Abstract: A system for modulating operation of an organ in real time by controlling illumination of one or more light components is provided. The system includes an external device comprising a processing unit and a power supply configured to transmit stimulation parameters, a wireless implantable device comprising, a sensor configured to detect, in real time, activity data from a tissue cluster of an organ, a stimulator including a plurality of light components corresponding to at least a first wavelength and a second wavelength and a flexible elastomer coupled to the plurality of light components, and a transceiver configured to transmit the activity data to the external device, wherein the stimulator is configured to illuminate, based on the stimulation parameters, one of the plurality of light components coupled to the flexible elastomer, wherein the processing unit is configured to update the stimulation parameters based on the activity data.

Abstract: A system and a method for predicting kinesthetic outcomes from observed position, posture, behavior or activity of an individual 1602, 1702. The system uses kinesthetic activity sensors 102, 104 each collecting one or more of audio, video, or physiological signals and capturing the activity of the individual or an ambient environment of the individual. These signals are delivered into a computer system 106 implementing a learning routine 108 which constructs one or more personalized kinetic state models 1510 of positional states for the individual and transitions between the positional states, and further develops one or more customized multi-dimensional prediction models 1500 for the individual and uses the multidimensional prediction models to predict behaviors, activities and/or positional changes likely to occur in the future, and provides notice of predicted unsafe or undesired outcomes.

Abstract: 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 treatin

Abstract: One or more time-varying signals from continuous monitoring of an individual or the individual's environment are processed in a non-linear fashion to develop signatures for those signals to assess the individual's health. Qualitative data such as individual, family, and/or health care provider reporting of activity or status, and lab data, may also be used. The system may compute an integrated likelihood of the individual experiencing an illness or condition, which is provided to the individual and/or a training or health-care provider for the individual, and updated on a time schedule, giving pre-symptomatic notice of illnesses and early identification of conditions. The system may also optimize a course of performance training and diet. Further, by incorporating treatment data, the invention may be used in forming a quality measure of the individual's care, health, function, risk of adverse or undesired event, and efficacy or lack thereof of medical treatments or other necessary interventions.

Abstract: In accordance with an aspect of the present invention, a system and method allows for the assessment of health and mortality based on dynamic nonlinear calculations of self-similar fluctuation patterns in a time series of QT intervals and of other physiological signals, such as RR intervals, temperature, blood pressure, respiration, saturation of peripheral oxygen, intracardiac pressures, and electroencephalogram. In order to nonlinearly determine health and mortality, time series of QT intervals and of other physiological signals (e.g., RR intervals) are simultaneously obtained, and entropy values are calculated for each signal over the same temporal interval. “EntropyX” is calculated from relative changes between moments and entropy of QT intervals and those of other physiological signals over seconds to days. The absolute and relative entropy values at a specific time point and/or subsequent changes in entropy over future time points can be used to determine a treatment plan for the subject.

Abstract: A system and a method for predicting kinesthetic outcomes from observed position, posture, behavior or activity of an individual 1602, 1702. The system uses kinesthetic activity sensors 102, 104 each collecting one or more of audio, video, or physiological signals and capturing the activity of the individual or an ambient environment of the individual. These signals are delivered into a computer system 106 implementing a learning routine 108 which constructs one or more personalized kinetic state models 1510 of positional states for the individual and transitions between the positional states, and further develops one or more customized multi-dimensional prediction models 1500 for the individual and uses the multidimensional prediction models to predict behaviors, activities and/or positional changes likely to occur in the future, and provides notice of predicted unsafe or undesired outcomes.

Abstract: In accordance with an aspect of the present invention, a system and method allows for the assessment of health and mortality based on dynamic nonlinear calculations of self-similar fluctuation patterns in a time series of QT intervals and of other physiological signals, such as RR intervals, temperature, blood pressure, respiration, saturation of peripheral oxygen, intracardiac pressures, and electroencephalogram. In order to nonlinearly determine health and mortality, time series of QT intervals and of other physiological signals (e.g., RR intervals) are simultaneously obtained, and entropy values are calculated for each signal over the same temporal interval. “EntropyX” is calculated from relative changes between moments and entropy of QT intervals and those of other physiological signals over seconds to days. The absolute and relative entropy values at a specific time point and/or subsequent changes in entropy over future time points can be used to determine a treatment plan for the subject.

Abstract: A method of determining health and mortality includes obtaining a ventricular activation (RR) time series from a subject for multiple temporal intervals. The method also includes calculating a cardiac entropy in the RR time series over the temporal intervals using coefficient of sample entropy (COSEn). Additionally, the method includes comparing the cardiac entropy between the intervals to determine health and mortality. The absolute and relative changes in entropy over a patient's follow up period provide dynamic information regarding health and mortality risk. The determination of health and mortality can then be used to create a treatment plan for the subject.

Abstract: A method of determining health and mortality includes obtaining a ventricular activation (RR) time series from a subject for multiple temporal intervals. The method also includes calculating a cardiac entropy in the RR time series over the temporal intervals using coefficient of sample entropy (COSEn). Additionally, the method includes comparing the cardiac entropy between the intervals to determine health and mortality. The absolute and relative changes in entropy over a patient's follow up period provide dynamic information regarding health and mortality risk. The determination of health and mortality can then be used to create a treatment plan for the subject.