn by the patient. The illustrated embodiment of the insulin infusion device 130 includes a housing 132 adapted to receive an insulin-containing reservoir (hidden from view in FIG. 2). An opening in the housing 132 accommodates a fitting 134 (or cap) for the reservoir, with the fitting 134 being configured to mate or otherwise interface with tubing 136 of an infusion set 138 that provides a fluid path to/from the body of the user. In this manner, fluid communication from the interior of the insulin reservoir to the user is established via the tubing 136. The illustrated version of the insulin infusion device 130 includes a human-machine interface (HMI) 140 (or user interface) that includes elements that can be manipulated by the user to administer a bolus of fluid (e.g., insulin), to change therapy settings, to change user preferences, to select display features, and the like. The insulin infusion device 130 also includes a display 142, such as a liquid crystal display (LCD) or another suitable display technology, that can be used to present various types of information or data to the user, such as, without limitation: the current glucose level of the patient; the time; a graph or chart of the patient's glucose level versus time; device status indicators; etc. The insulin infusion device 130 may be configured and controlled to support other features and interactive functions described in more detail below.

Generally, a fluid infusion device (such as the insulin infusion device 130) includes a fluid pump mechanism having a motor or other actuation arrangement that is operable to linearly displace a plunger (or stopper) of a fluid reservoir provided within the fluid infusion device to deliver a dosage of fluid medication, such as insulin, to the body of a user. Dosage commands that govern operation of the motor may be generated in an automated manner in accordance with the delivery control scheme associated with a particular operating mode, and the dosage commands may be generated in a manner that is influenced by a current (or most recent) measurement of a physiological condition in the body of the user. For a glucose control system suitable for use by diabetic patients, a closed-loop or automatic operating mode can be used to generate insulin dosage commands based on a difference between a current (or most recent) measurement of the interstitial fluid glucose level in the body of the user and a target (or reference) glucose setpoint value. In this regard, the rate of infusion may vary as the difference between a current measurement value and the target measurement value fluctuates. For purposes of explanation, the subject matter is described herein in the context of the infused fluid being insulin for regulating a glucose level of a user (or patient); however, it should be appreciated that many other fluids may be administered through infusion, and the subject matter described herein is not necessarily limited to use with insulin.

FIG. 3 is a top perspective view of an embodiment of an insulin infusion device 146 implemented as a patch pump device that is suitable for use as the medication delivery system 102 shown in FIG. 1. The insulin infusion device 146 can be implemented as a combination device that includes an insertable insulin delivery cannula and an insertable glucose sensor (both of which are hidden from view in FIG. 3). In such an implementation, the glucose sensor may take the place of the separate analyte sensor 112 shown in FIG. 1. The insulin infusion device 146 includes a housing 148 that serves as a shell for a variety of internal components. FIG. 3 shows the insulin infusion device 146 with a removable fluid cartridge 150 installed and secured therein. The housing 148 is suitably configured to receive, secure, and release the removable fluid cartridge 150. The insulin infusion device 146 includes at least one user interface feature, which can be actuated by the patient as needed. The illustrated embodiment of the insulin infusion device 146 includes a button 152 that is physically actuated. The button 152 can be a multipurpose user interface if so desired to make it easier for the user to operate the insulin infusion device 146. In this regard, the button 152 can be used in connection with one or more of the following functions, without limitation: waking up the processor and/or electronics of the insulin infusion device 146; triggering an insertion mechanism to insert a fluid delivery cannula and/or an analyte sensor into the subcutaneous space or similar region of the user; configuring one or more settings of the insulin infusion device 146; initiating delivery of medication fluid from the fluid cartridge 150; initiating a fluid priming operation; disabling alerts or alarms generated by the insulin infusion device 146; and the like. In lieu of the button 152, the insulin infusion device 146 can employ a slider mechanism, a pin, a lever, a switch, a touch-sensitive element, or the like. In certain embodiments, the insulin infusion device 146 may be configured and controlled to support other features and interactive functions described in more detail below.

FIG. 4 is a perspective view of an exemplary embodiment of a smart insulin pen 160 suitable for use as the medication delivery system shown in FIG. 1. The pen 160 includes an injector body 162 and a cap 164. FIG. 4 shows the cap 164 removed from the injector body 162, such that a delivery needle 166 is exposed. The pen 160 includes suitably configured electronics and processing capability to communicate with an application running on a user device, such as a smartphone, to support various functions and features such as: tracking active insulin; calculating insulin dosages (boluses); tracking insulin dosages; monitoring insulin supply levels; patient reminders and notifications; and patient status reporting. In certain embodiments, the smart insulin pen 160 can receive insulin dosage recommendations or instructions and/or recommended dosing times (or a recommended dosing schedule). Moreover, the smart insulin pen 160 may be configured and controlled to support other features and interactive functions described in more detail below.

FIG. 5 is a perspective view of an exemplary embodiment of a smart pen accessory 170 that is suitable for use with the medication delivery system 102 shown in FIG. 1. In particular, the smart pen accessory 170 cooperates with a “non-smart” insulin pen that lacks the intelligence and functionality of a smart insulin pen (as described above). The smart pen accessory 170 can be realized as a pen cap, a clip-on apparatus, a sleeve, or the like. The smart pen accessory 170 is attached to an insulin pen 172 such that the smart pen accessory 170 can measure the amount of insulin delivered by the insulin pen 172. The insulin dosage data is stored by the smart pen accessory 170 along with corresponding date/time stamp information. In certain embodiments, the smart pen accessory 170 can receive, store, and process additional patient-related or therapy-related data, such as glucose data. Indeed, the smart pen accessory 170 may also support various features and functions described above in the context of the smart insulin pen 160. For example, the smart pen accessory 170 may be configured to receive insulin dosage recommendations or instructions and/or recommended dosing times (or a recommended dosing schedule). Moreover, the smart pen accessory 170 may be configured and controlled to support other features and interactive functions described in more detail below.

The analyte sensor 112 may communicate sensor data to the medication delivery system 102 for use in regulating or controlling operation of the medication delivery system 102. Alternatively or additionally, the analyte sensor 112 may communicate sensor data to one or more other components in the system 100, such as, without limitation: a user device 108 (for use with the patient care application 110); a data processing system 116; and/or a patient history and outcomes database 114.

The system 100 can support any number of user devices 108 linked to the particular user or patient. In this regard, a user device 108 may be, without limitation: a smartphone device; a laptop, desktop, or tablet computer device; a medical device; a wearable device; a global positioning system (GPS) receiver device; a system, component, or feature onboard a vehicle; a smartwatch device; a television system; a household appliance; a video game device; a media player device; or the like. For the example described here, the medication delivery system 102 and the at least one user device 108 are owned by, operated by, or otherwise linked to a user/patient. Any given user device 108 can host, run, or otherwise execute the patient care application 110. In certain embodiments, for example, the user device 108 is implemented as a smartphone with the patient care application 110 installed thereon. In accordance with another example, the patient care application 110 is implemented in the form of a website or webpage, e.g., a website of a healthcare provider, a website of the manufacturer, supplier, or retailer of the medication delivery system 102, or a website of the manufacturer, supplier, or retailer of the analyte sensor 112. In accordance with another example, the medication delivery system 102 executes the patient care application 110 as a native function.

