公开(公告)号：US20250022373A1

公开(公告)日：2025-01-16

申请号：US18635295

申请日：2024-04-15

Applicant: UNIVERSITY OF SOUTH CAROLINA

Inventor： SANJIB SUR ,  HEM K. REGMI

IPC: G08G1/16 ,  G01S13/931 ,  G06N3/0475

Abstract: The disclosed system and methodology enable coexistence of networking and sensing on next-generation millimeter-wave (mmWave) picocells for traffic monitoring and pedestrian safety at intersections in all weather conditions. Existing wireless signal-based object detection systems suffer from limited resolution, and their outputs may not provide sufficient discriminatory information in complex scenes, such as traffic intersections. The disclosed system uses 5G picocells, which operate at mmWave frequency bands and provide higher data rates and higher sensing resolution than traditional wireless technology. It is difficult to run sensing applications and data transfer simultaneously on mmWave devices due to potential interference. MmWave devices are vulnerable to weak reflectivity and specularity challenges which may result in loss of information about objects and pedestrians. To address such challenges, the disclosed design uses customized deep learning models that not only can recover missing information about the target scene but also enable coexistence of networking and sensing.

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：US20240324949A1

公开(公告)日：2024-10-03

申请号：US18429626

申请日：2024-02-01

Applicant: UNIVERSITY OF SOUTH CAROLINA

Inventor： SANJIB SUR

IPC: A61B5/00 ,  A61B5/11 ,  A61B90/00 ,  G16H50/70

CPC classification number: A61B5/4815 ,  A61B5/002 ,  A61B5/1116 ,  A61B5/1126 ,  A61B5/4809 ,  A61B5/4812 ,  A61B5/7225 ,  A61B5/7239 ,  A61B5/7246 ,  A61B5/7257 ,  A61B5/7267 ,  A61B90/06 ,  G16H50/70

Abstract: Methodology and corresponding apparatus pertains to sleep disruption monitoring, including use of a wireless signal-based monitoring system leveraging millimeter-wave technology. A software-only sleep disruption monitoring solution can be based on millimeter-wave (mmWave) wireless-based solutions which leverage cross-correlation between successive mmWave reflected signals and a Hidden Markov Model (HMM) to identify respective sleep (rest) and disruptions (toss-turn) periods. A toss-turn detector module can identify sudden movements during sleep from mmWave wireless signals and classify the sleeping period into the two states: Rest or toss-turn. Whenever mmWave transceivers (such as included in 5G-and-beyond devices) are implemented as access points, in mass privacy non-invasive sleep disruption monitoring can be provided for consumers at home.

公开(公告)号：US20220373673A1

公开(公告)日：2022-11-24

申请号：US17726279

申请日：2022-04-21

Applicant: UNIVERSITY OF SOUTH CAROLINA

Inventor： SANJIB SUR ,  HEM K. REGMI

IPC: G01S13/88 ,  G01S13/90 ,  G06V10/774 ,  G06V10/764 ,  G06N3/04 ,  G06N3/08

Abstract: System and methodology are disclosed for approximating traditional SAR imaging on mobile mmWave devices. The presently disclosed technology enables human-perceptible and machine-readable shape generation and classification of hidden objects on mobile mmWave devices. The resulting system and corresponding methodology are capable of imaging through obstructions, like clothing, and under low visibility conditions. To this end, the presently disclosed technology incorporates a machine-learning model to recover the high-spatial frequencies in the object to reconstruct an accurate 2D shape and predict its 3D features and category. The technology is disclosed in particular for security applications, but the broader model disclosed is adaptable to different applications, even with limited training samples.

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.