This National Science Foundation (NSF) Engineering (CFDA 47.041) project grant award of $430,826 to the New York Institute of Technology aims to develop a quantitative sensing system to evaluate fine motor skills in autistic children. The project will create a low-cost wearable glove and interactive game to objectively measure hand motions and fine motor abilities like grasping and holding a pencil. This technology will provide an alternative to subjective clinical assessments, enabling more...
The National Science Foundation (NSF) awarded a $319,556 Project Grant under the Computer and Information Science and Engineering (CISE) program to The Research Foundation for the State University of New York (RF-SUNY) at the University at Albany. The project aims to advance computational modeling of autism spectrum disorder (ASD) through multimodal data collection, fusion, and phenotyping. The research team will integrate behavioral data (e.g. eye tracking, audio/video) with neuroimaging data...
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 Project Grant award of $208,888.00 from the National Institute of Mental Health (CFDA 93.242 - Mental Health Research Grants) supports research to develop computer vision techniques for identifying autism spectrum disorder (ASD) motor deficits in infants. The Washington University project aims to leverage deep learning and computational ethology to automatically extract and analyze kinematic data from video assessments of infant behavior, with the goal of creating objective, scalable...
The Hugo W. Moser Research Institute At Kennedy Krieger, Inc. will provide medical and scientific research services under a $440,893 National Science Foundation Project Grant award through the Computer and Information Science and Engineering federal grant program (CFDA 47.070). Specifically, the Institute will conduct collaborative research on multimodal algorithms for motor imitation assessment in children with autism from October 1, 2021 through September 30, 2025. The Computer and Information...
This National Science Foundation (NSF) Division of Information and Intelligent Systems award provides $600,000 over 3 years (8/1/2023 - 7/31/2026) to Michigan State University to develop an assistive technology framework for improving social skills interventions for children with autism and other developmental disabilities in classroom settings. The key products to be developed include a wearable human interaction tracker (WHET) tag that can quantitatively measure and track interactions...
This Project Grant award of $599,960 from the National Science Foundation's Engineering program (CFDA 47.041) will enhance the effectiveness and engagement of home-based hand rehabilitation through artificial intelligence (AI) and motion-sensing technology. The key products and services to be delivered under this 5-year award include: Developing a computer vision-based recovery monitoring system that integrates motion sensing and muscle activity data to model and visualize hand recovery...
This National Science Foundation (NSF) Project Grant, awarded under the Computer and Information Science and Engineering program (CFDA 47.070), provides $816,735 to Northeastern University to research techniques for improving the sample efficiency of reinforcement learning and imitation learning for robotic manipulation tasks. The key goals are to: 1) expand symmetric learning methods to handle imperfect symmetries; 2) explore object-factored symmetric models; 3) explore symmetric learning in...
The University of Louisville received a $249,995 Project Grant from the National Science Foundation to develop an artificial intelligence-enabled software framework for the automatic identification of genetic and neuroimaging markers of Autism Spectrum Disorder. The goal is to reduce the age of diagnosis to approximately six months and provide a detailed profile of where individuals fall on the autism spectrum. The proposed computer-assisted diagnostic system will analyze brain structure and...
This $200,000 Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) aims to develop the mathematical and statistical foundations for a Digital Twin (DT) system to enhance neurophysiological modeling and uncertainty quantification for individuals with Autism Spectrum Disorder (ASD). The key products and services to be delivered include: Computational models based on Conditional Variational Auto-Encoders (CVAE) and longitudinal CVAE to analyze brain...
The National Science Foundation (NSF) awarded a $614,957 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to The Trustees of the University of Pennsylvania (Clinical Practices of the University of Pennsylvania) to design, develop, and test an objective, reproducible, and scalable multimodal system for observing and quantitatively assessing motor imitation performance in children with autism spectrum disorder (ASD). The key products and services to be delivered through this 3-year project include:
Designing motor imitation tasks relevant for ASD assessment.
Developing a scalable system to collect and label multimodal data of children imitating movements.
Creating novel fine-grained representations and computer vision/metric learning algorithms to compare children's movements to target movements.
Generating candidate imitation scores that can serve as quantitative biomarkers for ASD.
This interdisciplinary project aims to address challenges in current imitation assessment methods and leverage advances in computer vision and machine learning to inform ASD diagnosis and behavioral therapies. The research could also have broader applications beyond ASD, such as in video-based rehabilitation, surgical skill assessment, and athletic activities.