In certain embodiments, at least some of the features or output of the gesture-based physical behavior detection system 104 and/or the ancillary system(s) 106 can be used to influence features, functions, and/or therapy-related operations of the medication delivery system 102. In particular, the systems 104, 106 may be suitably configured and operated to generate and provide output (e.g., data, control signals, markers, or flags) that indicates whether the user's behavior or activity is out of the ordinary, unusual, or has significantly changed relative to a currently implemented or active therapy behavior pattern of the user, such that the medication delivery system 102 can dynamically respond in an appropriate manner that contemplates a change in user activity. Changes in user activity patterns, behavior, routine, or lifestyle may include, for example: working longer hours than usual; working on a different schedule than usual; eating, drinking, walking, or exercising more than usual combined with an unusual geographic location (which might indicate that the user is on vacation or is traveling for business); a new or altered exercise regimen; a change in diet or eating schedule; the addition of a daily walking or running routine as a result of a new dog; weekly participation in a sport due to the start of a recreational league; or the like.

As described in more detail below, the gesture-based physical behavior detection system 104 includes one or more sensors, detectors, measurement devices, and/or readers to automatically detect certain user gestures that correlate to user behavior, eating habits, work habits, or the like (e.g., work-related physical activity, commuting, eating at common meal times, eating particular portion sizes, sleeping, exercising, or watching television). The gesture-based physical behavior detection system 104 may communicate gesture data to the medication delivery system 102, the user device 108, and/or the data processing system 116 for processing in an appropriate manner for use in regulating or controlling certain functions of the medication delivery system 102. For example, the gesture data may be communicated to a user device 108, such that the user device 108 can process the gesture data and inform the user or the medication delivery system 102 as needed (e.g., remotely regulate or control certain functions of the medication delivery system 102). As another example, the gesture-based physical behavior detection system 104 may communicate the gesture data to one or more cloud computing systems or servers (such as a remote data processing system 116) for appropriate processing and handling in the manner described herein.

Similarly, an ancillary system 106 may include one or more sensors, detectors, measurement devices, and/or readers that obtain ancillary user status data that correlates to user activity, detectable behavior, eating habits, etc. In certain embodiments, an ancillary system 106 may include, cooperate with, or be realized as any of the following, without limitation: a heartrate monitor linked to the user; a blood pressure monitor linked to the user; a respiratory rate monitor linked to the user; a vital signs monitor linked to the user; a microphone; a thermometer (for the user's body temperature and/or the environmental temperature); a sweat detector linked to the user; an activity tracker linked to the user; a global positioning system (GPS); a clock, calendar, or appointment application linked to the user; a pedometer linked to the user; or the like. An ancillary system 106 may be configured and operated to communicate its output (user status data) to one or more components of the system 100 for analysis, processing, and handling in the manner described herein. In certain embodiments, user status data obtained from one or more ancillary systems 106 supplements the gesture data obtained from the gesture-based physical behavior detection system 104, such that user habits, physical behavior, and activity events are accurately and reliably detected. For example, the user's location (obtained from GPS location data) can be useful to identify a change in the user's lifestyle or behavior patterns, wherein the change results from a move to a new city, a vacation, a business trip, or the like.

In certain embodiments, the gesture-based physical behavior detection system 104 and the medication delivery system 102 are implemented as physically distinct and separate components, as depicted in FIG. 1. In such embodiments, the gesture-based physical behavior detection system 104 is external to the medication delivery system 102 and is realized as an ancillary component, relative to the medication delivery system 102. In accordance with alternative embodiments, however, the medication delivery system 102 and the gesture-based physical behavior detection system 104 can be combined into a single hardware component or provided as a set of attached hardware devices. For example, the medication delivery system 102 may include the gesture-based physical behavior detection system 104 or integrate the functionality of the system 104. Similarly, the analyte sensor 112 can be incorporated with the medication delivery system 102 or the gesture-based physical behavior detection system 104. These and other arrangements, deployments, and topologies of the system 100 are contemplated by this disclosure.

The at least one patient history and outcomes database 114 includes historical data related to the user's physical condition, physiological response to the medication regulated by the medication delivery system 102, activity patterns or related information, eating patterns and habits, work habits, and the like. In accordance with embodiments where the medication delivery system 102 is an insulin infusion device and the analyte sensor 112 is a glucose meter, sensor, or monitor, the database 114 can maintain any of the following, without limitation: historical glucose data and corresponding date/time stamp information; insulin delivery and dosage information; user-entered stress markers or indicators; gesture data (provided by the gesture-based physical behavior detection system 104) and corresponding date/time stamp information; ancillary user status data (provided by one or more ancillary systems 106) and corresponding date/time stamp data; diet or food intake history for the user; location information; and/or any other information that may be generated by or used by the system 100 for purposes of controlling the operation of the medication delivery system 102. In certain embodiments, the at least one patient history and outcomes database 114 can receive and maintain training data that is utilized to train, configure, and initialize the system 100 based on historical user behavior, physiological state, operation of the medication delivery system 102, and user-identified activity events.

A patient history and outcomes database 114 may reside at a user device 108, at the medication delivery system 102, at a data processing system 116, or at any network-accessible location (e.g., a cloud-based database or server system). In certain embodiments, a patient history and outcomes database 114 may be included with the patient care application 110. The patient history and outcomes database 114 enables the system 100 to generate recommendations, warnings, and guidance for the user and/or to regulate the manner in which the medication delivery system 102 functions to administer therapy to the user, based on detected changes in the user's activity (which may be temporary, ongoing for an extended period of time, or somewhat permanent in nature).

In accordance with certain embodiments, any or all of the components shown in FIG. 1 can be implemented as a computer-based or a processor-based device, system, or component having suitably configured hardware and software written to perform the functions and methods needed to support the features described herein. In this regard, FIG. 6 is a simplified block diagram representation of an exemplary embodiment of a computer-based or processor-based device 200 that is suitable for deployment in the system 100 shown in FIG. 1.

The illustrated embodiment of the device 200 is intended to be a high-level and generic representation of one suitable platform. In this regard, any computer-based or processor-based component of the system 100 can utilize the architecture of the device 200. The illustrated embodiment of the device 200 generally includes, without limitation: at least one controller (or processor) 202; a suitable amount of memory 204 that is associated with the at least one controller 202; device-specific items 206 (including, without limitation: hardware, software, firmware, user interface (UI), alerting, and notification features); a power supply 208 such as a disposable or rechargeable battery; a communication interface 210; at least one application programming interface (API) 212; and a display element 214. Of course, an implementation of the device 200 may include additional elements, components, modules, and functionality configured to support various features that are unrelated to the primary subject matter described here. For example, the device 200 may include certain features and elements to support conventional functions that might be related to the particular implementation and deployment of the device 200. In practice, the elements of the device 200 may be coupled together via at least one bus or any suitable interconnection architecture 216.

The at least one controller 202 may be implemented or performed with a general purpose processor, a content addressable memory, a microcontroller unit, a digital signal processor, an application specific integrated circuit, a field programmable gate array, any suitable programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination designed to perform the functions described here. Moreover, the at least one controller 202 may be implemented as a combination of computing devices, e.g., a combination of a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other such configuration.

