METHOD, APPARATUS AND DEVICE FOR OBTAINING BLOOD GLUCOSE MEASUREMENT RESULT

    公开(公告)号:US20220338764A1

    公开(公告)日:2022-10-27

    申请号:US17763658

    申请日:2021-05-21

    Abstract: A method, apparatus and device for obtaining a blood glucose measurement result. A neural network model is trained by using the following method, so as to obtain a trained first neural network model: acquiring a first invasive blood glucose measurement result of a tested object (101); forming a group of new training data by means of same and characteristic values of the most recent PPG signals of the tested object (102); training the neural network model with the training data, so as to obtain a trained first neural network model (106); and after a group of new PPG signals is acquired, extracting characteristic values of the new PPG signals, and inputting the characteristic values into the trained first neural network model, so as to obtain a target blood glucose measurement result (107).

    BLOOD PRESSURE MEASUREMENT METHOD, DEVICE AND STORAGE MEDIUM

    公开(公告)号:US20200015688A1

    公开(公告)日:2020-01-16

    申请号:US16507441

    申请日:2019-07-10

    Abstract: A blood pressure measurement method, a blood pressure measurement device, and a storage medium are provided. The method includes: acquiring a video of a part of a human body, and generating a PPGi signal based on the video; extracting feature information from the PPGi signal; fitting blood pressure models based on the feature information, and acquiring blood pressure data corresponding to each heartbeat, wherein the blood pressure models include a systolic blood pressure linear model and a diastolic blood pressure exponential model.

    USER FEATURE VALUE MEASUREMENT METHOD AND APPARATUS, STORAGE MEDIUM AND ELECTRONIC DEVICE

    公开(公告)号:US20240298973A1

    公开(公告)日:2024-09-12

    申请号:US17768495

    申请日:2021-05-20

    CPC classification number: A61B5/7267 A61B5/7246 G16H50/70 A61B5/14532

    Abstract: A user feature value measurement method includes: generating training data of at least one user that by acquiring standard feature values and measurement feature data input multiple times by the user, wherein the training data of each user comprises a standard feature value set and a measurement feature data set; training a data calculation model of each user based on the training data; acquiring current feature data of a target user, and determining a data calculation model of the target user according to the current feature data and the measurement feature data set; and calculating a user feature value of the target user based on the current feature data and the data calculation model of the target user.

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