A method for adjusting an output signal of a pulse diagnosis device includes obtaining a pulse signal at a pulse diagnosis region that is acquired by a sensing element of the pulse diagnosis device, and performing a parameter recognition on the pulse signal to obtain a parameter of the pulse signal, and determining a biological structure of the pulse diagnosis region based on the parameter of the pulse signal. The method also includes determining an adjustment factor for the parameter of the pulse signal based on the biological structure, and adjusting the parameter of the pulse signal based on the adjustment factor for the parameter of the pulse signal, to obtain an adjusted pulse signal for the pulse diagnosis region. The pulse signal is a pressure signal applied by an artery in the pulse diagnosis region to an external skin surface corresponding to the artery.

Provided are a method and an apparatus for measuring an endolymphatic hydrops ratio of inner ear organs using an artificial neural network. The method of measuring an endolymphatic hydrops ratio includes obtaining a plurality of frame images obtained by capturing inner ear organs, obtaining a plurality of pieces of mask data corresponding to each of the plurality of frame images by inputting the plurality of frame images into a neural network, clustering the plurality of pieces of mask data according to the inner ear organs and obtaining representative images according to the inner ear organs according to certain conditions, and overlapping a target image synthesized by using the plurality of frame images and the representative images according to the inner ear organs so as to measure an endolymphatic hydrops ratio.

A system to detect a position of a cannula may include a cannula, which may include a distal tip and an inner lumen. Also, the system may include an optical fiber, which may be disposed within the inner lumen of the cannula and may include a first end, a second end, and a U-shaped portion disposed between the first end and the second end. The U-shaped portion may be at least proximate the distal tip. Further, the system may include a light emitter, which may be coupled with the first end of the optical fiber, and a light receiver, which may be coupled with the second end of the optical fiber. Moreover, the system may include an electronic processor. The electronic processor may be coupled with the light receiver and configured to detect a decrease in an intensity of light received at the light receiver.

Disease prediction using analyte measurements and machine learning is described. In one or more implementations, a combination of features of analyte measurements may be selected from a plurality of features of the analyte measurements based on a robustness metric and a performance metric of the combination, and a machine learning model may be trained to predict a health condition classification using the combination. The performance metric may be associated with an accuracy of predicting the health condition classification, and the robustness metric may be associated with an insensitivity to analyte sensor manufacturing variabilities on the accuracy. Once trained, the machine learning model predicts the health condition classification for a user based on analyte measurements of the user collected by a wearable analyte monitoring device. The combination of features may be extracted from the analyte measurements of the user and input into the machine learning model to predict the classification.

The present invention relates to the use of an aza-BODIPY fluorophore compound as a contrast agent in the optical window ranging from 1000 to 1700 nm. The invention also relates to the use, as a contrast agent, of a composition comprising said fluorophore compound and a pharmaceutically acceptable excipient and/or a solvent, in a kit comprising an injection system and said fluorophore or said composition, and also to a method for identifying a biological target (such as a healthy or tumour cell, a protein, DNA, RNA, for example).

A method for obtaining a cycle of a physiological signal includes: a collection device for collecting a vibration signal of body movements; a processor for obtaining a physiological signal by processing the vibration signal; receiving a physiological signal value and a register value, comparing the physiological signal value with the register value, and reserving one of the physiological signal value and the register value; determining the physiological signal value having a corresponding time duration, reaching a given set time to be an extreme value, wherein the time duration is a time duration of the physiological signal value received is not exceeded; restarting the procedure and determining a next extreme value; obtaining the cycle of the physiological signal by processing the at least one extreme value; and displaying the cycle of the physiological signal in a display device.

Arrangements, apparatus, systems and systems are provided for obtaining data for at least one portion within at least one luminal or hollow sample. The arrangement, system or apparatus can be (insertable via at least one of a mouth or a nose of a patient. For example, a first optical arrangement can be configured to transceive at least one electromagnetic (e.g., visible) radiation to and from the portion. A second arrangement may be provided at least partially enclosing the first arrangement. Further, a third arrangement can be configured to be actuated so as to position the first arrangement at a predetermined location within the luminal or hollow sample. The first arrangement may be configured to compensate for at least one aberration (e.g., astigmatism) caused by the second arrangement and/or the third arrangement. The second arrangement can include at least one portion which enables a guiding arrangement to be inserted there through. Another arrangement can be provided which is configured to measure a pressure within the at least one portion. The data may include a position and/or an orientation of the first arrangement with respect to the luminal or hollow sample.

Certain aspects relate to apparatuses and techniques for non-invasive optical imaging that acquires a plurality of images corresponding to both different times and different frequencies. Additionally, alternatives described herein are used with a variety of tissue classification applications, including assessing the presence and severity of tissue conditions, such as burns and other wounds.

There is provided a system and methods for quantitatively assessing fetal well-being based on observing fetal movement activity in an audio/visual data feed. The system includes a method that detects and quantifies fetal movements in an audio/visual data feed by audio/visual-processing motion estimation techniques. The system also includes a method that captures metrics relating to fetal movements sensed by the mother or other independent party, whereby termed “maternal perception”. The system further includes a method that cross validates the fetal movement detected by the system with fetal movement sensed by “maternal perception”. The system further includes a method that generates output to summarize fetal movement activity over a recorded time period. This output may be reviewed by a third party to assist in determining if further intervention is needed.

Training at the proper level of effort is important for athletes whose objective is to achieve the best results in the least time. In running, for example, pace is often monitored. However, pace alone does not reveal specific issues with regard to running form, efficiency, or technique, much less inform how training should be modified to improve performance or fitness. A sensing system and wearable sensor platform described herein provide real-time feedback to a user/wearer of his power expenditure during an activity. In one example, the system includes an inertial measurement unit (IMU) for acquiring multi-axis motion data at a first sampling rate, and an orientation sensor to acquire orientation data at a second sampling rate that is varied based on the multi-axis motion data.