The memory 204 may be realized as at least one memory element, device, module, or unit, such as: RAM memory, flash memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. In this regard, the memory 204 can be coupled to the at least one controller 202 such that the at least one controller 202 can read information from, and write information to, the memory 204. In the alternative, the memory 204 may be integral to the at least one controller 202. As an example, the at least one controller 202 and the memory 204 may reside in an ASIC. At least a portion of the memory 204 can be realized as a computer storage medium that is operatively associated with the at least one controller 202, e.g., a tangible, non-transitory computer-readable medium having computer-executable instructions stored thereon. The computer-executable instructions are configurable to be executed by the at least one controller 202 to cause the at least one controller 202 to perform certain tasks, operations, functions, and processes that are specific to the particular embodiment. In this regard, the memory 204 may represent one suitable implementation of such computer-readable media. Alternatively or additionally, the device 200 could receive and cooperate with computer-readable media (not separately shown) that is realized as a portable or mobile component or platform, e.g., a portable hard drive, a USB flash drive, an optical disc, or the like.

The device-specific items 206 may vary from one embodiment of the device 200 to another. For example, the device-specific items 206 will support: sensor device operations when the device 200 is realized as a sensor device; smartphone features and functionality when the device 200 is realized as a smartphone; activity tracker features and functionality when the device 200 is realized as an activity tracker; smart watch features and functionality when the device 200 is realized as a smart watch; medical device features and functionality when the device is realized as a medical device; etc. In practice, certain portions or aspects of the device-specific items 206 may be implemented in one or more of the other blocks depicted in FIG. 6.

If present, the UI of the device 200 may include or cooperate with various features to allow a user to interact with the device 200. Accordingly, the UI may include various human-to-machine interfaces, e.g., a keypad, keys, a keyboard, buttons, switches, knobs, a touchpad, a joystick, a pointing device, a virtual writing tablet, a touch screen, a microphone, or any device, component, or function that enables the user to select options, input information, or otherwise control the operation of the device 200. The UI may include one or more graphical user interface (GUI) control elements that enable a user to manipulate or otherwise interact with an application via the display element 214. The display element 214 and/or the device-specific items 206 may be utilized to generate, present, render, output, and/or annunciate alerts, alarms, messages, or notifications that are associated with operation of the medication delivery system 102, associated with a status or condition of the user, associated with operation, status, or condition of the system 100, etc.

The communication interface 210 facilitates data communication between the device 200 and other components as needed during the operation of the device 200. In the context of this description, the communication interface 210 can be employed to transmit or stream device-related control data, patient-related user status (e.g., gesture data or status data), device-related status or operational data, sensor data, calibration data, and the like. It should be appreciated that the particular configuration and functionality of the communication interface 210 can vary depending on the hardware platform and specific implementation of the device 200. In practice, an embodiment of the device 200 may support wireless data communication and/or wired data communication, using various data communication protocols. For example, the communication interface 210 could support one or more wireless data communication protocols, techniques, or methodologies, including, without limitation: RF; IrDA (infrared); Bluetooth; BLE; ZigBee (and other variants of the IEEE 802.15 protocol); IEEE 802.11 (any variation); IEEE 802.16 (WiMAX or any other variation); Direct Sequence Spread Spectrum; Frequency Hopping Spread Spectrum; cellular/wireless/cordless telecommunication protocols; wireless home network communication protocols; paging network protocols; magnetic induction; satellite data communication protocols; wireless hospital or health care facility network protocols such as those operating in the WMTS bands; GPRS; and proprietary wireless data communication protocols such as variants of Wireless USB. Moreover, the communication interface 210 could support one or more wired/cabled data communication protocols, including, without limitation: Ethernet; powerline; home network communication protocols; USB; IEEE 1394 (Firewire); hospital network communication protocols; and proprietary data communication protocols.

The at least one API 212 supports communication and interactions between software applications and logical components that are associated with operation of the device 200. For example, one or more APIs 212 may be configured to facilitate compatible communication and cooperation with the patient care application 110, and to facilitate receipt and processing of data from sources external to the device 200 (e.g., databases or remote devices and systems).

The display element 214 is suitably configured to enable the device 200 to render and display various screens, recommendation messages, alerts, alarms, notifications, GUIs, GUI control elements, drop down menus, auto-fill fields, text entry fields, message fields, or the like. Of course, the display element 214 may also be utilized for the display of other information during the operation of the device 200, as is well understood. Notably, the specific configuration, operating characteristics, size, resolution, and functionality of the display element 214 can vary depending upon the implementation of the device 200.

As mentioned above, the medication delivery system 102 is suitably configured and programmed to support an automatic mode to automatically control delivery of insulin to the user. In this regard, FIG. 7 is a simplified block diagram representation of a closed loop glucose control system 300 arranged in accordance with certain embodiments. The system 300 depicted in FIG. 7 functions to regulate the rate of fluid infusion into a body of a user based on feedback from an analyte concentration measurement taken from the body. In particular embodiments, the system 300 is implemented as an automated control system for regulating the rate of insulin infusion into the body of a user based on a glucose concentration measurement taken from the body. The system 300 is designed to model the physiological response of the user to control an insulin infusion device 302 in an appropriate manner to release insulin 304 into the body 306 of the user in a similar concentration profile as would be created by fully functioning human β-cells when responding to changes in blood glucose concentrations in the body. Thus, the system 300 simulates the body's natural insulin response to blood glucose levels and not only makes efficient use of insulin, but also accounts for other bodily functions as well since insulin has both metabolic and mitogenic effects.

Certain embodiments of the system 300 include, without limitation: the insulin infusion device 302; a glucose sensor system 308 (e.g., the analyte sensor 112 shown in FIG. 1); and at least one controller 310, which may be incorporated in the insulin infusion device 302 as shown in FIG. 7. The glucose sensor system 308 generates a sensor signal 314 representative of blood glucose levels 316 in the body 306, and provides the sensor signal 314 to the at least one controller 310. The at least one controller 310 receives the sensor signal 314 and generates commands 320 that regulate the timing and dosage of insulin 304 delivered by the insulin infusion device 302. The commands 320 are generated in response to various factors, variables, settings, and control algorithms utilized by the insulin infusion device 302. For example, the commands 320 (and, therefore, the delivery of insulin 304) can be influenced by a target glucose setpoint value 322 that is maintained and regulated by the insulin infusion device 302. Moreover, the commands 320 (and, therefore, the delivery of insulin 304) can be influenced by any number of adaptive parameters and factors 324. The adaptive parameters and factors 324 may be associated with or used by: a therapy control algorithm of the insulin infusion device 302; a digital twin model of the patient, which can be used to recommend manual insulin dosages; a meal prediction algorithm; a user glucose prediction algorithm; or the like. In this context, the adaptive parameters and factors 324 may include, without limitation: a time constant; a threshold value; a glucose limit; an insulin delivery limit; and/or a gain value.

Generally, the glucose sensor system 308 includes a continuous glucose sensor, sensor electrical components to provide power to the sensor and generate the sensor signal 314, a sensor communication system to carry the sensor signal 314 to the at least one controller 310, and a sensor system housing for the electrical components and the sensor communication system. As mentioned above with reference to FIG. 6, the glucose sensor system 308 may be implemented as a computer-based or processor-based component having the described configuration and features.

Typically, the at least one controller 310 includes controller electrical components and software to generate commands for the insulin infusion device 302 based on the sensor signal 314, the target glucose setpoint value 322, the adaptive parameters and factors 324, and other user-specific parameters, settings, and factors. The at least one controller 310 may include a controller communication system to receive the sensor signal 314 and issue the commands 320.

