Methods and apparatus monitor health by detection of sleep stage. For example, a sleep stage monitor may access sensor data signals related to bodily movement and respiration movements. At least a portion of the detected signals may be analyzed to calculate respiration variability. The respiration variability may include variability of respiration rate or variability of respiration amplitude. A processor may then determine a sleep stage based on a combination bodily movement and respiration variability. The determination of sleep stages may distinguish between deep sleep and other stages of sleep, or may differentiate between deep sleep, light sleep and REM sleep. The bodily movement and respiration movement signals may be derived from one or more sensors, such as non-invasive sensor (e.g., a non-contact radio-frequency motion sensor or a pressure sensitive mattress).

In a configuration in which a holder holding a controller is mounted on a seat part with a plate-shaped member, the exposure of the mounting part of the plate-shaped member on which the holder is mounted is eliminated. A seat device includes a pressure sensor measuring a value relating to the seated person's state a vibration imparting device performing a vibration imparting operation, an ECU controlling the vibration imparting device corresponding to the measurement result of the pressure sensor, a holder holding the ECU, and a mounting bracket fixed to a lower frame such that the holder is mounted on the lower frame of a seat part. The mounting bracket includes a mounting projection on which a side wall of the holder is mounted in a predetermined mounting direction. When the side wall is mounted on the mounting projection, the mounting projection is covered with the side wall.

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.

An estimation device includes a first converting unit configured to convert a beat signal to a one-dimensional first candidate signal on the basis of a two-dimensional distribution of the beat signal, a second converting unit configured to convert the beat signal to a one-dimensional second candidate signal on the basis of a two-dimensional position change of the beat signal, and a signal deciding unit configured to decide a one-dimensional signal on the basis of the first candidate signal and the second candidate signal.

An estimation device includes a first converting unit configured to convert a beat signal to a one-dimensional first candidate signal on the basis of a two-dimensional distribution of the beat signal, a second converting unit configured to convert the beat signal to a one-dimensional second candidate signal on the basis of a two-dimensional position change of the beat signal, and a signal deciding unit configured to decide a one-dimensional signal on the basis of the first candidate signal and the second candidate signal.

A method is disclosed that includes removing an adhesive layer from a lower housing of a monitoring device; aligning the monitoring device with an opening of an adhesive applicator; coupling an uppermost adhesive layer that is positioned within the adhesive applicator with the lower housing; and removing the monitoring device from the opening of the adhesive applicator. A shape of the lower housing of the monitor corresponds to the shape of the opening of the adhesive applicator; and aligning the monitoring device with the opening of the adhesive applicator comprises matching the orientation of the lower housing to the orientation of the opening in the adhesive applicator. The method also includes the monitoring device selectively entering a battery preservation mode.

In a method and system, a medical imaging modality and the parameters to be deployed for the determined imaging modality are determined to produce an image of an examination object using the determined imaging modality and the determined parameters. Information from the preliminary examination(s) of the examination object can be automatically classified to generate classification results corresponding to interfering influence(s) resulting from the production of the image. The classification results can be analyzed to evaluate the classification results. The medical imaging modality and the parameter(s) is determined, based on the evaluated results, to minimize an influence of the interfering influences of the classification results in image(s) of the examination object generated using the determined medical imaging modality and the determined one or more parameters. The image(s) may then be generated using the determined medical imaging modality and the determined parameter(s).

In a method and system, a medical imaging modality and the parameters to be deployed for the determined imaging modality are determined to produce an image of an examination object using the determined imaging modality and the determined parameters. Information from the preliminary examination(s) of the examination object can be automatically classified to generate classification results corresponding to interfering influence(s) resulting from the production of the image. The classification results can be analyzed to evaluate the classification results. The medical imaging modality and the parameter(s) is determined, based on the evaluated results, to minimize an influence of the interfering influences of the classification results in image(s) of the examination object generated using the determined medical imaging modality and the determined one or more parameters. The image(s) may then be generated using the determined medical imaging modality and the determined parameter(s).

A method for reducing artifacts in magnetic resonance imaging (MRI) data includes acquiring a k-space dataset of an anatomical subject using a MRI scanner. An iterative compressed sensing reconstruction method is used to generate a reconstructed image based on the k-space dataset. This iterative compressed sensing reconstruction method uses (a) L1-norm based total variation constraints applied the temporal and spatial dimensions of the k-space dataset and (b) a low rank constraint. After the reconstructed image is generated, a deep learning network is used to generate an artifact image depicting motion artifacts present in the reconstructed image. The reconstructed image is subtracted from the artifact image to yield a final image with the motion artifacts removed.

Techniques for performing one or both of gesture recognition and biometric monitoring with an electronic device are disclosed, where the electronic device has a wireless communications capability using beamforming techniques and includes a plurality of millimeter wave antenna modules. Each module includes at least one transmit antenna and at least one receive antenna, operable in one or more frequency ranges not less than 20 GHz, the receive antenna coupled with a first branch configured to receive H-polarized signals and a second branch configured to receive V-polarized signals. Performing one or both of gesture recognition and biometric monitoring includes detecting a presence and motion of a reflective object or anatomical feature by determining a relationship between received H-polarized signals and received V-polarized signals for two or more receive antennas as a function of time.