A system (10) for configuring data collection for provision to a digital twin for generating a desired set of output information (e.g. physiological or anatomical parameter estimations) from the digital twin. The system is configured to detect available sources (56) of physical sensor data (58) pertaining to a patient, and to compare these with determined input data requirements of a digital twin (32) of the patient for computing a particular defined set of required output information. Depending on the result, a virtual sensing module (28) can be used to generate supplementary ‘virtual’ sensor data to compensate for any insufficiencies in the available physical sensor data or augment the physical sensor data, and the system can re-configure operating settings of the available physical sensors (56) to optimally meet the input data requirements of the digital model.

An oximetry device sealed in a sheath directs a user to allow the oximetry device to make oximetry readings at a number of different tissue locations of a patient and average two or more of the oximetry readings by directing the lifts and placements of the oximetry device and sheath to and from the different tissue locations and detecting the lift and placements. The averages are generated and displayed on a display of the device for the oximetry readings if the lifts are made while use directions for the lifts are displayed on a display of the oximetry device. The averages are not generated if the lifts are not made while the user directions for the lifts are not displayed. The averages are simultaneously displayed with the oximetry readings which are instantaneous measurement for patient tissue.

A peristaltic pump includes a plunger-cam follower, a tube receiver, a spring-biased plunger, a spring, a position sensor, and a processor. The plunger-cam follower engages the plunger cam to follow the plunger cam and to disengage from the plunger cam. The spring-biased plunger is coupled to the plunger-cam follower and the spring biases the spring-biased plunger toward the tube receiver. The position sensor determines a position of the spring-biased plunger when the plunger-cam follower is disengaged from the plunger came. The processor estimates fluid flow utilizing at least the position of the spring-biased plunger as indicated by the position sensor when the plunger-cam follower is disengaged from the plunger cam and the spring biases the spring-biased plunger against the tube.

A peristaltic pump includes a plunger-cam follower, a tube receiver, a spring-biased plunger, a spring, a position sensor, and a processor. The plunger-cam follower engages the plunger cam to follow the plunger cam and to disengage from the plunger cam. The spring-biased plunger is coupled to the plunger-cam follower and the spring biases the spring-biased plunger toward the tube receiver. The position sensor determines a position of the spring-biased plunger when the plunger-cam follower is disengaged from the plunger came. The processor estimates fluid flow utilizing at least the position of the spring-biased plunger as indicated by the position sensor when the plunger-cam follower is disengaged from the plunger cam and the spring biases the spring-biased plunger against the tube.

The disclosed embodiments are directed to methods for dynamically adjusting the total daily insulin requirements of a user during pregnancy, based on the gestational week. An initial estimate of the adjusted total daily insulin requirement may be calculated as a multiple of the pre-pregnancy total daily insulin requirement, based on an average scale factor from a population of pregnant women suffering from Type I diabetes mellitus. An automatic drug delivery device may adjust the initial estimate of the total daily insulin requirement based on blood glucose level readings from a continuous glucose monitor during the course of the pregnancy.

Provided is an intelligent system for search and rescue in a special environment such as a disaster, including a body surface feature extracting apparatus, a vital sign extracting apparatus, a speech feature extracting apparatus and a network transmission apparatus that are successively in communication connection with one another. The body surface feature extracting apparatus uses a gated recurrent unit (GRU) network model for transmission and storage. The speech feature extracting apparatus includes a sound collecting module, a sound feature extracting module and a sound analyzing and processing module that are successively in communication connection with one another, with the sound analyzing and processing module being provided with a noise database comprising a plurality of ambient sounds. The network transmission apparatus includes a Zigbee network communication module, a network transmission module, a drone network relay module and a network receiving base station that are successively in communication connection with one another.

Provided are techniques, devices and systems that include monitoring a trend of blood glucose measurement values over a series of measurement cycles. A processor may identify a potential excursion outside a range of a target blood glucose measurement value setting of a user based on the monitored trend. In response to the identified potential excursion, an alert may be generated to the user to consume rescue carbohydrates. In addition, the disclosed techniques may include a processor that assesses the factors related to a potential impending hypoglycemic event for a user. Based on a result of the assessment of the factors, the processor may determine whether the user is approaching the potential impending hypoglycemic event for the user. In response to a determination that the user is approaching the potential impending hypoglycemic event for the user, a number of rescue carbohydrates to suggest for consumption by the user may be determined.

The present disclosure relates to an apparatus for predicting a hemodynamics parameter being conventionally obtained from an implanted sensor or catheter (invasive sensor), e.g., pulmonary artery pressure, based on noninvasive biosignals, such as electrocardiographic (ECG), impedance cardio graphic (ICG), phonocardiogram (PCG), pulse oximetry plethysmograph (PPG). The present disclosure also relates to a method of feeding multiple noninvasive biosignals and/or general inputs into an AI model or AI models to predict a hemodynamics parameter, such as pulmonary artery pressure, which is conventionally obtained from an implanted sensor or catheter.

System (10) for controlling a timing of consecutive biological samples (s0, s1) from a patient for a medical test (T), comprising: a registration device (30), configured for determining a patient id (PID) indicating an identity of the patient, and a first sample time point (t0) indicating a time of obtaining a first biological sample (s0) from the patient at a first time point (t0); a processing system (20), communicatively connected to the registration device (30), configured for receiving the patient id (PID) and the first sample time point (t0) from the registration device (30); determining a protocol rule (R) for the medical test (T) relating to timing requirements of obtaining biological samples (s0, s1) from the patient depending on the medical test (T); and determining a sample timing dependent on the protocol rule (R) and the first sample time point (t0), indicating when to obtain a second biological sample (s1) from the patient in compliance with the protocol rule (R).

Embodiments of a system for controlling an object using brainwaves are disclosed. The system includes a set of EEG electrodes configured to be positioned on a head of a user and to collect EEG signals. The system further includes one or more computer readable storage mediums storing a framework configured to execute an extensible architecture through which EEG signals are interpreted for control of the object. The framework includes an EEG device plugin associated with the set of EEG electrodes and configured to extract the EEG signals from the set of EEG electrodes. The framework also includes an interpreter plugin configured to convert the EEG signals extracted by the EEG device plugin into a command. Further, the framework includes an object control plugin configured to access the command through an extension point of the interpreter plugin and to execute the command to control the object.