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119
results.
Updated on
29/08/2025 [06:57:00]
Results 100 to 119 of 119
Absstract of: US2025213191A1
A continuous analyte monitoring system includes analyte sensors configured to sense analytes such as lactate and glucose in the tissue of a user. A controller is coupled to the analyte sensors and configured evaluate first samples of outputs of a first analyte sensor and second samples of outputs of a second analyte sensor to determine whether the first samples and the second samples indicate compression of the tissue. The controller compensates for the compression of the tissue with respect to the first samples. A force sensor may be used and may be positioned between a circuit board and a housing, the circuit board supported by supports providing preloading of the force sensor. A force deflector may be used to direct loads away from tissue holding the analyte sensors. A housing may have a flexible lower surface to reduce loading of the tissue.
Absstract of: US2025213155A1
The present disclosure relates to a body attachment unit for continuous blood glucose measurement, in which the body attachment unit is manufactured so as to be assembled in an applicator, thereby minimizing additional work and allowing attachment of the body attachment unit to a body simply by operation of the applicator. In particular, a wireless communication chip is provided in the body attachment unit to enable communication with an external terminal, thereby enabling simple and convenient use without performing additional work of connecting a separate transmitter, and allowing easier maintenance. In addition, activation occurs by a user's operation after the body attachment unit is attached to the body, such that an activation start point in time can be adjusted to an appropriate point in time according to the user's needs, and activation occurs in a stabilized state, thereby providing the body attachment unit allowing more accurate blood glucose measurement.
Absstract of: US2025213785A1
Disclosed herein are improved infusion sets that incorporate halogenated siloxane materials within the composition of an infusion set septum. Such configurations maintain the mechanical robustness of using elastomeric materials for a fluidic septum while providing barrier properties that prevent phenolic preservative from being absorbed out of the infusion set. Infusion sets as disclosed herein are particularly suitable for maintaining insulin's anti-microbial effectiveness over extended wear (e.g., 7 days) to maintain performance of the infusion set during extended wear and reduce the risk of insulin aggregate propagation.
Absstract of: US2025213784A1
Systems and methods are provided for enabling and disabling insulin pumps in a multi-pump insulin pump system. For example, multiple pumps (e.g., patch pumps) may be rotated to deliver insulin to a user. In one example, one patch pump may be worn while another patch pump is charging. Once the battery of the patch pump worn by the user is depleted, a mobile device may cause the enabled pump in operation and worn by the user to send the latest pump settings and configuration data to the mobile device which will share the information with the disabled pump. The disabled pump will then update the pump settings based on this information and the mobile device may active the pump such that is now enabled for insulin delivery. The mobile device may also facilitate pairing between the patch pumps and a CGM device as they enabled for use and disabled.
Absstract of: US2025218600A1
Glucose forecasting systems and methods that include a minimally invasive scalp-worn behind-the-ear EEG device that includes first and second sensors. With an application on a personal device, analyzing the processed EEG signals with a trained forecasting model and forecasting future glucose levels, of the subject; causing the personal device to visually present on a display information that is indicative of the forecasted future glucose levels.
Absstract of: US2025218565A1
The present disclosure provides a technology capable of appropriately supporting improvement of an individual's lifestyle. The lifestyle improvement support system is provided with an acquisition device capable of acquiring from a target person at least their blood glucose level data, physical activity data, and dietary activity data, as well as a controlling device.
Absstract of: US2025218598A1
Methods and devices include predicting future glucose and engagement levels for a user by receiving the user's glucose levels collected by a continuous glucose monitoring (CGM) device over a time period, receiving engagement data associated with the user, wherein the engagement data are associated with the user's medication intake, diet, physical activity, laboratory results, and education activity, determining a first glycemia risk index (GRI) value, determining, using a machine learning model and responsive to the user's glucose levels and the engagement data collected over the time period, one or more predictions for future glucose levels for the user including a prediction that a future GRI value is greater than or less than the first GRI value, and determining, using the machine learning model and responsive to the user's engagement data collected over the time period, one or more predictions for future engagement levels.
Absstract of: WO2025145062A1
Disclosed herein are system, method, and computer program product embodiments for calculating accurate doses for medication using improved user interface features that may be implemented as part of an application operating within a continuous glucose monitoring system. The disclosed interface provides an improved user experience when a medication delivery device is disconnected or does not have the ability to query the requisite data.
Absstract of: WO2025141541A1
Systems and methods are provided for enabling and disabling insulin pumps in a multi- pump insulin pump system. For example, multiple pumps (e.g., patch pumps) may be rotated to deliver insulin to a user. In one example, one patch pump may be worn while another patch pump is charging. Once the battery of the patch pump worn by the user is depleted, a mobile device may cause the enabled pump in operation and worn by the user to send the latest pump settings and configuration data to the mobile device which will share the information with the disabled pump. The disabled pump will then update the pump settings based on this information and the mobile device may active the pump such that is now enabled for insulin delivery. The mobile device may also facilitate pairing between the patch pumps and a CGM device as they enabled for use and disabled.
Absstract of: WO2025140911A1
A method performed by a computer system for monitoring diabetes and comprising: a model explainability phase, comprising steps of: - collecting specific foot data from at least one sensor disposed at least within a shoe insole of a user, - collecting a plurality of trained machine learning models configured for decision-making in diabetes diagnosis based on a plurality of collected data, - based on the plurality of trained machine learning models, generating a plurality of specific machine learning models, said specific machine learning models being specified for decision-making in diabetes diagnosis based on the specific foot data, and a model reliability assessment phase, comprising steps of: - evaluating a reliability of each of the specific machine learning model based on neutrosophic sets and intuitionistic fuzzy sets, - based on the result of the evaluation step, selecting the optimized machine learning model for monitoring diabetes based on the specific foot data.