Generally, the insulin infusion device 302 includes a fluid pump mechanism 328, a fluid reservoir 330 for the medication (e.g., insulin), and an infusion tube to infuse the insulin 304 into the body 306. In certain embodiments, the insulin infusion device 302 includes an infusion communication system to handle the commands 320 from the at least one controller 310, electrical components and programmed logic to activate the fluid pump mechanism 328 motor according to the commands 320, and a housing to hold the components of the insulin infusion device 302. Accordingly, the fluid pump mechanism 328 receives the commands 320 and delivers the insulin 304 from the fluid reservoir 330 to the body 306 in accordance with the commands 320. It should be appreciated that an embodiment of the insulin infusion device 302 can include additional elements, components, and features that may provide conventional functionality that need not be described herein. Moreover, an embodiment of the insulin infusion device 302 can include alternative elements, components, and features if so desired, as long as the intended and described functionality remains in place. In this regard, as mentioned above with reference to FIG. 6, the insulin infusion device 302 may be implemented as a computer-based or processor-based components having the described configuration and features, including the display element 214 or other device-specific items 206 as described above.

The at least one controller 310 is configured and programmed to regulate the operation of the fluid pump mechanism 328 and other functions of the insulin infusion device 302. The at least one controller 310 controls the fluid pump mechanism 328 to deliver the fluid medication (e.g., insulin) from the fluid reservoir 330 to the body 306. As mentioned above, the at least one controller 310 can be housed in the infusion device housing, wherein the infusion communication system is an electrical trace or a wire that carries the commands 320 from the at least one controller 310 to the fluid pump mechanism 328. In alternative embodiments, the at least one controller 310 can be housed in the sensor system housing, wherein the sensor communication system is an electrical trace or a wire that carries the sensor signal 314 from the sensor electrical components to the at least one controller 310. In accordance with some embodiments, the at least one controller 310 has its own housing or is included in a supplemental or ancillary device. In other embodiments, the at least one controller 310, the insulin infusion device 302, and the glucose sensor system 308 are all located within one common housing.

Referring again to FIG. 1, the gesture-based physical behavior detection system 104 employs at least one sensor to obtain corresponding user-specific sensor data. The obtained user-specific sensor data is processed or analyzed by the gesture-based physical behavior detection system 104 and/or by another suitably configured device or component of the system 100 to determine whether the user's current behavior reflects a significant or measurable change in activity, relative to a currently implemented, active, or monitored therapy behavior pattern of the user. The obtained user-specific sensor data may also be processed or analyzed to obtain certain activity-related parameters, characteristics, and/or metadata for the user. For example, the obtained user-specific sensor data may identify, include, or indicate any or all of the following, without limitation: timestamp data corresponding to the occurrence of detected events; a type, category, or classification of the detected physical behavior or activity; location data; user posture or position information; etc. In some examples, the type, category, or classification of detected physical behavior or activity can correspond to activity duration and/or intensity.

The gesture-based physical behavior detection system 104 may include, cooperate with, or be realized as a motion-based physical behavior detection system, an activity-based physical behavior detection system, an image or video based activity detection system, or the like. In certain embodiments, the system 104 may be realized as a unitary “self-contained” wearable system that communicates with one or more other components of the system 100. For example, the system 104 can be implemented with at least one wearable device such as an activity monitor device, a smart watch device, a smart bracelet or wristband device, or the like. In some embodiments, the system 104 may be realized as at least one portable or wearable device that includes or communicates with one or more external or ancillary sensor devices, units, or components. For example, the system 104 can be implemented with a wearable or portable smart device that is linked with one or more external sensors worn or carried by the user. These and other possible deployments of the system 104 are contemplated by this disclosure. In this regard, United States patent publication number US 2020/0135320 and United States patent publication number US 2020/0289373 disclose gesture-based physical behavior detection systems that are suitable for use as the system 104; the entire content of these United States patent documents is incorporated by reference herein.

FIG. 8 is a block diagram representation of a gesture-based physical behavior detection system 400 arranged in accordance with certain embodiments. The system 400 is suitable for use with the system 100 shown FIG. 1. In certain embodiments, the system 400 is deployed as a wearable electronic device in the form factor of a bracelet or wristband that is worn around the wrist or arm of a user's dominant hand. The system 400 may optionally be implemented using a modular design, wherein individual modules include one or more subsets of the disclosed components and overall functionality. The user may choose to add specific modules based on personal preferences and requirements.

The system 400 includes a battery 402 and a power management unit (PMU) 404 to deliver power at the proper supply voltage levels to all electronic circuits and components. The PMU 404 may also include battery-recharging circuitry. The PMU 404 may also include hardware, such as switches, that allows power to specific electronics circuits and components to be cut off when not in use.

When there is no movement-based or gesture-based behavior event in progress, most circuitry and components in the system 400 are switched off to conserve power. Only circuitry and components that are required to detect or help predict the start of a behavior event of interest may remain enabled. For example, if no motion is being detected, all sensor circuits but an accelerometer 406 may be switched off and the accelerometer 406 may be put in a low-power wake-on-motion mode or in another lower power mode that consumes less power and uses less processing resources than its high performance active mode. A controller 408 of the system 400 may also be placed into a low-power mode to conserve power. When motion or a certain motion pattern is detected, the accelerometer 406 and/or the controller 408 may switch into a higher power mode and additional sensors such as, for example, a gyroscope 410 and/or a proximity sensor 412 may also be enabled. When a potential start of a movement-based or gesture-based event is detected, memory variables for storing event-specific parameters, such as gesture types, gesture duration, etc. can be initialized.

In another example, upon detection of user motion, the accelerometer 406 switches into a higher power mode, but other sensors remain switched off until the data from the accelerometer 406 indicates that the start of a behavior event has likely occurred. At that point in time, additional sensors such as the gyroscope 410 and the proximity sensor 412 may be enabled.

In another example, when there is no behavior event in progress, both the accelerometer 406 and gyroscope 410 are enabled but at least one of either the accelerometer 406 or the gyroscope 410 is placed in a lower power mode compared to their regular power mode. For example, the sampling rate may be reduced to conserve power. Similarly, the circuitry required to transfer data from the system 400 to a destination device may be placed in a lower power mode. For example, radio circuitry 414 could be disabled. Similarly, the circuitry required to transfer data from the system 400 may be placed in a lower power mode. For example, the radio circuitry 414 could be disabled until a possible or likely start of a behavior event has been determined. Alternatively, it may remain enabled but in a low power state to maintain the connection between the system 400 and one or more other components of the system 100, but without transferring user status data, sensor data, or the like.

In yet another example, all motion-detection related circuitry may be switched off if, based on certain metadata, it is determined that the occurrence of a particular behavior event, such as a food intake event, is unlikely. This may be desirable to further conserve power. Metadata used to make this determination may, among other things, include one or more of the following: time of the day, location, ambient light levels, proximity sensing, and detection that the system 400 has been removed from the wrist or hand, detection that the system 400 is being charged, or the like. Metadata may be generated and collected by the system 400. Alternatively, metadata may be collected by another device that is external to the system 400 and is configured to directly or indirectly exchange information with the system 400. It is also possible that some metadata is generated and collected by the system 400, while other metadata is generated and collected by a device that is external to the system 400. In case some or all of the metadata is generated and collected external to the system 400, the system 400 may periodically or from time to time power up its radio circuitry 414 to retrieve metadata related information from another device.

