Project Grant R01CA309524
- This is a Project Grant awarded by the National Science Foundation (NSF) Division of Information and Intelligent Systems under the Computer and Information Science and Engineering program (CFDA 47.070). The $299,708 grant, with a period of performance from August 15, 2023 to July 31, 2027, supports collaborative research to develop AI-driven radio frequency identification (RFID) sensing techniques for smart health monitoring applications. The research aims to create more affordable, comfortable,...
- This Project Grant award for $299,997.00 from the National Science Foundation's Computer and Information Science and Engineering Program (CFDA 47.070) supports a collaborative research effort led by Auburn University. The project aims to develop AI-driven radio frequency identification (RFID) sensing techniques to enable more affordable, comfortable, and accessible smart health monitoring systems. The key products and services to be delivered include: Investigating challenges and performance...
- This federal Project Grant award, valued at $678,710 and awarded on June 15, 2025, was provided by the National Heart, Lung, and Blood Institute under the Cardiovascular Diseases Research program (CFDA 93.837). The award supports the development and validation of a novel multi-sensor machine learning approach to precision sleep tracking for nightshift workers. The project aims to leverage wearable devices, environmental sensors, and machine learning algorithms to accurately detect sleep patterns...
- The National Institute of Biomedical Imaging and Bioengineering (NIBIB) awarded a $130,600 Project Grant under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286) to Marian University Inc. The project, titled "Advancing Wakefulness Screening of Sleep Apnea Disorder Through Breathing Sound Analysis and Hardware Optimization," aims to enhance a wakefulness-based sleep apnea screening tool that utilizes breathing sound analysis to...
- This two-year, $200,000 project grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) will support the development of interpretable deep learning methods and software for automated sleep phenotyping using multimodal health sensing data. Beth Israel Deaconess Medical Center will leverage polyssomnography data and novel radio frequency sensing techniques to build accurate deep learning models for classifying sleep stages and apnea...
- This Project Grant award of $300,000.00 from the National Science Foundation's Division of Information and Intelligent Systems will fund a collaborative research effort led by Florida International University (FIU) to develop AI-driven RFID sensing techniques for smart health applications. The project aims to create more affordable, comfortable, and accessible health monitoring systems by leveraging advances in the Internet of Things and machine learning/AI. The research will focus on addressing...
- This Project Grant award, valued at $503,930.00 and awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070), supports the development of a wearable sensor network with distributed computing capabilities for advanced health monitoring and biomechanics analysis. The research aims to address key challenges associated with wearable technologies, such as limited computational power, battery capacity, data...
- This National Science Foundation award of $247,721 provides funding under the Computer and Information Science and Engineering program (CFDA 47.070) to Arizona State University for a project titled "CAREER: Autonomous Wearable Computing for Personalized Healthcare." The project aims to develop foundations for computational autonomy in wearable-based health monitoring and interventions. Specifically, the university will investigate methods for automatically and autonomously labeling...
- The Project Grant award of $164,110 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program supports a collaborative research initiative to develop AI-driven radio frequency sensing techniques for smart health monitoring applications. The project aims to create more affordable, comfortable, and accessible health monitoring systems by leveraging advancements in the Internet of Things (IoT) and machine learning/artificial intelligence (ML/AI)....
- This $227,886 federal Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) aims to develop a novel deep sensor array decoding system for high-fidelity remote health monitoring. The project will leverage data-driven deep learning algorithms to decode weak and noisy signals captured remotely from the human body, without requiring a reference signal. By leveraging multi-sensor spatial information, the system will recover the signal of interest from...
This federal Project Grant award from the Department of Health and Human Services' National Institutes of Health (CFDA 93.310 - Trans-NIH Research Support program) provides $974,377 to Arizona State University to develop a smart wireless environment using multi-band reconfigurable intelligent surfaces (RIS) for remote sensing of biomarkers like heart and respiratory rates. The objective is to enable the retrieval of these biomarkers without disrupting a user's typical sleep environment, which can revolutionize sleep medicine, smart healthcare, independent living, and sleep health monitoring of cancer patients. The project aims to create a comprehensive physics-based model for RIS-based computational imaging and a novel multi-modal learning algorithm to detect and track regions of interest, such as the user's torso, using high-resolution wideband millimeter RIS. This technology has the potential to significantly improve remote monitoring of sleep-related health indicators.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $974.4k | 9/3/25 |