A subject's Default Mode Network is accessed through corresponding measurements of the Micro-Coherence Oximetry Network Strength (MCO-S). An associated MCO-S system (100) includes a wearable (102), a user device (112) and a processing platform (123). The wearable (102) collects subject information sufficient to enable monitoring and optimization of the subject's Default Mode Network include sensors such as pulse oximetry instrumentation and EEG electrodes to obtain brainwave data, oxygen saturation data, heart rate variability data, and galvanic skin conductance data. Information from the sensors may be communicated to a user device (112), such as a cell phone or VR headset. The user device (112) communicates with a remote processing platform (123) that may execute artificial intelligence functionality and other logic in connection with assessing the patient's micro-coherence network strength and optimizing behavior modification protocols in relation to attributes and objectives of the subject.

The invention provides systems and methods for monitoring the wellbeing of a fetus by the non-invasive detection and analysis of fetal cardiac electrical activity data.

Disclosed in the present disclosure is a method, system and apparatus for prediction, estimation and prevention of occurrence of agitation episode in a subject predisposed to agitation. The method comprises receiving, from a first monitoring device attached to a subject, physiological data of sympathetic nervous system activity in the subject and activity data of the subject; receiving, from a computing device, a plurality of indications associated with a plurality of agitation episodes of the subject; analyzing, using at least one machine learning model, the physiological data, the activity data, and the plurality of indications to determine a probability of an occurrence of an agitation episode of the subject; and sending a signal to a second monitoring device to notify the second monitoring device of the probability of the occurrence of the agitation episode of the subject such that treatment can be provided to the subject to decrease sympathetic nervous system activity in the subject.

A system for monitoring a sleep of a user includes a plurality of patches for placement adjacent to a surface of a body of a user, a processor, and a data communication system. Each patch from the plurality of patches includes at least one sensor. The data communication system transmits positional data generated by the plurality of sensors, including orientation data and motion data, to the processor. The processing of the positional data includes detecting a change in position of the body of the user between a first position and a second position. Based on the first image and the second image, an animation of a movement of the body from the first position to the second position is generated.

The present invention relates to a wearable smart appliance in the form of a wristwatch. The appliance is designed to track a user's vital signs and send real-time alerts to a paired electronic device for remote monitoring of the user. More specifically, the appliance tracks vital signs such as heart rate, and notifies a parent or guardian if the user is in danger. The appliance also includes a water sensor that activates an alert if the sensor is underwater for a certain length of time. A GPS sensor detects the current location of the user and is included in the real-time alerts during emergencies.

This disclosure relates to technologies for a sensor network, machine-to-machine (M2M) communication, machine type communication (MTC), and Internet of Things (IoT). This disclosure can be utilized in intelligent services based on the above technologies, such as smart homes, smart buildings, smart cities, smart cars or connected cars, health care, digital education, retail sales, security and safety related services, etc. This disclosure relates to a method for generating an instruction for controlling equipment on the basis of biometric information, comprising: a step of obtaining at least one biometric information; a step of determining whether to calculate a calorific value by using stored biometric information and the obtained biometric information, and calculating the calorific value by using the stored biometric information and the obtained biometric information according to the determined result; and generating an instruction for controlling the equipment on the basis of the calculated calorific value.

A system and method for managing the care of a patient includes receiving (410) physiological signals of a patient; extracting (440) respiration information from the physiological signals; determining a vital sign of the patient by: using (450, 460) the respiration information to determine portions of the physiological signals, or of vital sign information extracted from the physiological signals, that correspond to the expiration phase of the respiratory cycle; determining (470) a vital sign of the patient using only the portions of the physiological signals, or of the vital signal information, that correspond to an expiration phase of the respiratory cycle; and displaying an indication of the determined vital sign at an output device.

The present disclosure provides systems and method of analyzing whether a sleep epoch is a REM sleep epoch or a wake epoch. In accordance with aspects of the present disclosure, a computer-implemented method includes accessing cardiopulmonary coupling data spanning a sleep period for a person, identifying an epoch in the sleep period corresponding to very-low frequency coupling in the cardiopulmonary coupling data, accessing high-frequency coupling data and/or low-frequency coupling data in the cardiopulmonary coupling data corresponding to the epoch, and designating the epoch as a REM sleep epoch or as a wake epoch based on the high-frequency coupling data and/or the low-frequency coupling data corresponding to the epoch, where the epoch is designated based on the cardiopulmonary coupling data without using non-cardiopulmonary coupling physiological data.

A system for capturing behavioural data in order to influence an interpersonal interaction. In one aspect, the system assists n the training of an entity whose role is to engage in such interpersonal interactions. In this aspect, the collected information may be used to judge performance, and/or adapt or improve approach to future interactions, In another aspect, the system may assist with a live interaction, i.e. to provide feedback in an ongoing conversation. The system comprises a wearable device having (i) a data collection device configured to capture behavioural data during the interpersonal interaction, (ii) a microphone configured to capture audio data during the interpersonal interaction, and (ill) an analysis module arranged to extract emotional state information and content data, and use that extracted data to evaluate an interaction quality metric to obtain an interaction score for the interpersonal interaction.

Computer-aided diagnosis algorithms require a large volume of training data. The existing methods for simulating artificial biomedical signals are mostly based on physics driven mathematical models that require too many assumptions, making them challenging to simulate on a large scale. Alternatively, conventional deep learning-based approaches are pure data driven and hence, do not have physiological interpretation. The present disclosure provides a method that effectively combines both physiological domain knowledge and deep learning to enable simulation of realistic cardiovascular disease specific biomedical signals. An ensemble Generative Adversarial Network (GAN) including a Long Short-Term Memory GAN (LSTM-GAN) configured to generate a Heart Rate Variability (HRV) pattern associated with the cardiovascular disease condition and a Deep Convolutional GAN (DCGAN) configured to create a morphology of a representative cardiac cycle is provided. A complete waveform is simulated by combining an output from each GAN.