NONINVASIVE BLOOD PRESSURE MEASUREMENT METHOD AND DEVICE

    公开(公告)号:US20190298188A1

    公开(公告)日:2019-10-03

    申请号:US16010289

    申请日:2018-06-15

    Abstract: A method for estimating blood pressure using a blood flow occlusion system applied to an artery includes receiving from a first sensor a sensed signal; processing at a processor the sensed signal to detect beats in a pulsatile signal; determining validity of the detected beats; storing the detected beats and data associated with the detected beats in the sensed signal as the pressure applied to the artery by the blood flow occlusion system deflates towards a level below a nominal level; determining baseline beat characteristics; evaluating the stored beats and associated data to detect change in beat characteristics as compared to the baseline beat characteristics; selecting a beat before the detected change in the beat characteristic as the last beat indicating the onset of the diastolic blood pressure for the artery; determining a value of the applied pressure at the last beat as the diastolic blood pressure for the artery.

    ABSOLUTE DEPTH ESTIMATION FROM A SINGLE IMAGE USING ONLINE DEPTH SCALE TRANSFER

    公开(公告)号:US20240303838A1

    公开(公告)日:2024-09-12

    申请号:US18180643

    申请日:2023-03-08

    Abstract: The present disclosure provides architectures and techniques for absolute depth estimation from a single (e.g., monocular) image, using online depth scale transfer. For instance, estimation of up-to-scale depth maps from monocular images may be decoupled from estimation of the depth scale (e.g., such that additional online measurements, additional calibrations, etc. are not required). One or more aspects of the present disclosure include fine-tuning or training from scratch an absolute depth estimator using collected monocular images, as well as existing images and absolute depth measurements (e.g., from additional setups, such as LiDAR/stereo sensors). Collected monocular images may be used to create up-to-scale depth maps, and existing images and absolute depth measurements may be used to estimate the scale of a scene from the up-to-scale depth map. Scale transfer may thus be achieved between source images with known ground truth depth information and a new target domain of collected monocular images.

    Region of interest selection for object detection

    公开(公告)号:US11461992B2

    公开(公告)日:2022-10-04

    申请号:US17095883

    申请日:2020-11-12

    Abstract: An object detection system may generate regions of interest (ROIs) from an input image that can be processed by a wide range of object detectors. According to the techniques described herein, an image is processed by a light-weight neural network (e.g., a heatmap network) that outputs object center and object scale heat-maps. The heatmaps are processed to define ROIs that are likely to include objects. Overlapping ROIs are then merged to reduce the aggregate size of the ROIs, and merged ROIs are downscaled to a reduced set of pre-defined resolutions. Fully-convolutional, high-accuracy object detectors may then operate on the downscaled ROIs to output accurate detections at a fraction of the computations by operating on a reduced image. For example, fully-convolutional, high-accuracy object detectors may operate on a subset of the entire image (e.g., cropped images based on ROIs) thus reducing computations otherwise performed over the entire image.

    REGION OF INTEREST SELECTION FOR OBJECT DETECTION

    公开(公告)号:US20220147751A1

    公开(公告)日:2022-05-12

    申请号:US17095883

    申请日:2020-11-12

    Abstract: An object detection system may generate regions of interest (ROIs) from an input image that can be processed by a wide range of object detectors. According to the techniques described herein, an image is processed by a light-weight neural network (e.g., a heatmap network) that outputs object center and object scale heat-maps. The heatmaps are processed to define ROIs that are likely to include objects. Overlapping ROIs are then merged to reduce the aggregate size of the ROIs, and merged ROIs are downscaled to a reduced set of pre-defined resolutions. Fully-convolutional, high-accuracy object detectors may then operate on the downscaled ROIs to output accurate detections at a fraction of the computations by operating on a reduced image. For example, fully-convolutional, high-accuracy object detectors may operate on a subset of the entire image (e.g., cropped images based on ROIs) thus reducing computations otherwise performed over the entire image.

    Noninvasive blood pressure measurement method and device

    公开(公告)号:US11576583B2

    公开(公告)日:2023-02-14

    申请号:US16010289

    申请日:2018-06-15

    Abstract: A method for estimating blood pressure using a blood flow occlusion system applied to an artery includes receiving from a first sensor a sensed signal; processing at a processor the sensed signal to detect beats in a pulsatile signal; determining validity of the detected beats; storing the detected beats and data associated with the detected beats in the sensed signal as the pressure applied to the artery by the blood flow occlusion system deflates towards a level below a nominal level; determining baseline beat characteristics; evaluating the stored beats and associated data to detect change in beat characteristics as compared to the baseline beat characteristics; selecting a beat before the detected change in the beat characteristic as the last beat indicating the onset of the diastolic blood pressure for the artery; determining a value of the applied pressure at the last beat as the diastolic blood pressure for the artery.

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