A system and method for prompting a patient experiencing ventilatory depression to breathe includes at least one sensor for detecting ventilatory depression by detecting inadequate breathing or lack of breathing in the patient. The system also includes one or more sensors for determining the type of breathing problem experienced by the patient. A sensor for detecting motion of the patient is used to determine whether the patient is moving. If inadequate or a lack of breathing is detected and the patient is not moving, the system provides verbal prompts or tactile stimuli to prompt the patient to breathe to improve patient ventilation.

Devices and methods for predicting, identifying and/or managing pneumonia are disclosed and may generally comprise a housing, a mouthpiece in communication with the housing and configured for insertion within a mouth, a temperature sensor positioned within or along the housing, a pulse oximeter sensor positioned along the housing for sensing a heart rate or blood oxygen level, a microphone positioned within or along the housing for obtaining sounds associated with respiration, and a controller configured to receive physiologic parameters including the temperature, heart rate, blood oxygen level, and sounds associated with respiration. The controller can determine a likelihood of contracting pneumonia, a presence of pneumonia, or a status of pneumonia within the user based on deviations from a threshold value which are present in at least two of the physiologic parameters.

Provided is a cough recognition method and device, which can detect cough sounds from an audio signal, and not only can detect coughs but also can track the location at which the cough sounds are generated by calculating the location of a sound source.

This disclosure presents systems and methods to provide telehealth and medical IoT communication. Exemplary implementations may: obtain health information characterizing physiological state of a user; obtain activity information characterizing physical activity of the user; identify potential occurrences of one or more medical-related events based on the health information and the activity information; in response to identifying the potential occurrences of the one or more medical-related events, generate and deliver one or more notifications to one or more computing platforms; and/or perform other features and/or functionality.

A smart face mask comprising a mask body; a temperature sensor; a respiration sensor; and a transmitter for transmitting information from the temperature sensor, the respiration sensor and a geotracker to a smart phone or smart watch.

Introduced here are computer programs and associated computer-implemented techniques for deriving insights into the health of patients through analysis of audio data generated by electronic stethoscope systems. A diagnostic platform may be responsible for examining the audio data generated by an electronic stethoscope system so as to gain insights into the health of a patient. The diagnostic platform may employ heuristics, algorithms, or models that rely on machine learning or artificial intelligence to perform auscultation in a manner that significantly outperforms traditional approaches that rely on visual analysis by a healthcare professional.

A method for counting coughs is provided. The method includes acquiring a plurality of onset signals from the sound signal, wherein the onset signal has a predetermined time length; acquiring a plurality of spectrograms corresponding to each of the plurality of onset signals; determining whether each of the acquired plurality of spectrograms represents a cough using a cough determination model; and calculating a number of coughs included in the sound signal based on a time point of a cough signal. The cough signal is an onset signal corresponding to one spectrogram determined to represent the cough. When a time interval between a first time point of a first cough signal and a second time point of a second cough is within a reference time interval, the first cough signal and the second cough signal are regarded as one cough signal at the first time point.

Provided is a biological examination apparatus and a biological information analysis method capable of quickly grasping a two-dimensional motions of the up-down and front-back directions of the thyroid cartilage and the lingual bone accompanied by a swallowing sound as swallowing dynamics by a non-invasive examination. In the biological examination apparatus of the present invention, an up-down motion component associated with an up-down motion of the thyroid cartilage and a front-back motion component associated with a front-back motion of the thyroid cartilage are extracted from a fitting result obtained by fitting a model function modeling a swallowing motion to distance information based on detection data detected by a larynx portion displacement detector, and a two-dimensional trajectory data indicating behavior trajectories of an up-down direction and a front-back direction of the thyroid cartilage is generated based on the extracted up-down motion component and the extracted front-back motion component.

Method and processor for time-domain processing of a waveform signal are disclosed. The method includes filtering, by employing one or more cut-off frequency values, the waveform signal for generating the first portion and the second portion, acquiring a frequency shift value, generating a modulated signal having a first frequency portion and a second frequency portion and where the one or more cut-off frequency values have been determined for ensuring that the first frequency portion and the second frequency portion are non-overlapping portions of the modulated signal, and generating the modified signal using the first frequency portion.

An automatic diagnostic apparatus and corresponding method is disclosed for recognizing heart sounds of interest, i.e., murmurs, detected in streaming audio data picked up by a stethoscope. Sensors included in the device capture audio data in real time during an auscultation exam performed by a physician. A feature vector that models the stream of audio data is created and supplied to a deep neural network stored on the diagnostic device. The deep neural network generates a probability for each of the heart sounds of interest. When the probability of detection exceeds a pre-established threshold value the device alerts the physician through visual and/or audio cues, enhancing the physician's diagnostic capability during routine examination.