Project Grant 2513070
- Lifespan Digital Health LLC was awarded a $255,409 Project Grant from the National Science Foundation under the NSF Technology, Innovation, and Partnerships program (CFDA 47.084) to develop bio-behavioral technology and provide mental health services. Specifically, the company will use predictive algorithms, wearable technology, and peripheral autonomic biofeedback to provide timely data and enable mental health professionals to treat students. This innovative approach aims to address the...
- This $1,183,690 Project Grant awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program funds research to develop personalized artificial intelligence (AI) models that can predict repeat adverse health events like substance use and stress-related blood pressure spikes using data from wearable devices like Fitbit and Apple Watch. The key innovation is training these models to learn from each individual's unique biosignal data patterns,...
- This $412,423 project grant from the National Science Foundation's Computer and Information Science and Engineering program will fund the development of novel closed-loop behavioral health interventions at the University of Chicago from September 2022 through July 2026. The university researchers will create three types of wearable and Internet of Things systems delivering sensory interventions for mental health issues like substance cravings, workplace stress, and social stress. The...
- This four-year $686,879 project grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) will support the development of novel closed-loop behavioral health interventions delivered through wearable and Internet of Things devices. Specifically, the researchers at Cornell University will create three types of wearable and IoT systems using different sensory modalities like vibration, airflow, and touch to provide just-in-time interventions...
- The National Science Foundation awarded a $256,000 Small Business Innovation Research Phase I Project Grant to Futuresthrive LTD under the Engineering federal grant program (CFDA 47.041). The grant will support development of a first-of-its-kind mental health screening tool for youth ages 4-18. Using artificial intelligence, voice and facial biometrics, sentiment analysis and machine learning, the gamified online platform aims to identify risk factors and provide a baseline for understanding a...
- This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) provides $260,000 to The University of Texas Rio Grande Valley (UTRGV) to develop robust deep learning techniques for medical sensor time series data analysis. The key objectives are to: 1) identify input confounders that lead to spurious correlations in time series data, 2) design mitigation strategies to correct these spurious correlations, and 3)...
- The National Science Foundation (NSF) awarded a $650,000 Project Grant under the Engineering program (CFDA 47.041) to The Pennsylvania State University, doing business as Penn State, to develop an interactive artificial intelligence (AI) system called the Trustworthy, Explainable, and Adaptive Monitoring Machine for AI Teams (TEAMMAIT). This system aims to help address the national shortage of mental health workers skilled in research-supported treatment protocols. The project will investigate...
- This National Science Foundation (NSF) Engineering Research Initiation (ERI) project grant, under CFDA 47.041, is awarded for $200,000 to the University of the Pacific to develop a novel wearable sensing and analysis system for early detection of autism spectrum disorder (ASD) in young children and toddlers. The research aims to leverage non-invasive wearable devices to collect physiological and environmental data, and then use machine learning to identify physiological biomarkers and early...
- This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Award, valued at $503,930, supports the development of a distributed wearable computing system for advanced health monitoring. The research aims to address key challenges in wearable technologies, such as limited computational power, battery capacity, data privacy, and user interface design. The funded project seeks to create a novel distributed machine learning architecture deployed on...
- This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Award, with CFDA number 47.070, provides $692,847 to the University of Texas at Austin and the University of Texas at San Antonio to develop integrated systems for health and chemical sensing. The project focuses on integrating energy-efficient "weightless neural networks" with cardiac and chemical sensors to create intelligent, wearable health monitoring systems. Key objectives...
This National Science Foundation (NSF) Project Grant award, titled "PSYCHSENS: A Framework for Automated and Pervasive Psychiatric Comorbidity Screening Using Multimodal Wearable Sensor Data", aims to develop advanced machine learning models to detect comorbid mental health conditions using wearable sensor data. The $499,573 grant, awarded under the NSF's Engineering program (CFDA 47.041), will be executed by the University of Texas at Dallas (UTD) from October 2025 through September 2028. The project seeks to create a framework for automated, pervasive screening of psychiatric comorbidities, leveraging hierarchical state-space models to efficiently process sensor data. It also aims to enable personalized, privacy-aware risk stratification through novel sociodemographic encoding and differential private model training, as well as automated biomarker discovery via sequential influence functions. The project goals include reducing mental health burdens, particularly among college students, through early warning systems and targeted interventions. The award also includes an educational plan to enhance undergraduate, graduate, and K-12 curricula with hands-on machine learning projects focused on sensor-based mental health analysis.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $499.6k | 7/24/25 |