The invention relates to an interventional system comprising an interventional device (11) for being inserted into a living being (7) and comprising an optical shape sensing fiber, wherein the optical shape sensing fiber is used for determining respiratory motion by monitoring a movement of a part of the interventional device, which moves in accordance with the respiratory motion. Thus, respiratory motion can be determined, without necessarily requiring a physician to handle with further devices like a respiratory belt, i.e. the same interventional device can be used for performing an interventional procedure and for determining the respiratory motion. The interventional procedure can therefore be less cumbersome for a physician. Moreover, since the determination of the respiratory motion is based on optical shape sensing, which is a very accurate position determination technique, the respiratory motion can be determined very accurately.

The invention relates to an interventional system comprising an interventional device (11) for being inserted into a living being (7) and comprising an optical shape sensing fiber, wherein the optical shape sensing fiber is used for determining respiratory motion by monitoring a movement of a part of the interventional device, which moves in accordance with the respiratory motion. Thus, respiratory motion can be determined, without necessarily requiring a physician to handle with further devices like a respiratory belt, i.e. the same interventional device can be used for performing an interventional procedure and for determining the respiratory motion. The interventional procedure can therefore be less cumbersome for a physician. Moreover, since the determination of the respiratory motion is based on optical shape sensing, which is a very accurate position determination technique, the respiratory motion can be determined very accurately.

In order to help reduce the effects of motion sickness, there is provided a method for reducing motion sickness in a subject which comprises acquiring a sequence of video images, extracting measurements of a heart-rate of the subject over a first period of time from the sequence of video images using photoplethysmography (PPG), calculating at least one trend in the measurements, determining a presence of motion sickness when the at least one trend is positive over a first time window, the first time window being included in the first period of time, and generating an event arranged to generate a corrective action. It is often possible to detect the onset of motion sickness before the subject actually feels the symptoms. Indeed, by the time the symptoms appear, corrective action is much less effective. Therefore, by detecting the onset early and alerting the subject so that they can react, it is possible to avoid the attack of motion sickness or, at least, reduce significantly its effects.

Methods and apparatus monitor health by detection of sleep stage. For example, a sleep stage monitor (100) may access sensor data signals related to bodily movement and/or respiration movements. At least a portion of the detected signals may be analyzed to calculate respiration variability. The respiration variability may include one or more of variability of respiration rate and variability of respiration amplitude. A processor may then determine a sleep stage based on one or more of respiration variability and bodily movement, such as with a combination of both. 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).

Methods and apparatus monitor health by detection of sleep stage. For example, a sleep stage monitor (100) may access sensor data signals related to bodily movement and/or respiration movements. At least a portion of the detected signals may be analyzed to calculate respiration variability. The respiration variability may include one or more of variability of respiration rate and variability of respiration amplitude. A processor may then determine a sleep stage based on one or more of respiration variability and bodily movement, such as with a combination of both. 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).

A medical image processing method performed by a computer, for measuring the spatial location of a point on the surface of a patient's body including: acquiring at least two two-dimensional image datasets, wherein each two-dimensional image dataset represents a two-dimensional image of at least a part of the surface which comprises the point, and wherein the two-dimensional images are taken from different and known viewing directions; determining the pixels in the two-dimensional image datasets which show the point on the surface of the body; and calculating the spatial location of the point from the locations of the determined pixels in the two-dimensional image datasets and the viewing directions of the two-dimensional images; wherein the two-dimensional images are thermographic images.

A medical image processing method performed by a computer, for measuring the spatial location of a point on the surface of a patient's body including: acquiring at least two two-dimensional image datasets, wherein each two-dimensional image dataset represents a two-dimensional image of at least a part of the surface which comprises the point, and wherein the two-dimensional images are taken from different and known viewing directions; determining the pixels in the two-dimensional image datasets which show the point on the surface of the body; and calculating the spatial location of the point from the locations of the determined pixels in the two-dimensional image datasets and the viewing directions of the two-dimensional images; wherein the two-dimensional images are thermographic images.

A device of the disclosure determines to which of a plurality of sleep stages in a sleep state including a wake stage a sleep state of a subject belongs, based on a sleep state function that is calculated using as variables a respiratory motion feature and a body motion feature that are respectively extracted from respiratory motion index values and body motion index values of the subject measured in time series. The sleep state function is a function including coefficient parameters that are set based on learning data including sleep stage determination results by a measurement of a sleep state for calibration, and respiratory motion features and body motion features that are measured simultaneously with the measurement for calibration.

A device of the disclosure determines to which of a plurality of sleep stages in a sleep state including a wake stage a sleep state of a subject belongs, based on a sleep state function that is calculated using as variables a respiratory motion feature and a body motion feature that are respectively extracted from respiratory motion index values and body motion index values of the subject measured in time series. The sleep state function is a function including coefficient parameters that are set based on learning data including sleep stage determination results by a measurement of a sleep state for calibration, and respiratory motion features and body motion features that are measured simultaneously with the measurement for calibration.

A method and system for indicating a breathing pattern is disclosed. The system comprises a plurality of smart wearable devices, a plurality of mobile devices, an application server and a database. The plurality of smart wearable devices is either connected to the mobile devices or directly connected to the application server. The database is connected to the application server to store the data transmitted by the smart wearable device or the mobile device. The plurality of smart wearable devices is capable of monitoring the health index and identifying the variation in health conditions of the user, in real-time and activating a suitable breathing pattern to normalize such medical condition.