公开(公告)号：US11690563B2

公开(公告)日：2023-07-04

申请号：US17772844

申请日：2020-10-27

Applicant: ARIZONA BOARD OF REGENTS ON BEHALF OF ARIZONA STATE UNIVERSITY

Inventor： Yu Rong ,  Alex Chiriyath ,  Arindam Dutta ,  Daniel W. Bliss

IPC: A61B5/00 ,  A61B5/0205 ,  A61B5/05

CPC classification number: A61B5/4812 ,  A61B5/0205 ,  A61B5/05 ,  A61B5/4818

Abstract: Methods and systems for remote sleep monitoring are provided. Such methods and systems provide non-contact sleep monitoring via remote sensing or radar sensors. In this regard, when processing backscattered radar signals from a sleeping subject on a normal mattress, a breathing motion magnification effect is observed from mattress surface displacement due to human respiratory activity. This undesirable motion artifact causes existing approaches for accurate heart-rate estimation to fail. Embodiments of the present disclosure use a novel active motion suppression technique to deal with this problem by intelligently selecting a slow-time series from multiple ranges and examining a corresponding phase difference. This approach facilitates improved sleep monitoring, where one or more subjects can be remotely monitored during an evaluation period (which corresponds to an expected sleep cycle).

公开(公告)号：US20230139637A1

公开(公告)日：2023-05-04

申请号：US17801543

申请日：2021-03-31

Applicant: Arizona Board of Regents on behalf of Arizona State University

Inventor： Daniel W. Bliss ,  Yu Rong ,  Arindam Dutta ,  Alex Chiriyath

IPC: A61B5/05 ,  A61B5/11 ,  A61B5/00

Abstract: Vital sign monitoring via remote sensing on stationary exercise equipment is provided. A new non-contact approach described herein uses radio frequency (RF) radar (e.g., ultra-wide band (UWB) radar) to remotely monitor vital sign information (such as heartbeat and breathing) and human activity information of subjects using stationary exercise equipment. In some embodiments, a radar sensor captures micro-scale chest motions (corresponding to the vital sign information) as well as macro-scale body motions (corresponding to movements from exercise). A signal processor receives radar signals from the radar sensor and processes the radar signals to reconstruct vital sign information from the micro-scale chest motions and/or human activity information from the macro-scale body motions using a joint vital sign-motion model, which can be trained using machine learning and other approaches.