A drinking behavior monitoring device is provided which comprises a stress level detection sensor (300) detecting a stress level of a baby (20), a suction frequency detection sensor (200) detecting a suction frequency during a feeding of the baby and an analyzer (400) analyzing a drinking behavior of a baby based on the detected stress level and the detected suction frequency. The analyzer (400) compares the analyzed drinking behavior with a typical or predetermined drinking behavior and can output a notification based on the analyzed drinking behavior.

Glucose measurement and glucose-impacting event prediction using a stack of machine learning models is described. A CGM platform includes stacked machine learning models, such that an output generated by one of the machine learning models can be provided as input to another one of the machine learning models. The multiple machine learning models include at least one model trained to generate a glucose measurement prediction and another model trained to generate an event prediction, for an upcoming time interval. Each of the stacked machine learning models is configured to generate its respective output when provided as input at least one of glucose measurements provided by a CGM system worn by the user or additional data describing user behavior or other aspects that impact a person's glucose in the future. Predictions may then be output, such as via communication and/or display of a notification about the corresponding prediction.

A posture improvement device, system, and method. The system for improving posture may comprise: a sensor device; posture improvement software program, comprising a posture improvement system interface; and one or more user devices. The sensor device may be physically associated with a user and may communicate with the posture improvement software program. The sensor device may comprise: one or more sensors for monitoring positions and movements of the user. The system may calculate one or more optimum postural positions for the user, based on data communicated by the sensor device and collected information about the user. The system may monitor a conformance of the user with the optimum postural positions and may display the conformance on the posture improvement system interface. The system may detect and notify the user of one or more non-conformances, such that a user is reminded to maintain at least one optimum postural position.

Provided are systems, methods, and devices for providing mediation of a traumatic brain injury. Systems may include an interface, processing devices, and a controller. The interface is configured to obtain measurements from a brain of a user with a traumatic brain injury. A first processing device is configured to generate multiple brain state parameters characterizing one or more features of a brain state of the user. A second processing device is configured to generate models of the brain of the user based on the plurality of brain state parameters and the plurality of measurements, and determine, using the models and training data comprising one or more mediation data points, a mediation procedure for reducing one or more symptoms of the traumatic brain injury. The mediation procedure is provided to one or more entities, and one or more control signals are generated by the controller based on the mediation procedure.

Provided is a system (10) for determining a probability of a CORD exacerbation in a subject. The system comprises a first inhaler (100) for delivering a rescue medicament to the subject. The rescue medicament may be suitable for treating the subject's acute respiratory disease, for example by effecting rapid dilation of the bronchi and bronchioles upon inhalation of the medicament. The first inhaler has a use-detection system (12B) configured to determine a rescue inhalation performed by the subject using the first inhaler. The system optionally includes a second inhaler for delivering a maintenance medicament to the subject during a routine inhalation A sensor system (12A) is configured to measure a parameter relating to airflow during the rescue inhalation and/or during the routine inhalation, when the second inhaler is included in the system. The system further comprises a processor (14) configured to determine a number of the rescue inhalations during a first time period, and receive the parameter measured for at least some of the rescue and/or routine inhalations. The processor then determines, using a weighted model, the probability of the CORD exacerbation based on the number of rescue inhalations and the parameters. The model is weighted such that the parameters are more significant in the probability determination than the number of rescue inhalations. Further provided is a method for determining the probability of a COPD exacerbation in a subject, which method employs the weighted model.

A system for monitoring sepsis comprises one or more sensors that noninvasively monitor a patient to produce sensor data and processing circuitry to: derive, from the sensor data, one or more physiological parameters of the patient that are indicative of a sepsis state of the patient; apply a set of rules to the one or more physiological parameters; determine a sepsis state for the patient based on the set of rules applied to the one or more physiological parameters; and output at least one signal indicative of the sepsis state of the patient. The system includes an output device that outputs a notification based on the at least one signal.

A multifunctional personal health monitor with an activity tracker embedded into a pet leash is provided. The energy of a pet pulling the leash cord is converted to an electrical power to run and to analyze operation of multiple biometric sensors embedded into the leash. The physical activities of the pet and its owner may be tracked. The obtain biometric information may be wirelessly transmitted for additional processing or an emergency call can be initiated. The generated by pet movement electrical power may be used to charge various external devices.

A system comprising a remotely programmable micromonitor with a wireless sensing system-on-module (SOM), one or more sensors to detect one or more conditions in a subject by monitoring one or more parameters associated with the conditions by comparing any monitored parameter to a baseline measurement of the monitored parameter from the subject, a plurality of sensors corresponding to a monitored parameter and connected to the micromonitor to convey measurements of all monitored parameters, the sensors including at least one of a non-optical pulse wave sensor or an electrocardiogram (ECG) sensor, a communications module capable of communicating with a wireless technology, wherein the module can send an alert signal to the subject or an attending physician or a remote service center or any other subject, and one or more algorithms for monitoring conditions and/or for predicting conditions, including at least one of a fall detection or fall prediction algorithm.

The present invention discloses a technique for alerting on vision impairment. The system comprises a processing unit configured and operable for receiving scene data being indicative of a scene of at least one consumer in an environment, identifying in the scene data a certain consumer, identifying an event being indicative of a behavioral compensation for vision impairment, and, upon identification of such an event, sending a notification relating to the vision impairment.

A solution for estimating a cardiac pre-ejection period is disclosed. According to an aspect, a method includes: measuring, from a user by using a plurality of cardiac sensor devices, electrocardiogram measurement data, a first set of cardiac measurement data measured at a first location of the user's body a first distance from the user's heart, and a second set of cardiac measurement data measured at a second location of the user's body a second distance from the heart, the second distance different from the first distance, wherein the electrocardiogram measurement data is clock-synchronized with the first set of cardiac measurement data and second set of cardiac measurement data; determining, in the electrocardiogram measurement data, a first set of time instants associated with electric heart activations; determining, in the first set of cardiac measurement data, a second set of time instants associated with detections of blood pulses at the first location, the blood pulses resulting from the electric heart activations; determining, in the second set of cardiac measurement data, a third set of time instants associated with detections of the blood pulses at the second location; forming a set of scatter points on the basis of at least the second set of time instants and the third set of time instants and performing a fitting for the scatter points; and computing the cardiac pre-ejection period from at least one parameter describing the fitting.