A system for monitoring medical conditions including pressure ulcers, pressure-induced ischemia and related medical conditions comprises at least one sensor adapted to detect one or more patient characteristic including at least position, orientation, temperature, acceleration, moisture, resistance, stress, heart rate, respiration rate, and blood oxygenation, a host for processing the data received from the sensors together with historical patient data to develop an assessment of patient condition and suggested course of treatment, including either suspending or adjusting turn schedule based on various types of patient movement. Compliance with Head-of-Bed protocols can also be performed based on actual patient position instead of being inferred from bed elevation angle. The sensor can include bi-axial or tri-axial accelerometers, as well as resistive, inductive, capacitive, magnetic and other sensing devices, depending on whether the sensor is located on the patient or the support surface, and for what purpose.

A method of providing content related to capture of a medical image of an object is provided. The method includes acquiring at least one of information related to a state of the object and information related to a capture protocol, determining content to be provided to the object on a basis of the acquired information, and outputting the determined content.

A method of analysing changes in the electrical activity of a patient's heart between a reference state and a test state, the method using a reference data set of electrophysiological data captured from the patient in the reference state and at least one test data set of electrophysiological data captured from the patient in the test state, each data set defining a plurality of electrograms for a respective plurality of spatial locations relative to the heart, the method comprising processing the electrophysiological data by, matching each electrogram in the reference data set to a corresponding electrogram in the at least one test data set to create a pair of electrograms for each of the plurality of spatial locations, and deriving a time delay for each spatial location by calculating the time delay between the electrograms of the pair of matched electrograms for that spatial location.

A method for monitoring health of a subject based on a physiological response to physical exertion, by processing circuitry of a medical device system, is described that includes detecting a plurality of exertion events of the subject based on a first sensed signal that varies as a function of movement of the subject. The method further includes determining a response of a physiological parameter of the subject to the exertion event for each of the detected exertion events based on second sensed signal that varies as a function of the physiological parameter. The method further includes determining that a change in the responses over time crosses threshold and generating an alert to a user based on the determination that the change crosses the threshold.

A physiological information processing method executed by a computer includes: obtaining a stress index parameter indicating a stress index of a subject based on physiological information data of the subject; comparing the stress index parameter with a predetermined threshold value; determining that the subject is stressed in accordance with the comparison between the stress index parameter and the predetermined threshold value; and outputting stress information indicating that the subject is stressed.

A wearable device includes a processor and a lower module. The lower module includes a pressure sensor for detecting a mechanical movement of a skin that covers an artery. The mechanical movement of the skin is due to blood flow through the artery. The processor is configured to receive skin movement information from the movement sensor; calculate a pulse front velocity (PFV), which is a velocity of a blood wave as the blood wave passes under the pressure sensor; estimate a pulse wave velocity (PWV) using the PFV; and estimate the blood pressure using the PWV.

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.

This disclosure relates generally to assessment of cognitive workload using breathing pattern of a person, where cognitive workload is the amount of mental effort required while doing a task. The method and system provides assessment of cognitive workload based on breathing pattern extracted from photoplethysmograph (PPG) signal, which is collected from the person using a wearable device. The PPG signal collected using the wearable device are processed in multiple stages that include breathing signal extraction to extract breathing pattern. The extracted breathing pattern is used for assessment of cognitive workload using a generated personalized training model, wherein the personalized training model is generated and dynamically updated for each person based on selection of a sub-set of breathing pattern features using feature selection and classification techniques that include maximal information coefficient (MIC) techniques. Finally based on personalized training model, the extracted breathing pattern is classified as high cognitive workload or low cognitive workload.

The stress estimation device acquires the awakening degree of the subject and calculates the feature amount of the acquired awakening degree. The feature amount of the awakening degree is, for example, a ratio at which the temporal change of the awakening degree is within a predetermined range, information defining a histogram showing the distribution of the temporal change of the awakening degree, and the like. Then, the stress estimation device estimates the stress from the calculated feature amount using the stress model.

The present invention relates to a method to predict the risk of obtaining a stress related mood disorder or syndrome by a person, comprising a. Measuring at least three parameters comprising at least one sympathetic, one parasympathetic and one hormonal parameter during a stress response, said result of the measurement depicted as RS, RP and RH respectively; b. Estimate the value of one of these parameters by calculating it from the other two parameters; c. Predict the risk on basis of the deviation between calculated and measured value of the parameter that has been estimated in step b).