Absstract of: WO2025144547A1
A continuous analyte monitoring system includes analyte sensors configured to sense analytes such as lactate and glucose in the tissue of a user. A controller is coupled to the analyte sensors and configured evaluate first samples of outputs of a first analyte sensor and second samples of outputs of a second analyte sensor to determine whether the first samples and the second samples indicate compression of the tissue. The controller compensates for the compression of the tissue with respect to the first samples. A force sensor may be used and may be positioned between a circuit board and a housing, the circuit board supported by supports providing preloading of the force sensor. A force deflector may be used to direct loads away from tissue holding the analyte sensors. A housing may have a flexible lower surface to reduce loading of the tissue.
Absstract of: AU2023415787A1
The present invention provides a sensor applicator assembly for a blood glucose meter, comprising: an applicator; and a body attachment unit which is disposed in the applicator, and which is composed of a housing including an upper frame and a lower frame, wherein the lower frame has a recessed part formed along the entire circumferential section thereof.
Absstract of: US2019013091A1
User interfaces are needed to alert and inform a person suffering from diabetes or a healthcare provider of possible harmful changes in the patient's blood glucose level. An improved method is presented for issuing an alert regarding a glucose condition of a patient by a diabetes management system. For a given patient, their glucose condition is quantified and presented as a state on a display. Glucose conditions can also be prioritized for a plurality of patients and presented to a healthcare provider.
Absstract of: WO2025145024A1
Disclosed herein are improved infusion sets that incorporate halogenated siloxane materials within the composition of an infusion set septum. Such configurations maintain the mechanical robustness of using elastomeric materials for a fluidic septum while providing barrier properties that prevent phenolic preservative from being absorbed out of the infusion set. Infusion sets as disclosed herein are particularly suitable for maintaining insulin's anti-microbial effectiveness over extended wear (e.g., 7 days) to maintain performance of the infusion set during extended wear and reduce the risk of insulin aggregate propagation.
Absstract of: WO2025145056A1
Disclosed herein are apparatuses and methods that account for exercise in closed loop insulin delivery systems. The apparatuses and methods disclosed herein address exercise-induced glucose level increases by reducing the target IOB within the closed loop. By reducing the target IOB, the algorithm responds less aggressively to pre-exercise food, and does not build up the IOB that could potentially contribute to undesirably low glucose levels once the exercise also begins lowering glucose levels. Following exercise, because the user's body will not immediately transition to a pre-exercise state, the algorithm will gradually transition back to the initial target IOB.
Absstract of: WO2025140661A1
A blood glucose management method, a user interface, and a related apparatus. The blood glucose management method comprises: on the basis of historical blood glucose levels of a user, determining a first mealtime; on the basis of one or more of a mealtime inputted by the user, and body movements and the heart rate of the user, determining a second mealtime; on the basis of the first mealtime and the second mealtime, determining a third mealtime; and assessing a blood glucose health status of the user by means of the third mealtime. Historical mealtimes of a user that are determined using multiple methods are combined to predict a mealtime of the user, so that an electronic device can more accurately assess a blood glucose situation of the user.
Absstract of: WO2025140653A1
The present application discloses a measurement method, a system, and a related apparatus. The method is applied to a first electronic device, wherein the first electronic device comprises a first electrode set and a second electrode set; the distance between the first electrode set and the second electrode set is greater than a first distance; and under the condition that the first electrode set and the second electrode set are implanted into subcutaneous tissue, the method comprises: determining a first physiological parameter by means of the first electrode set; and determining an electrocardiogram signal by means of the first electrode set and the second electrode set. In this way, an electrocardiogram signal of a user can be acquired by the first electronic device anytime and anywhere, and any one or more of other physiological parameters (such as blood glucose, blood ketone, blood lactate, and uric acid) of the user can also be measured, thereby improving the health monitoring efficiency.
Absstract of: WO2025145037A1
Disclosed herein are portable infusion pump systems that provide an ability to simultaneously track insulin on board from insulin deliveries from different sources and/or of different types. A user interface can be provided to enable the user to enter insulin delivered from a source other than the portable infusion pump. A simplified calculation for estimating IOB remaining from each insulin delivery enables such calculations to be carried out with the limited memory and processing capacity of the portable infusion pump.
Nº publicación: LU505951B1 30/06/2025
Applicant:
LUXEMBOURG INSTITUTE OF SCIENCE AND TECH LIST [LU]
LUXEMBOURG INSTITUTE OF SCIENCE AND TECHNOLOGY (LIST)
Absstract of: LU505951B1
A method performed by a computer system for monitoring diabetes and comprising: a model explainability phase, comprising steps of: - collecting specific foot data from at least one sensor disposed at least within a shoe insole of a user, - collecting a plurality of trained machine learning models configured for decision-making in diabetes diagnosis based on a plurality of collected data, - based on the plurality of trained machine learning models, generating a plurality of specific machine learning models, said specific machine learning models being specified for decision-making in diabetes diagnosis based on the specific foot data, and a model reliability assessment phase, comprising steps of: - evaluating a reliability of each of the specific machine learning model based on neutrosophic sets and intuitionistic fuzzy sets, - based on the result of the evaluation step, selecting the optimized machine learning model for monitoring diabetes based on the specific foot data.
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