Abstract: Disclosed herein are techniques related to glucose level management without carbohydrate counting. The techniques may involve obtaining contextual information for food intake, predicting impact of carbohydrates on a patient's glucose included in the food intake during a duration of time based at least in part on the contextual information for the food intake, determining one or more amounts of insulin to counteract the predicted impact on the patient's glucose, and affecting insulin therapy based on outputting information indicative of the determined one or more amounts of insulin.

Abstract: One or more processors may be configured to detect whether a patient with diabetes is operating a vehicle based on one or more detected gestures. Based on the detection of operating the vehicle, the one or more processors may cause a patient device to output alerts according to a driving alert protocol. In another example, based on the detection of operating the vehicle, one or more processors may cause an insulin pump to operate according to a driving therapy protocol.

Abstract: Disclosed herein are techniques related to glucose level management without carbohydrate counting. The techniques may involve obtaining contextual information for a meal, predicting amounts of glucose to be absorbed into a bloodstream of the patient over a duration of time due to consumption of the meal based on the contextual information for the meal, determining one or more amounts of insulin to counteract effects of the predicted amounts of glucose, and affecting insulin therapy based on outputting information indicative of the determined one or more amounts of insulin.

Abstract: Disclosed herein are techniques related to safeguards against separation from portable medicine delivery devices. In some embodiments, the techniques may involve monitoring a wireless connection established between a portable computing device and a portable medicine delivery device. The techniques may also involve making a determination, based on the result of the monitoring, that the portable medicine delivery device is outside a predetermined range of the portable computing device. The techniques may further involve generating a notification based on the determination. The notification may be indicative of a user of the portable computing device being unaccompanied by the portable medicine delivery device.

Abstract: Methods, systems and non-transient computer-readable media are provided for optimizing sensor wear and/or longevity of a personalized model used for estimating glucose values.

Abstract: Techniques disclosed herein relate to gesture-based control of diabetes therapy. In some embodiments, the techniques may involve identifying at least one gesture indicative of utilization of an injection device for preparation of an insulin injection based on user activity data obtained from a wearable device disposed on an arm of a user. The techniques may further involve based on the at least one identified gesture, determining whether the insulin injection meets criteria of a proper insulin injection. The techniques may further involve outputting information indicative of whether the criteria is satisfied based on the determination.

Abstract: Methods, systems and non-transient computer-readable media are provided for updating models used for estimating glucose values. For example, technologies are provided for updating an existing population model for estimating glucose values for a population of users to generate a new updated population model for a subset of users of the population of users. As another example, technologies are provided for updating an existing personalized model for estimating glucose values to generate a new updated personalized model that is personalized for a particular user.

Abstract: Disclosed herein are techniques related to safeguards against separation from portable medicine delivery devices. In some embodiments, the techniques may involve monitoring a wireless connection established between a portable computing device and a portable medicine delivery device. The techniques may also involve making a determination, based on the result of the monitoring, that the portable medicine delivery device is outside a predetermined range of the portable computing device. The techniques may further involve generating a notification based on the determination. The notification may be indicative of a user of the portable computing device being unaccompanied by the portable medicine delivery device.

Abstract: Disclosed herein are techniques related to safeguards against usage of incorrect portable medicine delivery devices. Such techniques may be practiced for a plurality of portable medicine delivery devices that include a first device and a second device. The techniques may involve obtaining usage data indicative of the first device being manipulated in preparation for medicine delivery. The techniques may further involve determining, based on the usage data, that the first device is mistakenly being manipulated instead of the second device. The technique may also involve, responsive to determining that the first device is mistakenly being manipulated, generating a message to prevent medicine delivery by the first device.

Abstract: Devices, systems, and techniques for controlling delivery of therapy for diabetes are described. In one example, a system includes a wearable device configured to generate user activity data associated with an arm of a user; and one or more processors configured to: identify at least one gesture indicative of utilization of an injection device for preparation of an insulin injection based on the user activity data; based on the at least one identified gesture, generate information indicative of at least one of an amount or type of insulin dosage in the insulin injection by the injection device; compare the generated information to a criteria of a proper insulin injection; and output information indicative of whether the criteria is satisfied based on the comparison.

Abstract: An example system for therapy delivery includes one or more processors configured to in response to a prediction indicating that the meal event is to occur, output instructions to an insulin delivery device to deliver a partial therapy dosage, to a device to notify the patient to use the insulin delivery device to take the partial therapy dosage, or to the insulin delivery device to prepare the partial therapy dosage prior to the meal event occurring, and in response to a determination indicating that the meal event is occurring (e.g., based on movement characteristics of a patient arm), output instructions to the insulin delivery device to deliver a remaining therapy dosage, to the device to notify the patient to use the insulin delivery device to take the remaining therapy dosage, or to the insulin delivery device to prepare the remaining therapy dosage.