In certain embodiments, some or all of the sensors may be turned on or placed in a higher power mode if certain metadata indicates that the occurrence of a particular behavior event, such as the user beginning to work, jog, or eat, is likely. Metadata used to make this determination may, among other things, include one or more of the following: time of the day; location; ambient light levels; proximity sensing; historical user behavior patterns. Some or all of the metadata may be collected by the system 400 or by an ancillary device that cooperates or communicates with the system 400, as mentioned above.

User status data used to track certain aspects of a user's behavior may be stored locally inside memory 416 of the system 400 and processed locally using the controller 408 of the system 400. User status data may also be transferred to the medication delivery system 102, the patient care application 110, and/or one or more of the database 114 mentioned above with reference to FIG. 1 (such that the user status data can be processed, analyzed, or otherwise utilized by the applications or components that receive the user status data). It is also possible that some of the processing and analysis are performed locally by the system 400, while further processing and analysis are performed by one or more other components of the system 100.

The detection of the start of a behavior event, such as the start of a work activity, may trigger the power up and/or activation of additional sensors and circuitry, such as a camera 418. Power up and/or activation of additional sensors and circuitry may occur at the same time as the detection of the behavior event of interest or some time thereafter. Specific sensors and circuitry may be turned on only at specific times during a detected event, and may be switched off otherwise to conserve power. It is also possible that the camera 418 only gets powered up or activated upon explicit user intervention such as, for example, pushing and holding a button 420. Releasing the button 420 may turn off the camera 418 to conserve power.

When the camera 418 is powered up, a projecting light source 422 may also be enabled to provide visual feedback to the user about the area that is within view of the camera or to otherwise illuminate the field of view. Alternatively, the projecting light source 422 may only be activated sometime after the camera 418 has been activated. In certain cases, additional conditions may need to be met before the projecting light source 422 is activated. Such conditions may include: the determination that the projecting light source 422 is likely aiming in the direction of the object of interest; the determination that the system 400 is not moving excessively; or the like. In some embodiments, one or more light emitting diodes (LEDs) 426 may be used as the projecting light source 422.

Images may be tagged with additional information or metadata such as: camera focal information; proximity information from the proximity sensor 412; ambient light levels information from an ambient light sensor 424; timestamp information; etc. Such additional information or metadata may be used during the processing and analysis of the user status data.

The projecting light source 422 may also be used to communicate other information. As an example, an ancillary device may use inputs from one or more proximity sensors 412, process those inputs to determine if the camera 418 is within the proper distance range from the object of interest, and use one or more light sources to communicate that the camera is within the proper distance range, that the user needs to increase the distance between camera and the object of interest, or that the user needs to reduce the distance between the camera and the object of interest.

The projecting light source 422 may also be used in combination with the ambient light sensor 424 to communicate to the user if the ambient light is insufficient or too strong for an adequate quality image capture. The projecting light source 422 may also be used to communicate information including, but not limited to, a low battery situation or a functional defect.

The projecting light source 422 may also be used to communicate dietary coaching information. As an example, the projecting light source 422 might, among other things, indicate if not enough or too much time has expired since a previous food intake event, or may communicate to the user how he/she is doing against specific dietary goals.

Signaling mechanisms to convey specific messages using one or more projecting light sources 422 may include, but are not limited to, one or more of the following: specific light intensities or light intensity patterns; specific light colors or light color patterns; specific spatial or temporal light patterns. Multiple mechanisms may also be combined to signal one specific message.

A microphone 428 may be used by the user to add specific or custom labels or messages to a detected event and/or image. In certain embodiments, audio captured by the microphone 428 can be processed to assist in the determination of whether the user is eating, drinking, commuting, exercising, working, or resting. Audio snippets may be processed by a voice recognition engine.

In certain embodiments, the accelerometer 406 (possibly combined with other sensors, including other inertial sensors) may, in addition to tracking at least one parameter that is directly related to a gesture-based behavior event, also be used to track one or more parameters that are not directly related to that particular event. Such parameters may, among other things, include physical activity, sleep, stress, or illness.

In addition to the particular sensors, detectors, and components mentioned above, the system 400 may include or cooperate with any number of other sensors 430 as appropriate for the particular embodiment. For example, and without limitation, the system 400 may include or cooperate with any or all of the following: a heartrate monitor; a physiological characteristic or analyte sensor; a continuous glucose monitor; a GPS receiver; and any other sensor, monitor, or detector mentioned elsewhere herein. The system 400 obtains user status data from one or more of its sensors, detectors, and sources, wherein the user status data indicates a stressful activity of the user. The user status data can be analyzed and processed by the system 400 (and/or by one or more other components of the system 100) to determine whether the user's current behavior is consistent with normally expected behavior or activity. In certain embodiments, the system 400 and/or an ancillary system 106 or device determines the user's activity and related behavior primarily based on the output of user-worn motion sensors, movement sensors, one or more inertial sensors (e.g., one or more accelerometers and/or one or more gyroscopes), one or more GPS sensors, one or more magnetometers, one or more force or physical pressure sensors, or the like, which are suitably configured, positioned, and arranged to measure physical movement or motion of the user's limbs, digits, joints, facial features, head, and/or other body parts.

In some embodiments, the system 400 includes at least one haptic interface 440 that is suitably configured and operated to provide haptic feedback as an output. The at least one haptic interface 440 generates output(s) that can be experienced by the sense of touch by the user, e.g., mechanical force, vibration, movement, temperature changes, or the like. Haptic feedback generated by the at least one haptic interface 440 may represent or be associated with one or more of the following, without limitation: reminders; alerts; confirmations; notifications; messages; numerical values (such as measurements); status indicators; or any other type of output provided by the system 400.

In certain embodiments, the user status data (e.g., sensor data) is provided to a gesture recognizer unit or processor. To this end, sensor data may be sent in raw format. Alternatively, a source of sensor data may perform some processing (e.g., filtering, compression, or formatting) on raw sensor data before sending the processed sensor data to the gesture recognizer unit. The gesture recognizer unit analyzes the incoming sensor data and converts the incoming sensor data into a stream of corresponding gestures, which may be predetermined or otherwise classified or categorized. The gesture recognizer unit may use one or more ancillary inputs (such as the output from one or more ancillary systems 106) to aid in the gesture determination process. Nonlimiting examples of an ancillary input include: time of day; the probability of a specific gesture occurring based on statistical analysis of historical gesture data for that user; geographical location; heart rate; other physiological sensor inputs. Other ancillary inputs are also possible.

The output of the gesture recognizer unit—the detected gestures—can be sent to an event detector or processor. The event detector analyzes the incoming stream of gestures to determine if the start of an event of interest (e.g., eating a meal, going to bed, working out) has occurred, whether an event is ongoing, whether an event has ended, or the like. Although this description mentions meal detection, the gesture-based physical behavior detection system 400 may be suitably configured to monitor other types of physical behavior or activities. Such activities include, without limitation: reading; sleeping; smoking; getting dressed; driving; walking; commuting; working; exercising; turning down a bed; making a bed; brushing teeth; combing hair; talking on the phone; inhaling or injecting a medication; and activities related to hand hygiene or personal hygiene.

