A method of monitoring a patient using a system includes a medical device, a peripheral device configured to wirelessly communicate with the medical device, and processing circuitry. The method includes, by the processing circuitry, receiving sensor data collected by the medical device and evaluating the sensor data. The method further includes, based on the evaluation of the sensor data, outputting for display via the peripheral device at least one question relating to the sensor data collected by the medical device for a patient to answer. The method further includes receiving at least one answer via the peripheral device and determining, based on a combination of the sensor data and the at least one answer, a risk-level of the patient's health associated with at least one condition such as at least one of infection, stroke, sepsis, chronic obstructive pulmonary disease, cardiac arrhythmia, or myocardial infarction.

A system (CV stenosis system) and method for noninvasive cardiac stenosis monitoring, diagnosis, analysis and reporting are disclosed. The CV stenosis system includes an in-ear biosensor system and a data analysis system. The in-ear biosensor system includes at least one earbud placed at or within an ear canal of an individual, where the at least one earbud includes one or more acoustic/vibration sensors that operate in both infrasonic and audible frequency ranges and detect biosignals from the individual. The data analysis system receives the biosignals from the biosensor system, separates the biosignals into components including infrasonic cardiac signals, and determines a type and level/severity of cardiovascular stenosis of the individual based upon the biosignals. In embodiments, the CV stenosis system can detect aortic stenosis and determine its severity, and detect stenoses of the left and right carotid arteries.

A sensing device for sensing perinatal maternal uterine activity and fetal heart activity is provided. The sensing device includes a body with an electromechanical system and a housing, a passive acoustic system with a plurality of microphones and an acoustic waveguide, an attachment component, an accelerometer, a signal analysis system including a microcontroller unit, and a wireless transceiver. The signal analysis system is configured to process biopotential signals and acoustic signals detected by the passive acoustic system and to reduce motion artifacts from the signals using the accelerometer.

A sensing device for sensing perinatal maternal uterine activity and fetal heart activity is provided. The sensing device includes a body with an electromechanical system and a housing, a passive acoustic system with a plurality of microphones and an acoustic waveguide, an attachment component, an accelerometer, a signal analysis system including a microcontroller unit, and a wireless transceiver. The signal analysis system is configured to process biopotential signals and acoustic signals detected by the passive acoustic system and to reduce motion artifacts from the signals using the accelerometer.

Systems and methods are disclosed herein, comprising a risk analysis module configured to determine a heart failure (HF) risk score for a subject using an S3 heart sound parameter of the subject and a control module configured to calculate a worsening heart failure (WHF) score for the subject using a HF parameter, wherein the control module is configured to enable a logistic regression detection of the WHF score if the determined HF risk score is in a first HF risk score range and to enable a neural network detection of the WHF score if the determined HF risk score is in a second HF risk score range.

Systems and methods are disclosed herein, comprising a risk analysis module configured to determine a heart failure (HF) risk score for a subject using an S3 heart sound parameter of the subject and a control module configured to calculate a worsening heart failure (WHF) score for the subject using a HF parameter, wherein the control module is configured to enable a logistic regression detection of the WHF score if the determined HF risk score is in a first HF risk score range and to enable a neural network detection of the WHF score if the determined HF risk score is in a second HF risk score range.

A system and method are provided for diagnosis of animal respiratory diseases using auscultation techniques. Animal lung sounds are recorded and digitized. Lung sounds are obtained by an electronic digital stethoscope or a wireless audio digital recording unit. The sounds are stored as digital data, and one or more algorithms are applied to the data for producing an output to the user indicative of the health of the animal. The acoustic characteristics of the sound are compared with baseline data in the algorithms. One embodiment includes a digital stethoscope with an integral display. Another embodiment provides a system for gathering information about an animal to include not only auscultation data, but also information from other field devices such as temperature probes or weigh scales. The combined information can be analyzed by system software to generate detailed information to a user to include a diagnosis and recommended treatment options.

Disclosed is a multi-channel digital stethoscopy system. The present invention can provide accurate and detailed medical examination information by separating and filtering stethoscopy sounds received from a plurality of transmission units by frequency in a single reception terminal, dividing the filtered stethoscopy sounds into cardiac sounds and lung sounds, and then outputting the same.

Devices and methods for providing sensory feedback during an exercise are disclosed. An exertion target is set, for a user performing the exercise, based on a self-calibration that estimates the user's ability using signal amplitudes of surface electromyography (sEMG) data, wherein the exertion target includes a target signal amplitude of muscle contractions to be reached during the exercise. sEMG data are received from a measurement device attached to the user as the user performs the exercise. Upon processing the sEMG data, sensory feedback is generated at a computing device operated by the user, wherein the sensory feedback has an intensity proportional to the user's exertion level as the user performs the exercise, and wherein the sensory feedback changes over a course of the exercise in dependence on a duration that the user maintains a muscle contraction at or above the target signal amplitude, and the change in sensory feedback is configured to encourage the user to prolong the duration.

An earpiece module includes a physiological sensor, an external energy sensor, a transceiver, a communication module, a data storage component, and a power source. The communication module includes a microphone, a speaker, and a signal processor. The signal processor processes audio information received from a remote source via the transceiver and communicates the processed audio information to a subject via the speaker. The signal processor processes information in real time from the physiological sensor and the external energy sensor, and the signal processor provides biofeedback to the subject based on signals produced by the physiological sensor. The data storage component includes a plurality of algorithms. At least one algorithm focuses processing resources on extracting physiological information from the physiological sensor, at least one algorithm is configured to be modified or uploaded wirelessly via the transceiver, and at least one algorithm is a compression/decompression (CODEC) algorithm.