Abstract: Disclosed herein are techniques related to managing dose recommendations in medicine administering systems. The techniques may involve obtaining a recommendation for a dose of medicine to be delivered to a patient and determining that the recommendation is outdated based on detecting one or more intervening events occurring after generation of the recommendation but before any delivery of medicine in accordance with the recommendation. The techniques may further involve implementing one or more safety features for avoiding delivery of the dose of medicine to the patient.

Abstract: An example system for therapy delivery includes one or more processors configured to in response to a prediction indicating that the meal event is to occur, output instructions to an insulin delivery device to deliver a partial therapy dosage, to a device to notify the patient to use the insulin delivery device to take the partial therapy dosage, or to the insulin delivery device to prepare the partial therapy dosage prior to the meal event occurring, and in response to a determination indicating that the meal event is occurring (e.g., based on movement characteristics of a patient arm), output instructions to the insulin delivery device to deliver a remaining therapy dosage, to the device to notify the patient to use the insulin delivery device to take the remaining therapy dosage, or to the insulin delivery device to prepare the remaining therapy dosage.

Abstract: Techniques for glucose level management are disclosed. In some examples, the techniques may involve obtaining an estimated protein value for a meal and determining an equivalent carbohydrate value for the meal based on the estimated protein value. In some examples, the techniques may involve obtaining an estimated fat value for a meal and determining, based on the estimated fat value, a glucose absorption rate resulting from consumption of the meal. In some examples, the equivalent carbohydrate value and/or the glucose absorption rate may be used to determine an amount of insulin to deliver to a patient to counteract a glucose level increase caused by consumption of the meal. The techniques may further involve generating an output indicative of the amount of insulin to deliver to the patient to counteract the glucose level increase caused by consumption of the meal.

Abstract: Techniques for glucose level management are disclosed. In some examples, the techniques may involve obtaining an estimated protein value for a meal and determining an equivalent carbohydrate value for the meal based on the estimated protein value. In some examples, the techniques may involve obtaining an estimated fat value for a meal and determining, based on the estimated fat value, a glucose absorption rate resulting from consumption of the meal. In some examples, the equivalent carbohydrate value and/or the glucose absorption rate may be used to determine an amount of insulin to deliver to a patient to counteract a glucose level increase caused by consumption of the meal. The techniques may further involve generating an output indicative of the amount of insulin to deliver to the patient to counteract the glucose level increase caused by consumption of the meal.

Abstract: Disclosed herein are techniques related to adaptive signal processing. The techniques may involve: obtaining a plurality of unfiltered measurement values based on signals generated by a sensor; determining a plurality of filtered measurement values based on the plurality of unfiltered measurement values; determining, based on the plurality of filtered measurements, a first derivative metric for a current filtered measurement of the plurality of filtered measurements and a second derivative metric for the current filtered measurement; determining an output filtered measurement indicative of a physiological condition of a user based at least in part on the current filtered measurement, the first derivative metric, the second derivative metric, and a previous output measurement; and outputting the output filtered measurement.

Abstract: Techniques for determining insulin therapy are described. The techniques include obtaining patient characteristic information for a current patient; determining, with a machine-learning model, an insulin delivery therapy from a plurality of insulin delivery therapies for the current patient based on the patient characteristic information, wherein the machine-learning model is generated based on digital representations of a plurality of patients; and outputting information indicative of the determined insulin delivery therapy.

Abstract: Disclosed herein are techniques related to glucose level management without carbohydrate counting. The techniques may involve obtaining contextual information for a meal, predicting amounts of glucose to be absorbed into a bloodstream of the patient over a duration of time due to consumption of the meal based on the contextual information for the meal, determining one or more amounts of insulin to counteract effects of the predicted amounts of glucose, and affecting insulin therapy based on outputting information indicative of the determined one or more amounts of insulin.

Abstract: Disclosed herein are techniques related to glucose level management without carbohydrate counting. The techniques may involve obtaining contextual information for a meal, predicting amounts of glucose to be absorbed into a bloodstream of the patient over a duration of time due to consumption of the meal based on the contextual information for the meal, determining one or more amounts of insulin to counteract effects of the predicted amounts of glucose, and affecting insulin therapy based on outputting information indicative of the determined one or more amounts of insulin.

Abstract: Disclosed herein are techniques related to personalized event detection. In some embodiments, the techniques may involve obtaining a plurality of glucose measurements indicative of a glucose level in a body of a patient during an analysis interval, obtaining a patient-specific meal detection model that identifies one or more glucose measurement statistics that are correlative to occurrence of a meal for the patient, determining values for the one or more glucose measurement statistics based on the plurality of glucose measurements, determining a meal consumption metric based on the values for the one or more glucose measurement statistics, detecting an occurrence of the meal during the analysis interval based on the meal consumption metric, and in response to detecting the occurrence of the meal, providing an indication of the occurrence of the meal during with the analysis interval.