Referring again to FIG. 1, the output of the gesture-based physical behavior detection system 104 (in some embodiments, supplemented with the output of the ancillary system(s) 106) can be used to detect significant or relevant changes in the patient's activity or usual routine. For example, gesture detection can be leveraged to identify any or all of the following events, without limitation: a change in eating habits or eating patterns (e.g., eating larger or smaller portions, eating different types of foods); eating, working, sleeping, or exercising at unusual times; eating, working, sleeping, or exercising for periods of time that are longer or shorter than usual; consuming unusual types of food or drink; eating at restaurants that are out of the ordinary. Detected changes in the user's activity can inform certain functions, features, and/or therapy-related operations of the system 100. For example, detected changes in patient activity can influence one or more parameters of a closed loop medication delivery algorithm employed by the medication delivery system 102. Thus, a “nominal” or “default” therapy control algorithm may be active or implemented when the user's activity tracks a typical or routine pattern of behavior, and a different, altered, modified, or updated therapy control algorithm may be temporarily activated or utilized when the currently detected user activity changes. Any number of different types, classifications, categories, or levels of therapy control algorithms can be supported by the system described herein, as needed to contemplate detectable (and distinguishable) user activity patterns. In some examples, different therapy controls algorithms may be associated with different locations and/or time frame. For example, the system can support: one or more therapy control algorithms for city A and one or more other therapy control algorithms for city B; one or more therapy control algorithms for weekends and one or more other therapy control algorithms for weekdays; one or more therapy control algorithms for each season; and/or one or more therapy control algorithms for each month.

As another example, detected changes in activity can trigger modified adjustment of a digital twin model of the patient that simulates the patient's physiological response to medication. In this regard, an adaptive training or learning scheme can be used to dynamically train the digital twin model in an ongoing manner. However, in response to detected changes in the user's activity, a different, modified, or altered adaptive training scheme can be used to dynamically train the digital twin model in a manner that is appropriate for the currently detected behavior pattern. Any number of different types, classifications, categories, or levels of adaptive training schemes can be supported by the system described herein, as needed to contemplate detectable (and distinguishable) user activity patterns.

As another example, detected changes in activity can trigger adjustment of a currently active meal prediction algorithm, a currently active glucose prediction algorithm (when the analyte sensor 112 is realized as a glucose sensor), and/or a currently active dosage calculation algorithm. Similarly, detected changes in activity can trigger the selection, activation, or enablement of a different meal prediction algorithm, a different glucose prediction algorithm (when the analyte sensor 112 is realized as a glucose sensor), and/or a different dosage calculation algorithm. Any number of different types, classifications, categories, or levels of these therapy-related algorithms can be supported by the system described herein, as needed to contemplate detectable (and distinguishable) user activity patterns.

As yet another example, detected changes in user activity may reset, pause, alter, or restart an adaptive training scheme or an adaptive learning period during which at least one feature, function, setting, or model associated with the medication delivery system is adaptively trained or adjusted. In accordance with certain embodiments, operation of the medication delivery device 102 can be controlled or regulated based on a determination that the user's normal or routine activity has changed by some measurable amount. For example, if the system 100 determines (from an analysis of user gesture data) that the user's regular work, sleep, and/or eating schedule has changed, then certain adaptive, training, or learning functions of the medication delivery device 102 can be temporarily halted or frozen under the assumption that something unusual has occurred in the user's normally predictable daily routine. Accordingly, information and data that would normally be collected and used for adaptive training/learning need not be considered during periods of detected unusual or different user behavior. In accordance with certain embodiments, the system 100 need not take such temporary action until multiple occurrences of a triggering event have been recorded. For example, the system 100 may declare that a change in the user's behavior has occurred when an unusual or new activity has been detected multiple times over a designated period of time, such as a week, if an unusual or new activity has been detected at least X days in a row, or the like. Accordingly, a single instance of an unusual event or a one-time change in user behavior need not result in any changes to the adaptive, training, or learning functions of the medication delivery device 102.

FIG. 9 is a flow chart that illustrates an infusion device control process 500 according to certain embodiments. This example assumes that the medication delivery system 102 includes an insulin infusion device, and that the analyte sensor 112 is a glucose sensor, such as a continuous glucose monitor that communicates with the insulin infusion device. Accordingly, the process 500 receives or processes glucose sensor data (task 502) and controls the operation of the insulin infusion device to regulate the delivery of insulin to the user (task 504). Certain therapy-related functions or features of the insulin infusion device are influenced or based at least in part on the glucose sensor data, such that the insulin infusion device can respond to ongoing changes in the user's glucose level.

This description assumes that the process 500 begins with the insulin infusion device operating in a normal or default mode that contemplates the user behaving in a predictable manner that is consistent with a historical activity, eating pattern, work schedule, and the like. Accordingly, the process 500 performs adaptive training of at least one feature, function, setting, parameter, factor, or model that is associated with the insulin infusion device, e.g., basal amount, bolus schedule, a digital twin simulation of the patient, a glucose prediction algorithm, a delivery control algorithm for insulin (or parameters thereof such as a time constant, a gain value, a threshold, or a limit), an insulin dosage or delivery limit, or the like (task 506). In certain implementations, the adaptive training is based at least in part on the glucose sensor data provided by the glucose sensor. The adaptive training enables the insulin infusion device and/or associated models, algorithms, or control schemes to dynamically adapt and adjust to changes in the user's glucose level, as measured by the glucose sensor. The adaptive training may also be influenced by gesture data and/or ancillary data that is associated with the user, as described above.

As mentioned previously, the insulin infusion device can be controlled to operate in a closed loop mode or a hybrid closed loop mode (where basal insulin is automatically controlled, and insulin boluses are manually controlled) to automatically deliver insulin to the user in accordance with a therapy control algorithm (e.g., a closed loop insulin delivery algorithm). In this context, the adaptive training can be used to train at least one therapy-altering factor of the therapy control algorithm, such as, without limitation: a time constant; a threshold, a glucose limit, an insulin delivery limit, or the like.

The system 100 may include, utilize, or cooperate with a physiological model of the user (a digital twin) that simulates a physiological response of the user to delivery of medication, such as insulin. The physiological model can be used with or without an insulin infusion device. For example, the physiological model can be used in connection with the patient care application 110 to provide therapy guidance or recommendations to a user of a manual insulin delivery system or device (syringes, an injection pen, or the like). In certain implementations, the adaptive training can be used to train the physiological model, such that the model accurately simulates the actual response of the user. Various techniques, such as machine learning or artificial intelligence, can be leveraged to adaptively train the model.

The insulin infusion device may include, utilize, or cooperate with other adaptive algorithms, e.g., a meal prediction algorithm, a glucose prediction algorithm, an insulin dosage calculation algorithm, or a bolus calculation algorithm. In certain embodiments, the adaptive training can be used to train these and possibly other adaptive algorithms, settings, or control schemes.

The process 500 receives activity-identifying data that indicates a current behavior pattern of the user (e.g., physical activity information including time, type, duration, and/or intensity; meal information including time, duration, portion size, and/or carbohydrate amount) (task 508) and analyzes or processes at least some of the received activity-identifying data to determine whether the currently detected behavior pattern differs from a currently implemented therapy or expected behavior pattern of the user (e.g., typical physical activity or meal activity for the user) (query task 510). As mentioned above, the activity-identifying data is generated by sensors, detector units, or other sources of data that are included with or associated with a suitably configured gesture-based physical behavior detection system 104, 400 (e.g., the accelerometer 406, the gyroscope 410, the proximity sensor 412, one or more other sensors 430, the microphone 428, and/or the camera 418). Accordingly, the activity-identifying data may be generated at least in part from gesture data obtained for the user, e.g., location. Depending on the particular embodiment, at least some of the activity-identifying data may include user status data generated or provided by at least one ancillary system 106 or device (other than the gesture-based physical behavior detection system 104, 400) that monitors certain characteristics, status or condition of the user. Accordingly, the activity-identifying data may be generated at least in part from such user status data.

