Award Abstract # 2014232
SCH:INT: SCH: INT: A gamified mobile system for real-time mental health data modeling and personalized autism care across sociocultural settings

NSF Org: IIS
Div Of Information & Intelligent Systems
Recipient: THE LELAND STANFORD JUNIOR UNIVERSITY
Initial Amendment Date: September 4, 2020
Latest Amendment Date: October 20, 2020
Award Number: 2014232
Award Instrument: Standard Grant
Program Manager: Wendy Nilsen
wnilsen@nsf.gov
 (703)292-2568
IIS
 Div Of Information & Intelligent Systems
CSE
 Direct For Computer & Info Scie & Enginr
Start Date: October 1, 2020
End Date: September 30, 2024 (Estimated)
Total Intended Award Amount: $1,100,099.00
Total Awarded Amount to Date: $1,100,099.00
Funds Obligated to Date: FY 2020 = $1,100,099.00
History of Investigator:
  • Dennis Wall (Principal Investigator)
    dpwall@stanford.edu
Recipient Sponsored Research Office: Stanford University
450 JANE STANFORD WAY
STANFORD
CA  US  94305-2004
(650)723-2300
Sponsor Congressional District: 16
Primary Place of Performance: Stanford University
1265 WELCH RD, SUITE X141
PALO ALTO
CA  US  94305-5102
Primary Place of Performance
Congressional District:
16
Unique Entity Identifier (UEI): HJD6G4D6TJY5
Parent UEI:
NSF Program(s): Smart and Connected Health
Primary Program Source: 01002021DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 8018, 8062
Program Element Code(s): 801800
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Neuropsychiatric disorders are the single greatest cause of disability due to non-communicable disease worldwide, accounting for as much as 19% of the global burden of disease. These disorders include autism spectrum disorder, which is among the fastest growing pediatric concerns today and highly representative of many other neuropsychiatric conditions. The current standards of care for neuropsychiatric disorders are negatively impacted by subjectivity, inconsistent delivery, and limited access as waiting lists grow. New informatics solutions, in particular artificial intelligence (AI), that can port to more ubiquitous mobile health devices and that are not restricted for use in clinical settings, have great potential to complement the current standards of care. With this research project, a novel digital support for autism will be developed. Specifically, working together with families and their affected children ages 2-8, we will establish a gaming system for mobile devices. This system will use augmented reality and AI to adapt to the child?s needs and foster a social exchange between the primary caregiver and the child. This mobile solution will connect families to care, fostering the development of a community of stakeholders, together with AI models capable of personalizing care and tracking progress.

This project will: (1) combine human-computer-interaction with clinically validated treatment approaches through iterative co-design with affected children and their families; (2) identify at least three promising augmented reality game modes that address core autism features including social-reciprocity and emotional understanding; (3) lower barriers to secure and trustworthy data sharing by game players; and (4) construct a series of AI models from game play data that can be used to understand and dynamically react to the treatment effect during game play. Our research project will result in an engaging at-home digital tool accessible to any family with a smartphone (iOS or Android). The project will show how this mobile AI solution can act as a scalable supplement to standard therapy and one that has the potential to reduce the healthcare burden by empowering caregivers. Finally, the research will generate solutions, code, examples, and computer vision libraries that will enable the development of a broader platform for AI in the mobile environment in socioeconomically diverse populations and set the stage for similar solutions for other mental health conditions.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Penev, Yordan and Dunlap, Kaitlyn and Husic, Arman and Hou, Cathy and Washington, Peter and Leblanc, Emilie and Kline, Aaron and Kent, John and Ng-Thow-Hing, Anthony and Liu, Bennett and Harjadi, Christopher and Tsou, Meagan and Desai, Manisha and Wall, D "A Mobile Game Platform for Improving Social Communication in Children with Autism: A Feasibility Study" Applied Clinical Informatics , v.12 , 2021 https://doi.org/10.1055/s-0041-1736626 Citation Details
Kalantarian, Haik and Jedoui, Khaled and Dunlap, Kaitlyn and Schwartz, Jessey and Washington, Peter and Husic, Arman and Tariq, Qandeel and Ning, Michael and Kline, Aaron and Wall, Dennis Paul "The Performance of Emotion Classifiers for Children With Parent-Reported Autism: Quantitative Feasibility Study" JMIR Mental Health , v.7 , 2020 https://doi.org/10.2196/13174 Citation Details
Kalantarian, Haik and Jedoui, Khaled and Washington, Peter and Tariq, Qandeel and Dunlap, Kaiti and Schwartz, Jessey and Wall, Dennis P. "Labeling images with facial emotion and the potential for pediatric healthcare" Artificial Intelligence in Medicine , v.98 , 2019 https://doi.org/10.1016/j.artmed.2019.06.004 Citation Details
Washington, Peter and Kalantarian, Haik and Kent, Jack and Husic, Arman and Kline, Aaron and Leblanc, Emilie and Hou, Cathy and Mutlu, Cezmi and Dunlap, Kaitlyn and Penev, Yordan and Stockham, Nate and Chrisman, Brianna and Paskov, Kelley and Jung, Jae-Yo "Training Affective Computer Vision Models by Crowdsourcing Soft-Target Labels" Cognitive Computation , v.13 , 2021 https://doi.org/10.1007/s12559-021-09936-4 Citation Details
Washington, Peter and Kline, Aaron and Mutlu, Onur Cezmi and Leblanc, Emilie and Hou, Cathy and Stockham, Nate and Paskov, Kelley and Chrisman, Brianna and Wall, Dennis "Activity Recognition with Moving Cameras and Few Training Examples: Applications for Detection of Autism-Related Headbanging" , 2021 https://doi.org/10.1145/3411763.3451701 Citation Details

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