A health sensor system and method can include a wearable non-invasive biosensor for capturing cardiac waveform signals such as electrocardiogram (ECG) signals and composite vibration objects over one or more channels, one or more processors operatively coupled to the wearable non-invasive biosensor, and memory having computer instructions which causes the system to perform certain operations. In some embodiments, the operations can include powering the health sensor system in response to receiving a radio frequency signal using a near field communication protocol, monitoring pulmonary artery pressures based on cardiac time intervals during a period when the health sensor system is powered by the radio frequency signal, performing a heart and lung function assessment based on the monitoring of the pulmonary artery pressures, and presenting the heart and lung function assessment. In some embodiments, the biosensor can be a single NFC patch biosensor.

A system for noninvasive extraction, identification, and marking of the heart valve signals to evaluate and monitor elevated left ventricular end-diastolic pressure (LVEDP) or pulmonary capillary wedge pressure (PCWP) using at rest assessment of hemodynamic performance, based on quantitative measurements of heart and lung related parameters and cardiac events for diagnostic and therapeutic purposes includes one or more signals from one or more noninvasive sensors or transducers that measure one or more physiological effects that are correlated with cardiopulmonary functions, transmission of the data to a computing device and analysis software where a trained algorithm processes the data to determine the state or condition of elevated LVEDP or PCWP and provides an output indicative of the state or condition of the analysis. The described noninvasive cardiopulmonary health assessment and monitoring systems and methods can provide effective at-home self-assessment or an integrated telehealth remote patient monitoring (RPM) system.

This disclosure relates generally to systems and methods for characterizing a behavior of an anatomical structure. Tracking data can be generated by a tracking system to represent at least a location of at least one sensor in a three-dimensional tracking coordinate system over time. A motion model is generated to characterize the behavior of the anatomical structure over a plurality of time instances. For instance, the motion model includes at least one free parameter and a temporal parameter. Each free parameter estimating geometry of the anatomical structure derived from the tracking data, and the temporal parameter indexes the free parameter over the plurality of time instances. A visualization is generated to provide a sequence of graphical images based on the motion model to characterize behavior of the anatomical structure over time.

Methods and systems are provided for obtaining cleaned sequences showing trajectories of movement of a center of gravity and for estimating a biometric information pattern or value of a target. One of the methods includes removing noises from initial sequences showing trajectories of movement of a center of gravity to obtain the cleaned sequences. Another one of the methods includes reading cleaned sequences of the target into a memory, extracting features from the cleaned sequences, and estimating a biometric information pattern or value of the target from the extracted features, using a classification or regression model of biometric information patterns or values. The biometric information pattern may be a pattern derived from respiratory or circulatory organs of a target.

A patient-worn ambulatory cardiac monitoring device for monitoring a patient during a patient activity includes at least one physiological sensor configured to detect signals indicative of cardiac activity, an activity sensor and associated circuitry configured to monitor patient movements, and a vibrational sensor configured to monitor a cardio-vibrational signal of the patient. The at least one physiological sensor can include one of an ECG sensor and a heart rate sensor. At least one processor in communication with the at least one physiological sensor, the activity sensor, and the vibrational sensor, is configured to measure, during the patient activity, at least one time interval between an ECG fiducial point in an ECG signal and a cardio-vibrational fiducial point in the cardio-vibrational signal during a cardiac cycle of the patient's heart.

The present invention provides a method and a portable non-invasive sub-THz and THz (THz) radar system for remotely detecting physiological parameters of a subject, comprising: one or more transmission means for transmitting THz signals to a subject predefined tissue; one or more reception means for receiving a THz signal of the subject, the THz signals being a reflection of the THz signal from subject tissue thereby, receiving at least one physiological parameter change; and microprocessor means coupled and configured to communicate with the transmitter means and/or the reception means for receiving and processing the reflected signals. The microprocessor comprising instructions of pre-treatment and folding the reflected signals; filtering and decimating selected portions of the folded signals and removing folded segments; decomposing of the decimated signal s into sub-component signals: identifying and removing sub-component signals due to random motions; locating quasi-periodic signal information from the remaining sub-component signals thereby, determining at least one physiological parameter of the subject based upon the quasi-periodic signal information components.

A method for determining one or more vital signs of a person includes recording video images of a scene with an egocentric camera coupled to the person's body, detecting and magnifying image frame-to-image frame movements in the video images of the scene, representing the magnified image frame-to-image frame movements in the video images of the scene by a one-dimensional (1D) amplitude-versus-time series, and transforming the 1D amplitude-versus-time series representation into a frequency spectrum. The method further includes identifying one or more local frequency maxima in the frequency spectrum as corresponding to one or more vital signs of the person.

A method and apparatus for monitoring vital signs, such as cardiopulmonary activity, using a ballistograph are provided. The method and apparatus may be used to monitor an infant sleeping in a crib, a patient in a hospital, a person with a chronic disease at home or in professional care, or a person in an elder-care setting.

A method for obtaining a cycle of a physiological signal includes: a collection device for collecting a vibration signal of body movements; a processor for obtaining a physiological signal by processing the vibration signal; receiving a physiological signal value and a register value, comparing the physiological signal value with the register value, and reserving one of the physiological signal value and the register value; determining the physiological signal value having a corresponding time duration, reaching a given set time to be an extreme value, wherein the time duration is a time duration of the physiological signal value received is not exceeded; restarting the procedure and determining a next extreme value; obtaining the cycle of the physiological signal by processing the at least one extreme value; and displaying the cycle of the physiological signal in a display device.

A vital signs monitoring system includes a peak pattern detection module configured to output a peak prediction signal from sensor signals based on a peak prediction algorithm; a vital sign estimating module configured to estimate a vital sign based on the peak prediction signal; an activity and context detector module configured to output a context signal based on at least one environmental condition and/or activity level of the person; and a concept drift detection module configured to output a drift signal based on drift detected in the estimated vital sign. The peak pattern prediction module is configured to update the peak prediction algorithm based on the context signal and the drift signal.