The process 500 continues by analyzing or processing at least some of the received activity-identifying data to determine whether there has been a change in the user's activity (e.g., a threshold number of events outside the expected behavior pattern of the user are detected during a particular timeframe) (query task 510). If analysis of the activity-identifying data does not reveal a significant or relevant change in the user's activity (e.g., a threshold number of events outside the expected behavior pattern of the user are not detected during a particular timeframe) (the “No” branch of query task 510), then the process 500 continues to operate the insulin infusion device and perform adaptive training, as described above. Accordingly, FIG. 9 depicts the “No” branch of query task 510 leading back to task 502. If, however, the activity-identifying data indicates that the current behavior pattern differs from the currently implemented therapy behavior pattern of the user (the “Yes” branch of query task 510), then the process 500 takes appropriate action to temporarily halt, modify, or change the currently implemented adaptive training routine to compensate for the change in activity.

In accordance with some embodiments, if an activity change is indicated by the gesture data, then the process 500 generates a confirmation message or notification for the user (task 512). A message may document or explain the detected situation, for example, “Great job! We noticed that you have been walking more lately. Would you like us to update your therapy settings accordingly?” As another example, a confirmation message may simply ask the user to approve certain actions: “A change in your usual daily routine has been detected. OK to update your insulin delivery scheme?” As yet another example, a notification may read as follows: “New behavior pattern detected. Please update dynamic system learning and training.” The confirmation message requests user authorization to alter the adaptive training. In this regard, the confirmation message may include an active user interface element, such as a button or a link, that allows the user to confirm or initiate the altering of the adaptive training. This description assumes that the process 500 receives user authorization to alter the adaptive training and proceeds to alter (e.g., temporarily stop, modify, reset, change, or reclassify) the adaptive training (task 514). Thus, the adaptive training is altered in response to a detected activity change and further in response to receiving user authorization to alter the adaptive training. In alternative embodiments, altering the adaptive training occurs automatically in response to a detected activity change, without any user input or involvement. In certain scenarios, the adaptive training proceeds in accordance with the altered, changed, or modified adaptive training scheme.

Although not always required, operation of the insulin infusion device may be adjusted or changed in an activity-correlated manner to compensate for the detected change in activity, the manner or extent of the activity change, the time period associated with the changed activity, etc. In certain embodiments, the process 500 changes at least one therapy-altering factor of the currently active therapy control algorithm to obtain an appropriate updated therapy control algorithm that compensates for the detected activity change (task 516). For this scenario, the process 500 continues by operating the insulin infusion device to automatically deliver the insulin medication to the user in accordance with the updated therapy control algorithm (task 518).

In some implementations, a remote data processing system (e.g., a cloud-based system such as the data processing system 116 shown in FIG. 1) receives and processes the activity-identifying data to determine whether the user's behavior pattern is different than the currently implemented therapy behavior pattern and, if so, sends at least one command, instruction, or control signal to one or more destination devices, such as the insulin infusion device. The at least one command, instruction, or control signal causes the destination device to alter, pause, modify, or change the adaptive training. In certain embodiments, a user device 108 receives and processes the activity-identifying data, and communicates with a destination device to influence or impact the adaptive training. In yet other embodiments, the medication delivery system 102 (e.g., the insulin infusion device) receives and processes the activity-identifying data in the manner described herein, and initiates altering, changing, or pausing of the adaptive training.

In certain scenarios, the system reverts to a previous adaptive training scheme, a previous therapy control algorithm, and/or a previous therapy-related algorithm (e.g., meal prediction, glucose prediction, dosage calculation) when certain criteria is satisfied. If appropriate to revert to a previous state (the “Yes” branch of query task 520), then the process 500 may return to task 502 and continue as described above, with a previous state activated, selected, or implemented. For example, the immediately preceding adaptive training scheme may be reinstated, or any previously utilized adaptive training scheme can be utilized. As another example, if the current adaptive training scheme was paused, then it can be restarted to resume training as usual. If reverting to a previous state is inappropriate, untimely, or unnecessary (the “No” branch of query task 520), then the process 500 exits so that the system may continue using the current adaptive training scheme, therapy control algorithm, and other therapy-related algorithms (if applicable).

Reverting back to a previous state may be performed automatically after a predetermined period of time. In this regard, if the altered adaptive training scheme has been active for the designated period of time (e.g., 12 hours, one day, or eight hours), then the process 500 can automatically revert to a previous state. As another example, reverting back to a previous state may require user involvement. Thus, in accordance with some embodiments, the process 500 reverts back to a previous adaptive training scheme only in response to receiving a user-initiated command, instruction, or confirmation. As yet another example, reverting back to a previous state may be initiated when updated or current activity-identifying data indicates that an updated (current) behavior pattern of the user corresponds to a previously implemented therapy behavior pattern. In other words, if ongoing activity-identifying data suggests that the user's behavior is again following a historical and identifiable pattern, then the process 500 may revert back to a previous adaptive training scheme that is appropriate for the currently detected behavior pattern.

As mentioned above, the process 500 (at query task 510) determines whether the user's activity or behavior has changed, based on the received activity-identifying data. The activity-identifying data may include, for example, any of the following: raw (uncharacterized or unprocessed) or processed sensor data generated by the gesture-based physical behavior detection system 104, 400; gesture data provided by the system 104, 400; raw (uncharacterized or unprocessed) or processed sensor data generated by one or more ancillary systems 106; and raw (uncharacterized or unprocessed) or processed sensor data generated by the analyte sensor 112. In certain embodiments, the device or system that analyzes user activity has already been trained with historical data such that it can compare the received activity-identifying data against historical data, trends, patterns, and/or conditions that are known to be correlated with user activities or behavior.

FIG. 10 is a flow chart that illustrates a gesture training process 600 according to certain embodiments. As mentioned above, the system 100 can be initialized or trained with historical data for purposes of determining physical behavior events, based on obtained gesture data and/or ancillary user status data. Accordingly, the process 600 can be employed with certain embodiments to train the activity detection feature. It should be appreciated that other methodologies, including those that need not employ “training” per se, can be utilized in an implementation of the system 100.

The process 600 obtains gesture training data, which is provided by the gesture-based physical behavior detection system 104, 400 during one or more training sessions or periods of time (task 602). Alternatively or additionally, the process 600 obtains ancillary user status training data, which is provided by one or more ancillary systems 106 during one or more training sessions or periods of time (task 604). The process 600 may also obtain activity or behavior marker data, which may be entered by the user, during the training sessions or periods of time (task 606). The marker data can be obtained in response to the user interacting with one or more user devices 108 to record, flag, mark, or otherwise identify points in time or periods of time at which the user is engaging in a particular activity or physical behavior. The activity marker data may also include information that characterizes or describes the type of activity or behavior and/or other metadata related to the recorded activities. For example, the user can indicate points in time or periods of time corresponding to activities such as: working; sleeping; eating; watching television or a movie; driving or commuting; exercising; travelling overseas; vacationing; walking the dog; playing video games; playing music; etc. The process 600 may continue by temporally correlating the obtained training data (e.g., gesture training data and/or ancillary user status training data) with the obtained marker data (task 608). The temporal correlation can be utilized to identify and record certain activity-indicating or lifestyle-indicating gestures performed by the user and/or to identify and record user status information obtained during the marked period of user activity (task 610), along with corresponding time/date stamp data.

