Provided herein is a method for diagnosing and treating a subject at risk for atrial fibrillation (AF) or related health conditions, the method including: collecting one or more physiological signals from the subject in a sleep state or an awake state; extracting time series data from the one or more physiological signals; performing dynamic analyses of the time series data using artificial intelligence, wherein the artificial intelligence calculates a series of dynamic measurements, said dynamic measurements being indicative of a probability of an onset of an abnormal atrial rhythm; providing an integrated personalized risk score including the dynamic measurements, wherein the integrated personalized risk score is indicative of a probability of an onset of AF in the subject; diagnosing the subject as being at risk for AF when the integrated personalized risk score exceeds a threshold value, wherein the threshold value is calculated by the artificial intelligence based on a library of stored data; and treating the diagnosed subject with an effective therapy to prevent or treat AF or AF-related health conditions.

There is provided a method of training a machine learning model for generating a sleep wellness score used for treatment of a sleep condition in a target individual, comprising: providing a baseline machine learning model with weights set to initial baseline values, accessing sleep-parameters computed for historical sleep sessions of the target individual, training the baseline machine learning model using the sleep-parameters for the historical sleep sessions of the target individual by adjusting the initial baseline values of the weights, to obtain a customized machine learning model, accessing sleep-parameters computed for previous sleep session(s) of the target individual, inputting the sleep-parameters computed for previous sleep session(s) into the customized machine learning model, and obtaining a sleep wellness score as an outcome of the customized machine learning model.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for geofencing and location tracking for predicting and limiting disease exposure. In some implementations, location tracking data is received indicating locations of user devices over time. Location tags specifying visits of the user devices to different locations indicated by the location tracking data are defined. A geofence is assigned to each of the location tags to specify a geofenced area corresponding to the location tag. Disease transmission scores are assigned to first location tags representing visits of a first user. A disease exposure score is determined for a second user whose user device is determined, based on the location tracking data, to have entered at least one of the geofenced areas corresponding to the first location tags.

Methods and devices provide physiological movement detection with active sound generation. In some versions, a processor may detect breathing and/or gross body motion. The processor may control producing, via a speaker coupled to the processor, a sound signal in a user's vicinity. The processor may control sensing, via a microphone coupled to the processor, a reflected sound signal. This reflected sound signal is a reflection of the sound signal from the user. The processor may process the reflected sound, such as by a demodulation technique. The processor may detect breathing from the processed reflected sound signal. The sound signal may be produced as a series of tone pairs in a frame of slots or as a phase-continuous repeated waveform having changing frequencies (e.g., triangular or ramp sawtooth). Evaluation of detected movement information may determine sleep states or scoring, fatigue indications, subject recognition, chronic disease monitoring/prediction, and other output parameters.

A method for managing sleep of a user comprises obtaining, by a computing system, sleep data and environmental data for the user; determining, by the computing system, a sleep state of the user based on the sleep data; determining, by the computing system, one or more awakening actions based on the sleep state of the user and the environmental data; and causing one or more devices in an environment of the user to perform the one or more awakening actions to awaken the user.

Wearable blood pressure biosensors, systems, and methods for short-term blood pressure predictions are disclosed herein. In some embodiments, a blood pressure prediction system is configured to provide short-term predictions of average blood pressures for future time period(s) or horizon(s) (e.g., for a coming week). The system can include models trained to predict future systolic values, diastolic blood pressure values, and/or trends. The models can be trained on data related to personalized body, health, and/or physical characteristics of the user, e.g., the current or previous blood pressure, amount of sleep, heart rate, blood glucose (BG), activity, weight, etc. In some embodiments, the models can also determine whether the blood pressure of the user will change or remain relatively constant over a period/range of time.

Examples of the present subject matter provide techniques for measuring stress levels using a variety of physiological indicators, such as pupil diameter, voice, cortisol levels, skin resistance, etc. Different sensors may be provided to measure the physiological indicators. Those measurements may be collected at a central server, where those measurements may be analyzed to determine the stress level of the user.

Novel tools and techniques are provided for implementing intelligent assistance (“IA”) or extended intelligence (“EI”) ecosystem to placement procedures for cardiac implantable electronic device (“CIED”). In various embodiments, a computing system might analyze received one or more first layer input data (i.e., room content-based data) and received one or more second layer input data (i.e., patient and/or tool-based data), and might generate one or more recommendations for guiding a medical professional in performing a CIED placement procedure in a heart of the patient, based at least in part on the analysis, the generated one or more recommendations comprising 3D or 4D mapped guides toward, in, and around the heart of the patient. The computing system might then generate one or more XR images, based at least in part on the generated one or more recommendations, and might present the generated one or more XR images using a UX device.

A method and a system of alerting and/or monitoring patient with sleep disorder includes: a detector for detecting a change in a first parameter, a storage device, a control unit for deciding if the change meets a set criteria, and if the change meets the set criteria, saving the first parameter and/or time in the storage device, a feedback unit for adjusting the set criteria according to sleep behavior of the patient, and an alarm device for sending an alarm, wherein the first parameter includes sound, motion, heart rate, blood pressure, breathing frequency, magnitude and/or frequency of movement, muscle activity, brain activity, eye movements, heart rhythm, heart rate variability, blood oxygen levels, breathing pattern, and/or body position.

A sleep monitoring circuit and a sleep monitoring apparatus are provided, in the circuit: a bidirectional receiving unit includes an electrode pad, and when the electrode pad receives a power supply signal, a handover control unit generates a charging control signal according to the power supply signal, so as to control a charging unit to perform charging management; when the electrode pad receives a bioelectric signal, a command acquisition unit acquires from a user a sleep monitoring command, so as to trigger an enabling unit to generate a monitoring handover signal, the handover control unit outputs a bioelectric signal to a bioelectric signal pick-up unit according to the monitoring handover signal, causing the bioelectric signal pick-up unit to extract feature information from the bioelectric signal and output same to a sleep monitoring unit, and the sleep monitoring unit generates a person sleep monitoring result according to the feature information.