公开(公告)号：US20240350037A1

公开(公告)日：2024-10-24

申请号：US18436186

申请日：2024-02-08

Applicant: UNIVERSITY OF SOUTH CAROLINA

Inventor： SANJIB SUR ,  AAKRITI ADHIKARI

IPC: A61B5/11 ,  A61B5/00 ,  A61B5/05

CPC classification number: A61B5/1126 ,  A61B5/05 ,  A61B5/1114 ,  A61B5/1116 ,  A61B5/4806 ,  A61B5/7203 ,  A61B5/7267 ,  A61B5/7275

Abstract: Methodology and corresponding apparatus pertain to human sleep posture monitoring, using a wireless signal-based monitoring system leveraging millimeter-wave technology. A software-only human sleep posture monitoring solution based on millimeter-wave (mmWave) wireless-based solutions enables fine-grained posture monitoring under no light without being privacy-invasive. In zero visibility, body joint information and changes can be extracted directly from mmWave imaging using improved capabilities for extracting human sleep posture data from millimeter-wave wireless systems. A single-person sleep posture monitoring system leverages signal processing and deep learning models to enable fine-grained monitoring continuously and non-intrusively with commodity (i.e., generally available) mmWave devices. The system directly predicts joint locations from reflected mmWave signals by learning the hidden association between them from thousands of data samples. Learning is accomplished through a customized Deep Convolutional Neural Network (DCNN), that predicts the 3D locations of several key body joints from the reflected signals captured by multiple mmWave antennas.

公开(公告)号：US20220322965A1

公开(公告)日：2022-10-13

申请号：US17715561

申请日：2022-04-07

Applicant: UNIVERSITY OF SOUTH CAROLINA

Inventor： SANJIB SUR ,  AAKRITI ADHIKARI

IPC: A61B5/087 ,  A61B5/00 ,  G16H50/70

Abstract: Integrated methodologies and associated devices are provided for performing at-home spirometry tests using improved analysis based on deep learning technology. A deep residual decoder network is established for patient lung function monitoring. Curve learning is conducted with such decoder network based on a patient database of lung function measures. An existing spirometry device may be used for outputting at least one key spirometry indicator and obtaining airflow data from a patient with incident airflow of a sample exhalation resulting in at least one key spirometry indicator from the spirometry device for the patient user. The decoder network processes such spirometry indicator for the patient user to produce flow-volume graphing to extend the capability of the spirometry device for finer-grained, long-term lung function monitoring.

公开(公告)号：US20220322964A1

公开(公告)日：2022-10-13

申请号：US17715503

申请日：2022-04-07

Applicant: UNIVERSITY OF SOUTH CAROLINA

Inventor： SANJIB SUR ,  AAKRITI ADHIKARI

IPC: A61B5/087 ,  A61B5/0507 ,  A61B5/091 ,  A61B5/00 ,  G06N3/04

Abstract: An integrated system and associated methodology allow performing at-home spirometry tests using smart devices which leverage the built-in millimeter-wave (mmWave) technology. Implementations leverage deep learning with some embodiments including a combination of mmWave signal processing and CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory Network) architecture. Smartphone devices are transformed into reliable at-home spirometers by having a user hold a device in front of their mouth, inhale their full lung volume, and forcibly exhale until the entire volume is expelled, as in typical spirometry tests. Airflow on the device surface creates tiny vibrations which directly affect the phase of the reflected mmWave signal from nearby objects. Stronger airflow yields larger vibration and higher phase change. The technology analyzes tiny vibrations created by airflow on the device surface and combines wireless signal processing with deep learning. The resulting low-cost, contactless method of lung function monitoring is not affected by noise and motion and provides all key spirometry indicators.