Embodiments include a system for measuring respiratory function of a user. The system can include an optical sensing unit configured to detect movement of a torso of the user. The system can include an electronic device configured to provide a first request for the user to breathe at a first rate during a first time period and a second request for the user to breathe at a second rate during a second time period. The system can include a processing unit configured to determine a first respiration parameter based on the movement of the torso during the first time period and determine a second respiration parameter based on the movement of the torso during the second time period. The processing unit can determine a level of respiratory function based on the first respiration parameter and the second respiration parameter.

Health signal monitoring using continuous wave microwave signals is often affected by phase wrapping and null point detection issues. The disclosure herein generally relates to health monitoring, and, more particularly, to a method and a monitoring system for health monitoring using Amplitude Modulated Continuous Wave (AMCW) microwave signals. In this design of the monitoring system, the AMCW microwave signal comprises of a carrier signal and a modulating signal. The modulating signal is used for measuring heart rate and breathing rate of a subject, while the carrier signal is used to tune antenna size in the monitoring system. As the probing wavelength and the antenna size are independent of each other in this design of the monitoring system, the probing wavelength can be adjusted such that effect of the phase wrapping can be minimized. The system addresses the null point measurement problem by quadrature modulating the modulating signal.

A breathing-driven flexible respiratory sensor includes: a test cavity and a digital electrometer, wherein an upper internal wall of the test cavity is provided with an upper detecting component, and a lower internal wall of the test cavity is provided with a lower detecting component; the upper detecting component and the lower detecting component is arranged in a longitudinal symmetry form; wherein the upper detecting component comprises a substrate, an electrode and a gas sensitive film bonded in sequence from top to bottom, and the substrate is bonded to the upper internal wall of the test cavity; wherein a rubber airbag is disposed in the test cavity, and a friction film is bonded to the rubber airbag; an air inlet cylinder is connected to a left end of the rubber airbag, and an air outlet cylinder is connected to a right end of the rubber airbag.

A respiration monitoring system has deformation transducers on a flexible substrate arranged to adhere to a patient's torso. A processor receives signals in channels from the transducers and processes them to eliminate, reduce or compensate for noise arising from patient motion artefacts, to provide an output representative of respiration. The transducers have a size and a mutual location on the substrate so that a first transducer can overlie at least part of the 10.sup.th rib and a second transducer can overlie at least part of the 11.sup.th rib or the abdomen, and the processor processes data from the first transducer as being primarily representative of rib distending respiration and from the second transducer as being primarily representative of either diaphragm respiration or patient motion artefacts.

A respiration monitoring system has deformation transducers on a flexible substrate arranged to adhere to a patient's torso. A processor receives signals in channels from the transducers and processes them to eliminate, reduce or compensate for noise arising from patient motion artefacts, to provide an output representative of respiration. The transducers have a size and a mutual location on the substrate so that a first transducer can overlie at least part of the 10.sup.th rib and a second transducer can overlie at least part of the 11.sup.th rib or the abdomen, and the processor processes data from the first transducer as being primarily representative of rib distending respiration and from the second transducer as being primarily representative of either diaphragm respiration or patient motion artefacts.

Methods and implantable medical devices (IMDs) are provided for monitoring a cardiac function of a heart. A heart sound sensor is configured to sense heart sound signals of the subject. The IMD includes a memory to store program instructions. The IMD includes a processor that, when executing the program instructions, is configured to identify S2 signal segment from the heart sound signals, analyze the S2 signal segment to identify a pulmonary valve signal (P2 signal) and an aortic valve signal (A2 signal) within an S2 signal segment of the heart sound signals. The processor is configured to determine a time interval between the A2 and P2 signals, characterize the S2 signal segment to exhibit a first type of S2 split based on the time interval, and identify a cardiac condition based on a comparison of the first type of S2 split and a cardiac condition matrix.

Methods and implantable medical devices (IMDs) are provided for monitoring a cardiac function of a heart. A heart sound sensor is configured to sense heart sound signals of the subject. The IMD includes a memory to store program instructions. The IMD includes a processor that, when executing the program instructions, is configured to identify S2 signal segment from the heart sound signals, analyze the S2 signal segment to identify a pulmonary valve signal (P2 signal) and an aortic valve signal (A2 signal) within an S2 signal segment of the heart sound signals. The processor is configured to determine a time interval between the A2 and P2 signals, characterize the S2 signal segment to exhibit a first type of S2 split based on the time interval, and identify a cardiac condition based on a comparison of the first type of S2 split and a cardiac condition matrix.

A process for analyzing multidimensional vector data of a motion sensor for detecting a breathing motion, includes receiving and storing the multidimensional vector data of the motion sensor in a time series, calculating a plurality of medium-term vectors and of a plurality of long-term average vectors, calculating and storing a plurality of mean-free vectors depending on a difference between a respective medium-term vector and a respective long-term average vector and determining a plurality of unit vectors. The respective unit vector is oriented in a random direction. A plurality of scalar products are calculated from a respective mean-free vector and the unit vector assigned to the mean-free vector. A motion identification is calculated, which is an indicator of the breathing motion, based on the plurality of scalar products. An analysis signal is determined and output based on a comparison between the motion identification and a predefined motion threshold value.

A process for analyzing multidimensional vector data of a motion sensor for detecting a breathing motion, includes receiving and storing the multidimensional vector data of the motion sensor in a time series, calculating a plurality of medium-term vectors and of a plurality of long-term average vectors, calculating and storing a plurality of mean-free vectors depending on a difference between a respective medium-term vector and a respective long-term average vector and determining a plurality of unit vectors. The respective unit vector is oriented in a random direction. A plurality of scalar products are calculated from a respective mean-free vector and the unit vector assigned to the mean-free vector. A motion identification is calculated, which is an indicator of the breathing motion, based on the plurality of scalar products. An analysis signal is determined and output based on a comparison between the motion identification and a predefined motion threshold value.

A breathing detection apparatus has a first camera (12), a second camera (14) and a processing device (16). The processing device (16) is configured to perform breathing detection analysis using data relating to respective images captured by the first and second cameras (12, 14) to detect breathing characteristics of a person shown in the images.