A method of estimating a number of lung function indices of an individual. The method includes: transmitting an ultrasound signal toward a chest of the individual from a speaker of a smartphone while the individual is holding the smartphone in a hand of the individual; receiving in a microphone of the smartphone a reflected signal reflected from the chest of the individual in response to the ultrasound signal; extracting a number of features from the reflected signal; and providing the number of features to a neural network regression model, wherein the neural network regression model estimates the number of lung function indices based on the number of features and based on a non-linear correlation between chest wall motion and human lung function.

The disclosure is directed to a sensing device, configured to be installed in a bedding, for monitoring a user's sleep, the device comprising: a sensing part, for acquiring/determining a value representative of an a force or pressure and/or a value representative of a variation of a force or pressure, a housing comprising at least a pressure transducer and an electronic processing unit, a microphone connected to the electronic processing unit, wherein the electronic processing unit is configured to process first and second electrical signals delivered respectively by the microphone and the pressure converter, wherein the electronic processing unit is either configured to deduce locally at least a breathing disturbance therefrom or configured to send data representative of the first and second electrical signals to a remote device.

A user-wearable sensor device may be configured to be directly or indirectly secured to a user or to an article worn by the user. The user-wearable sensor device may include at least one sensor configured to collect sensor data associated with an orientation of the user, a display unit including at least one LED or other visual indicator, a battery configured to provide power to at least the display unit, and a control system. The control system may be configured to determine the orientation of the user based on sensor data collected by the at least one sensor, maintain the display unit in a deactivated state in the absence of a defined activation input, detect a defined activation input, activate the deactivated display unit in response to detecting the defined activation input, and control the activated display unit based on the determined orientation of the user.

A system and method for classifying the phonocardiogram (PCG) signal quality has been described. The system is configured to identify the quality of the PCG signal recording and accepting only diagnosable quality recordings for further cardiac analysis. The system includes the derivation of plurality features of the PCG signal from the training dataset. The extracted features are preprocessed and are then ranked using mRMR algorithm. Based on the ranking the irrelevant and redundant features are rejected if their mRMR strength is less. A training model is generated using the relevant set of features. The PCG signal of the person under test is captured using a digital stethoscope and a smartphone. The PCG signal is preprocessed and only the relevant set of features are extracted. And finally the PCG signal is classified into diagnosable or non-diagnosable using the relevant set of features and a random forest classifier.

A system and method for classifying the phonocardiogram (PCG) signal quality has been described. The system is configured to identify the quality of the PCG signal recording and accepting only diagnosable quality recordings for further cardiac analysis. The system includes the derivation of plurality features of the PCG signal from the training dataset. The extracted features are preprocessed and are then ranked using mRMR algorithm. Based on the ranking the irrelevant and redundant features are rejected if their mRMR strength is less. A training model is generated using the relevant set of features. The PCG signal of the person under test is captured using a digital stethoscope and a smartphone. The PCG signal is preprocessed and only the relevant set of features are extracted. And finally the PCG signal is classified into diagnosable or non-diagnosable using the relevant set of features and a random forest classifier.

Described herein are systems and methods for favorably coordinating a timing relationship between a musculoskeletal activity cycle and a cardiac cycle of a user. A method may include repetitively detecting a signal that correlates to a blood volume in the user; determining an actual value of the signal that varies with the timing relationship; computing a trend of the actual value of the signal; and adjusting the movement guidance based on the trend of the actual value. A system may include a prompt device configured to provide recurrently a movement guidance to the user for guiding performance of the rhythmic musculoskeletal activity; a sensor configured to provide a signal that correlates to a blood volume in the user; and a processor configured to determine an actual value of the signal that varies with the timing relationship and to adjust the movement guidance based on the trend of the actual value.

Described herein are systems and methods for favorably coordinating a timing relationship between a musculoskeletal activity cycle and a cardiac cycle of a user. A method may include repetitively detecting a signal that correlates to a blood volume in the user; determining an actual value of the signal that varies with the timing relationship; computing a trend of the actual value of the signal; and adjusting the movement guidance based on the trend of the actual value. A system may include a prompt device configured to provide recurrently a movement guidance to the user for guiding performance of the rhythmic musculoskeletal activity; a sensor configured to provide a signal that correlates to a blood volume in the user; and a processor configured to determine an actual value of the signal that varies with the timing relationship and to adjust the movement guidance based on the trend of the actual value.

A method and apparatus for detecting a respiratory function are provided. The method includes training a plurality of classification models, receiving a breathing sound by a sound receiver to generate a breathing signal, and classifying the breathing signal by each of the trained classification models to obtain a classification result corresponding to each of the classification models.

A swallowing action measurement device includes a holder, a sound detector and a posture detector. The holder is fitted to a neck region of a person being measured from behind. The sound detector is held in the holder in contact with the outer side surface of the neck region close to the epiglottis, and detects sound associated with at least a swallowing action of the person being measured and outputs a measured sound signal. The posture detector detects a posture of the person being measured.

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.