The training process 600 may continue by classifying, categorizing, or labeling certain physical behavior events (as indicated by the collected training data) that correspond to the user's activities, lifestyle, eating habits, daily routine, and/or behavior habits (task 612). In this regard, the system 100 can be trained in a way that links detectable user activity to the operation and control of the medication delivery system 102. Accordingly, the process 600 may generate and save one or more activity-correlated therapy control algorithms, settings, device configurations, adaptive training schemes, or the like.

The various tasks performed in connection with a process disclosed herein may be performed by software, hardware, firmware, or any combination thereof. It should be appreciated that an embodiment of an illustrated process may include any number of additional or alternative tasks, the tasks shown in the figures need not be performed in the illustrated order, and a disclosed process may be incorporated into a more comprehensive procedure or process having additional functionality not described in detail herein. Moreover, one or more of the tasks shown in a figure could be omitted from an embodiment of the depicted process as long as the intended overall functionality remains intact.

While at least one exemplary embodiment has been presented in the foregoing detailed description, it should be appreciated that a vast number of variations exist. It should also be appreciated that the exemplary embodiment or embodiments described herein are not intended to limit the scope, applicability, or configuration of the claimed subject matter in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient road map for implementing the described embodiment or embodiments. It should be understood that various changes can be made in the function and arrangement of elements without departing from the scope defined by the claims, which includes known equivalents and foreseeable equivalents at the time of filing this patent application.

Claims

1. A method of operating a medication delivery system comprising a fluid pump mechanism, an analyte sensor to provide sensor data indicative of a physiological characteristic of a user, and at least one controller that regulates operation of the fluid pump mechanism to deliver medication from the medication delivery system to the user, based at least in part on the sensor data, the method comprising:

performing adaptive training of at least one feature, function, setting, or model associated with the medication delivery system, based at least in part on the sensor data provided by the analyte sensor;

processing activity-identifying data that indicates a current behavior pattern of the user, the activity-identifying data comprising gesture data for the user, the gesture data provided by a gesture-based physical behavior detection system;

determining, from the activity-identifying data, that the current behavior pattern differs from a currently implemented therapy behavior pattern of the user; and

in response to the determining, altering the adaptive training of the at least one feature, function, setting, or model, resulting in an altered adaptive training scheme.

2. The method of claim 1, further comprising:

in response to the determining, generating a confirmation message for the user, the confirmation message requesting authorization to alter the adaptive training, wherein altering the adaptive training occurs in response to receiving an authorization to alter the adaptive training.

3. The method of claim 1, further comprising:

adaptively training the at least one feature, function, setting, or model in accordance with the altered adaptive training scheme for a predetermined period of time;

automatically reverting to a previous adaptive training scheme after the predetermined period of time; and

adaptively training the at least one feature, function, setting, or model in accordance with the previous adaptive training scheme.

4. The method of claim 1, further comprising:

adaptively training the at least one feature, function, setting, or model in accordance with the altered adaptive training scheme; and

reverting to a previous adaptive training scheme in response to receiving a user-initiated command.

5. The method of claim 1, further comprising:

processing updated activity-identifying data that indicates an updated behavior pattern of the user;

detecting, from the updated activity-identifying data, that the updated behavior pattern corresponds to a previously implemented therapy behavior pattern of the user; and

reverting to a previous adaptive training scheme in response to the detecting.

6. The method of claim 1, wherein:

the medication delivery system is controlled to automatically deliver the medication to the user in accordance with a therapy control algorithm; and

the adaptive training trains at least one therapy-altering factor of the therapy control algorithm.

7. The method of claim 6, further comprising:

changing one or more therapy-altering factors of the therapy control algorithm, in response to determining that the current behavior pattern differs from the currently implemented therapy behavior pattern of the user.

8. The method of claim 1, wherein the adaptive training trains a physiological model of the user that simulates physiological response of the user to delivery of the medication.

9. The method of claim 1, wherein altering the adaptive training occurs automatically without user input.

10. The method of claim 1, wherein:

the processing and determining steps are performed by a data processing system that communicates with the medication delivery system; and

the data processing system sends at least one command to the medication delivery system, the at least one command causing the medication delivery system to alter the adaptive training.

11. The method of claim 1, wherein the processed activity-identifying data comprises user status data for the user, the user status data generated by at least one ancillary system that monitors the user.

12. At least one non-transitory computer readable medium having stored thereon program code instructions that are configurable to cause at least one processor to perform a method comprising:

performing adaptive training of at least one feature, function, setting, or model associated with a medication delivery system, based at least in part on sensor data provided by an analyte sensor that measures a physiological characteristic of a user;

processing activity-identifying data that indicates a current behavior pattern of the user, the activity-identifying data comprising gesture data for the user, the gesture data provided by a gesture-based physical behavior detection system;

determining, from the activity-identifying data, that the current behavior pattern differs from a currently implemented therapy behavior pattern of the user; and

in response to the determining, altering the adaptive training of the at least one feature, function, setting, or model, resulting in an altered adaptive training scheme.

13. The at least one non-transitory computer readable medium of claim 12, wherein:

the medication delivery system operates to automatically deliver the medication to the user in accordance with a therapy control algorithm; and

the adaptive training trains at least one therapy-altering factor of the therapy control algorithm.

14. The at least one non-transitory computer readable medium of claim 13, wherein the method further comprises:

changing one or more therapy-altering factors of the therapy control algorithm, in response to determining that the current behavior pattern differs from the currently implemented therapy behavior pattern of the user.

15. The at least one non-transitory computer readable medium of claim 12, wherein the adaptive training trains a physiological model of the user that simulates physiological response of the user to delivery of the medication.

16. The at least one non-transitory computer readable medium of claim 12, wherein the processed activity-identifying data comprises user status data for the user, the user status data generated by at least one ancillary system that monitors the user.

17. A system comprising:

an insulin infusion device that regulates delivery of insulin to a user;

a gesture-based physical behavior detection system configured to generate gesture data for the user, and configured to communicate the gesture data; and

at least one controller that controls operation of the insulin infusion device, the at least one controller configured to: perform adaptive training of at least one feature, function, setting, or model associated with the insulin infusion device, based at least in part on the sensor data; process activity-identifying data that indicates a current behavior pattern of the user, the activity-identifying data comprising gesture data for the user, the gesture data provided by the gesture-based physical behavior detection system; determine, from the activity-identifying data, that the current behavior pattern differs from a currently implemented therapy behavior pattern of the user; and in response to the determining, altering the adaptive training of the at least one feature, function, setting, or model, resulting in an altered adaptive training scheme.

18. The system of claim 17, wherein the insulin infusion device comprises the at least one controller.

19. The system of claim 17, wherein the activity-identifying data comprises user status data for the user, the user status data generated by at least one ancillary system that monitors characteristics, status, or condition of the user.

20. The system of claim 17, wherein:

the insulin infusion device operates to automatically deliver insulin to the user in accordance with a therapy control algorithm; and

the adaptive training trains at least one therapy-altering factor of the therapy control algorithm.

21. The system of claim 17, wherein the adaptive training trains a physiological model of the user that simulates physiological response of the user to delivery of insulin.